diff --git a/app/components/OgLines.vue b/app/components/OgLines.vue
index 07ba4db8..405a464d 100644
--- a/app/components/OgLines.vue
+++ b/app/components/OgLines.vue
@@ -1,5 +1,6 @@
 <script setup lang="ts">
 import type { OgFace } from '../utils/og-style'
+import { computed } from 'vue'
 import { OG_MUTED, ogEvenLines, ogLineStyle, ogTitleStyle } from '../utils/og-style'
 
 // Centred OG text of up to `lines` lines, broken evenly when it needs two.
diff --git a/docs/editorial/skill-md-size-study-2026-10-09.md b/docs/editorial/skill-md-size-study-2026-10-09.md
new file mode 100644
index 00000000..37e88159
--- /dev/null
+++ b/docs/editorial/skill-md-size-study-2026-10-09.md
@@ -0,0 +1,58 @@
+# Skill size study evidence
+
+The study answers how Skill descriptions, bodies, and reference inventories differ across a frozen registry sample.
+Harlan requested original research using the Unhead research articles as the editorial reference.
+
+## Reproduction
+
+The source measurements are committed under `public/research/skill-md-size-2026-10-09/`.
+Run `pnpm exec tsx scripts/research/build-size-study.ts` to rebuild `summary.json`.
+The provenance manifest records the original export hash, measurement hash, tokenizer, and rubric version.
+
+Migration 0151 stores the same summary and provenance in D1.
+The existing source-version description and body observations remain in `skill_generated`.
+The research snapshot has no foreign key to live Skills. Source deletion must not erase published evidence.
+Publish corrections with an explicit snapshot revision. Do not silently replace historical measurements.
+
+## Claim checks
+
+| Claim | Evidence |
+| --- | --- |
+| 12,141 filtered Skills, 2,297 repositories | Included rows and distinct repository identities |
+| 12,138 matched bodies, three missing | Nonnull body token measurements |
+| 8.9% reach 5,000 tokens | 1,078 / 12,138 |
+| Repository weighting raises that to 10.8% | Mean of per-repository proportions, 2,296 measured repositories |
+| Mean description rises from 345 to 383 characters | Skill mean versus mean of repository means |
+| 11,760 normalised descriptions | Trim and collapse whitespace before SHA-256 |
+| 11,764 exact descriptions | Hash exact description text before normalisation |
+| File duplicates leave the tail near 8.9% | 1,061 / 11,983 measured unique raw files |
+| Reference mean is 2.27, median zero | 12,091 complete inventories, nulls excluded |
+
+Exact text uniqueness and whitespace-normalised uniqueness use different definitions.
+Neither means the source bodies are distinct.
+Body counts exclude frontmatter. Whole-file kB includes frontmatter.
+All percentiles use nearest rank.
+
+## Interpretation limits
+
+The corpus is neither a random sample nor a top-installed leaderboard sample.
+Existing aggregator flags predate the study. Their exclusion does not establish independent mirror detection.
+Models see different tokens. o200k_base estimates cannot reproduce every Agent's catalogue truncation.
+Jev scores have no independent human calibration. The article labels the semantic section exploratory.
+No actual Agent selection, task completion, or corpus-wide skills-ref validation claim appears.
+The public row dataset omits source instructions and descriptions. Pinned commits and paths identify those sources.
+Word frequencies require the source descriptions, the published stop list, and the stated token rule to reproduce.
+
+## Search and admission
+
+NuxtSEO Pro queries on 9 October 2026 measured the US `skill md` seed at 720 searches/month, KD 28.
+Specific size and description-length seeds returned no provider data. This is not measured zero demand.
+The owner explicitly approved original research as an authority surface, independent of keyword volume.
+The admission record names that purpose and the 11 November gate in `page-admissions.ts`.
+Review relevant impressions, citations, dataset use, and authoring-guide clicks separately.
+
+## Primary guidance
+
+- [Agent Skills specification](https://agentskills.io/specification): description cap, progressive disclosure, body guidance, and format validation.
+- [Claude authoring guidance](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices): concise instructions and real-usage tests.
+- `scripts/lib/description-review.ts`: exact Jev questions, model version, and thresholds.
diff --git a/eslint.config.mjs b/eslint.config.mjs
index ad8d2eef..9ac60888 100644
--- a/eslint.config.mjs
+++ b/eslint.config.mjs
@@ -3,7 +3,8 @@ import harlanzw from 'eslint-plugin-harlanzw'
 
 export default antfu({
   // skilld sync writes .skills from skilld-dev/skilld. CI checks it with check:skills.
-  ignores: ['.skills/**'],
+  // Immutable research exports retain their recorded byte hashes.
+  ignores: ['.skills/**', 'public/research/**/measurements.json'],
   rules: {
     'vue/no-useless-v-bind': 'off',
     'vue/attribute-hyphenation': 'off',
diff --git a/layers/marketing/app/components/content/SkillSizeStudy.vue b/layers/marketing/app/components/content/SkillSizeStudy.vue
new file mode 100644
index 00000000..aa8c5bb0
--- /dev/null
+++ b/layers/marketing/app/components/content/SkillSizeStudy.vue
@@ -0,0 +1,201 @@
+<script setup lang="ts">
+import summary from '../../../../../public/research/skill-md-size-2026-10-09/summary.json'
+import vocabulary from '../../../../../public/research/skill-md-size-2026-10-09/words.json'
+
+const { chart } = defineProps<{ chart: 'body' | 'topics' | 'descriptions' | 'words' }>()
+const number = (value: number) => value.toLocaleString('en-US', { maximumFractionDigits: 0 })
+const decimal = (value: number | null, digits = 1) => value === null ? 'Unavailable' : value.toFixed(digits)
+const topicLabels: Record<string, string> = {
+  coding: 'Coding',
+  writing: 'Writing',
+  design: 'Design',
+  operations: 'Operations',
+  data: 'Data',
+  planning: 'Planning',
+  security: 'Security',
+  research: 'Research',
+  other: 'Other tasks',
+  unclear: 'Task category uncertain',
+}
+const totals = summary.topics.reduce((total, topic) => ({
+  green: total.green + topic.bands.green,
+  amber: total.amber + topic.bands.amber,
+  red: total.red + topic.bands.red,
+}), { green: 0, amber: 0, red: 0 })
+const bodyBands = [
+  { label: 'At most 2,500 tokens', count: totals.green, class: 'study-green' },
+  { label: '2,501 to 4,999 tokens', count: totals.amber, class: 'study-amber' },
+  { label: '5,000 tokens or more', count: totals.red, class: 'study-red' },
+]
+const bandLabel = (min: number, max: number | null) => max === null ? `${number(min)}+` : `${number(min)} to ${number(max)}`
+</script>
+
+<template>
+  <figure class="study-chart not-prose">
+    <template v-if="chart === 'body'">
+      <h3>Most Skill bodies fit below 5,000 tokens</h3>
+      <p class="study-note">
+        12,138 measured bodies. Three source versions unavailable.
+      </p>
+      <div v-for="band in bodyBands" :key="band.label" class="study-band">
+        <div class="study-row-label">
+          <span>{{ band.label }}</span>
+          <span class="study-number">{{ number(band.count) }} · {{ decimal(band.count / summary.included.bodyMeasured * 100) }}%</span>
+        </div>
+        <div class="study-track" aria-hidden="true">
+          <span :class="band.class" :style="{ width: `${band.count / summary.included.bodyMeasured * 100}%` }" />
+        </div>
+      </div>
+      <div class="study-axis">
+        <span>0%</span><span>100% of measured bodies</span>
+      </div>
+      <dl class="study-stats">
+        <div><dt>Median body</dt><dd>{{ number(summary.included.bodyMedian!) }} tokens</dd></div>
+        <div><dt>Mean body</dt><dd>{{ number(summary.included.bodyMean!) }} tokens</dd></div>
+        <div><dt>90th percentile</dt><dd>{{ number(summary.included.bodyP90!) }} tokens</dd></div>
+      </dl>
+      <figcaption>Estimated with o200k_base. Colours show context use, not quality. Green marks half the recommended budget.</figcaption>
+    </template>
+
+    <template v-else-if="chart === 'topics'">
+      <h3>Avg Skill.md Size</h3>
+      <p class="study-note">
+        Whole-file size, including frontmatter. Task categories come from description assessment.
+      </p>
+      <div class="study-table" role="region" aria-label="Skill size by task category" tabindex="0">
+        <table>
+          <caption class="sr-only">
+            Means by task category, with measured Skill counts and reference coverage
+          </caption>
+          <thead>
+            <tr>
+              <th scope="col">
+                Task category
+              </th><th scope="col">
+                Skills
+              </th><th scope="col">
+                Description chars
+              </th><th scope="col">
+                SKILL.md kB
+              </th><th scope="col">
+                Reference files
+              </th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr v-for="topic in summary.topics" :key="topic.topic">
+              <th scope="row">
+                {{ topicLabels[topic.topic] }}
+              </th>
+              <td>{{ number(topic.skills) }}</td>
+              <td>{{ number(topic.descriptionMean!) }}</td>
+              <td>
+                <span>{{ decimal(topic.fileMeanKB) }}</span>
+                <span class="study-mini-track" aria-hidden="true"><span :style="{ width: `${topic.fileMeanKB! / 16 * 100}%` }" /></span>
+              </td>
+              <td>{{ decimal(topic.referenceMean, 2) }}<small>{{ number(topic.referenceMeasured) }} inventories</small></td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+      <figcaption>1 kB = 1,000 UTF-8 bytes. Bars start at zero, scale ends at 16 kB. Missing reference inventories stay missing. Zero counts remain in the mean.</figcaption>
+    </template>
+
+    <template v-else-if="chart === 'descriptions'">
+      <h3>Shorter descriptions do not always give clearer selection guidance</h3>
+      <p class="study-note">
+        Jev 1.13 assessment of task, activation, scope, and filler. Observed association, without an Agent selection test.
+      </p>
+      <div class="study-legend">
+        <span><i class="study-clear" />Clear guidance</span><span><i class="study-uncertain" />Uncertain</span><span><i class="study-red" />Needs work</span>
+      </div>
+      <div v-for="band in summary.descriptionBands" :key="band.min" class="study-band">
+        <div class="study-row-label">
+          <span>{{ bandLabel(band.min, band.max) }} characters</span><span class="study-number">{{ number(band.total) }} Skills</span>
+        </div>
+        <div class="study-track study-stack" aria-hidden="true">
+          <span class="study-clear" :style="{ width: `${band.clear / band.total * 100}%` }" />
+          <span class="study-uncertain" :style="{ width: `${band.uncertain / band.total * 100}%` }" />
+          <span class="study-red" :style="{ width: `${band.needsWork / band.total * 100}%` }" />
+        </div>
+        <p class="study-note">
+          {{ decimal(band.clear / band.total * 100) }}% clear · {{ number(band.uncertain) }} uncertain · {{ number(band.needsWork) }} need work
+        </p>
+      </div>
+      <figcaption>12,141 descriptions. Labels describe model judgements under a published rubric, not proven performance.</figcaption>
+    </template>
+
+    <template v-else>
+      <h3>The words Skill authors use</h3>
+      <div class="study-cloud" aria-hidden="true">
+        <span v-for="word in vocabulary.words.slice(0, 32)" :key="word.text" :style="{ fontSize: `${0.8 + word.count / vocabulary.words[0]!.count * 2.1}rem` }">{{ word.text }}</span>
+      </div>
+      <details>
+        <summary>Word frequencies and counting method</summary>
+        <p class="study-note">
+          {{ vocabulary.method }} Font size is a visual aid. Counts below give the precise frequency.
+        </p>
+        <div class="study-table">
+          <table>
+            <caption class="sr-only">
+              Top 32 words counted once per description
+            </caption><thead>
+              <tr>
+                <th scope="col">
+                  Word
+                </th><th scope="col">
+                  Descriptions containing it
+                </th>
+              </tr>
+            </thead><tbody>
+              <tr v-for="word in vocabulary.words.slice(0, 32)" :key="word.text">
+                <th scope="row">
+                  {{ word.text }}
+                </th><td>{{ number(word.count) }}</td>
+              </tr>
+            </tbody>
+          </table>
+        </div>
+      </details>
+      <figcaption>English alphanumeric words, counted once per description. Common words removed. This view does not represent every language.</figcaption>
+    </template>
+  </figure>
+</template>
+
+<style scoped>
+.study-chart { margin-block: 2.5rem; padding: clamp(1rem, 3vw, 2rem); border: 1px solid var(--ui-border); border-radius: var(--ui-radius); background: var(--ui-bg); color: var(--ui-text); }
+.study-chart h3 { margin: 0; font-size: 1.35rem; font-weight: 600; line-height: 1.4; letter-spacing: -.03em; }
+.study-note, .study-chart figcaption { margin-top: .75rem; color: var(--ui-text-muted); font-size: .875rem; line-height: 1.6; }
+.study-band { margin-top: 1.5rem; }
+.study-row-label { display: flex; flex-wrap: wrap; justify-content: space-between; gap: .5rem; font-size: .9rem; }
+.study-number, .study-axis, .study-stats dd { font-family: var(--font-mono); font-variant-numeric: tabular-nums; }
+.study-track { display: flex; height: 1.15rem; margin-top: .65rem; background: var(--ui-bg-accented); }
+.study-track > span { display: block; height: 100%; }
+.study-green { background: #15803d; }
+.study-amber { background: #b45309; }
+.study-red { background: #be123c; }
+.study-clear { background: #57534e; }
+.study-uncertain { background: #a8a29e; }
+.study-axis { display: flex; justify-content: space-between; gap: 1rem; margin-top: .8rem; font-size: .7rem; color: var(--ui-text-muted); }
+.study-stats { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 1rem; margin-top: 2rem; }
+.study-stats dt { color: var(--ui-text-muted); font-size: .8rem; }
+.study-stats dd { margin-top: .4rem; font-size: 1rem; }
+.study-table { overflow-x: auto; margin-top: 1.5rem; }
+.study-table:focus-visible, summary:focus-visible { outline: 2px solid var(--ui-primary); outline-offset: 3px; }
+table { width: 100%; border-collapse: collapse; text-align: left; font-size: .8rem; }
+th, td { padding: .9rem .7rem; border-bottom: 1px solid var(--ui-border); vertical-align: top; }
+th { font-weight: 500; }
+thead th { font-family: var(--font-mono); color: var(--ui-text-muted); font-size: .7rem; }
+td { font-family: var(--font-mono); font-variant-numeric: tabular-nums; }
+td small { display: block; margin-top: .25rem; color: var(--ui-text-muted); font-size: .65rem; white-space: nowrap; }
+.study-mini-track { display: block; height: .3rem; width: 5rem; margin-top: .4rem; background: var(--ui-bg-accented); }
+.study-mini-track > span { display: block; height: 100%; background: var(--ui-text-muted); }
+.study-legend { display: flex; flex-wrap: wrap; gap: 1rem; margin-top: 1.25rem; font-size: .8rem; }
+.study-legend span { display: flex; align-items: center; gap: .4rem; }
+.study-legend i { display: inline-block; width: .65rem; height: .65rem; }
+.study-cloud { display: flex; flex-wrap: wrap; align-items: center; justify-content: center; gap: .35rem 1rem; padding-block: 2rem; line-height: 1.25; }
+.study-cloud span { font-weight: 500; }
+summary { min-height: 44px; padding-block: .7rem; cursor: pointer; font-size: .9rem; }
+@media (max-width: 480px) { .study-stats { grid-template-columns: 1fr; gap: .75rem; } .study-stats div { display: flex; justify-content: space-between; gap: 1rem; } .study-stats dd { margin: 0; } }
+@media (min-width: 1024px) { .study-chart { width: calc(100% + 12rem); max-width: none; margin-left: -6rem; } }
+</style>
diff --git a/layers/marketing/app/pages/learn/[...slug].vue b/layers/marketing/app/pages/learn/[...slug].vue
index e5f09a9c..59e5980a 100644
--- a/layers/marketing/app/pages/learn/[...slug].vue
+++ b/layers/marketing/app/pages/learn/[...slug].vue
@@ -36,6 +36,37 @@ useSeoMeta({
 useHead({
   link: [{ rel: 'canonical', href: canonicalUrl }],
 })
+
+if (slug.value === 'research/skill-md-size-study') {
+  defineOgImage('Page.takumi', {
+    title: data.value.title,
+    description: '12,141 Skills · 2,297 repositories · Original skilld research',
+  }, { alt: data.value.title })
+
+  useSchemaOrg([
+    defineArticle({
+      headline: data.value.title,
+      description: data.value.description,
+      datePublished: data.value.publishedAt,
+      dateModified: data.value.updatedAt,
+      author: { '@type': 'Organization', 'name': 'skilld', 'url': siteOrigin },
+    }),
+    {
+      '@type': 'Dataset',
+      '@id': `${canonicalUrl.value}#dataset`,
+      'name': 'Skill size and description measurements, 9 October 2026',
+      'description': 'Frozen registry measurements with aggregator exclusions, source hashes, description assessment, and weighting comparisons.',
+      'url': canonicalUrl.value,
+      'version': '2026-10-09-v1',
+      'creator': { '@type': 'Organization', 'name': 'skilld', 'url': siteOrigin },
+      'distribution': ['csv', 'json'].map(format => ({
+        '@type': 'DataDownload',
+        'encodingFormat': format === 'csv' ? 'text/csv' : 'application/json',
+        'contentUrl': `${siteOrigin}/research/skill-md-size-2026-10-09/measurements.${format}`,
+      })),
+    },
+  ])
+}
 </script>
 
 <template>
diff --git a/layers/marketing/app/utils/page-admissions.ts b/layers/marketing/app/utils/page-admissions.ts
index cbc1dce3..e1249bf8 100644
--- a/layers/marketing/app/utils/page-admissions.ts
+++ b/layers/marketing/app/utils/page-admissions.ts
@@ -27,6 +27,7 @@ export interface PageAdmission {
 
 /** Pages added on or after 2026-09-04 that the audit covers. */
 export const FREEZE_AUDIT_PATHS: readonly string[] = [
+  '/learn/research/skill-md-size-study',
   '/agents',
   '/agents/claude-code',
   '/agents/codex',
@@ -59,6 +60,10 @@ export const FREEZE_AUDIT_PATHS: readonly string[] = [
 ]
 
 export const PAGE_ADMISSIONS: Readonly<Record<string, PageAdmission>> = {
+  '/learn/research/skill-md-size-study': {
+    targetQuery: 'SKILL.md size and description length',
+    admissionBar: 'Owner-approved original research, 2026-10-09. One frozen registry study with source hashes, downloadable measurements, methods, weighting comparisons, and an authoring-guide action. Specific size queries have no DataForSEO estimate; the broader skill md seed has 720 US searches a month, KD 28. Research authority and citations are explicit goals, independent of search volume. Review indexing, relevant impressions, citations, and authoring-guide clicks at the 2026-11-11 gate. Retain useful cited research; consolidate into the authoring guide if it has no distinct use.',
+  },
   '/skills/demos': {
     targetQuery: 'claude skills examples',
     admissionBar: '720 searches a month, KD 32. NuxtSEO research keywords, 2026-10-06. Holds only demos a human approved by merging, and renders noindex below six. Covers each demo page, `/skills/demos/<owner>/<repo>/<name>`, which targets "<skill> skill example" (owner-approved 2026-10-07; volume not measured). Review at the 2026-11-11 gate.',
diff --git a/layers/marketing/content.config.ts b/layers/marketing/content.config.ts
index a9d67794..c83545d7 100644
--- a/layers/marketing/content.config.ts
+++ b/layers/marketing/content.config.ts
@@ -38,7 +38,7 @@ export default defineContentConfig({
       type: 'page',
       source: {
         cwd: marketingContentRoot,
-        include: 'learn/*.md',
+        include: 'learn/**/*.md',
         prefix: '/learn',
       },
       schema: articleSchema,
diff --git a/layers/marketing/content/learn/create-agent-skills.md b/layers/marketing/content/learn/create-agent-skills.md
index b3e61cac..9f31714e 100644
--- a/layers/marketing/content/learn/create-agent-skills.md
+++ b/layers/marketing/content/learn/create-agent-skills.md
@@ -9,6 +9,8 @@ updatedAt: 2026-10-05
 command: npx skilld run skilld-dev/skilld/generate-project-skill
 ---
 
+Our [study of 12,141 Skills](/learn/research/skill-md-size-study) measures descriptions, body tokens, and reference files separately.
+
 Write one `SKILL.md` with the procedure you want an Agent to follow.
 Keep its instructions independent of Agent-specific features.
 Then check discovery and behavior in each Agent you support.
diff --git a/layers/marketing/content/learn/index.md b/layers/marketing/content/learn/index.md
index bb85541f..4b82a98e 100644
--- a/layers/marketing/content/learn/index.md
+++ b/layers/marketing/content/learn/index.md
@@ -4,6 +4,8 @@ description: Practical guides for finding, authoring, and shipping agent skills.
 ---
 
 ::card-grid
+- [SKILL.md size and descriptions: 12,141 Skills measured](/learn/research/skill-md-size-study)
+  Original research into context use, descriptions, and references across 2,297 repositories.
 - [Create Skills for Claude Code, Codex and Gemini CLI](/learn/create-agent-skills)
   Write one `SKILL.md`, check discovery paths, and test the same task across Agents.
 - [Browse skills](/skills)
diff --git a/layers/marketing/content/learn/research/skill-md-size-study.md b/layers/marketing/content/learn/research/skill-md-size-study.md
new file mode 100644
index 00000000..80d67b3d
--- /dev/null
+++ b/layers/marketing/content/learn/research/skill-md-size-study.md
@@ -0,0 +1,157 @@
+---
+title: 'SKILL.md size and descriptions: 12,141 Skills measured'
+description: 'We measured 12,141 Skills across 2,297 repositories. Compare description lengths, body tokens, and reference files, with source data and methods.'
+label: 'Original research · 9 October 2026'
+author: skilld
+publishedAt: '2026-10-09'
+updatedAt: '2026-10-09'
+---
+
+**8.9% of measured Skill bodies reach or exceed 5,000 estimated tokens.** Give each repository equal weight, and that figure becomes **10.8%**.
+
+We measured **12,141 Skills across 2,297 repositories** in a frozen skilld registry snapshot. The median description contains **288 characters**. The median whole `SKILL.md` file is **7.3 kB**, while its instruction body contains **1,600 estimated tokens**.
+
+Descriptions help an Agent select a Skill. The body supplies instructions when that Skill activates. Reference files hold details it can read later. Each uses context at a different stage, so we measured them separately.
+
+## Most Skill bodies fit within the recommended size
+
+The [Agent Skills specification](https://agentskills.io/specification#progressive-disclosure) recommends instruction bodies below 5,000 tokens. It also recommends keeping the main file below 500 lines.
+
+Of **12,138 source-matched bodies**, **1,078** reach or exceed 5,000 estimated tokens. Three historical source versions were unavailable for body measurement.
+
+::skill-size-study{chart="body"}
+::
+
+The mean body contains **2,331 tokens**, compared with a median of **1,600**. The 90th percentile is **4,748**. The 95th percentile reaches **6,828**. Larger files pull the mean above the middle of the sample.
+
+The green band stops at 2,500 tokens, half the recommended body budget. That boundary is our editorial choice. Amber covers 2,501 to 4,999 tokens; red starts at 5,000. The colours measure context use. They do not score how useful a Skill is.
+
+We counted tokens with `o200k_base`. An Agent using a different tokenizer will count differently. These estimates cannot reproduce its exact billing or truncation behaviour.
+
+## Task categories reveal different file sizes
+
+Design Skills have the largest mean whole-file size among the recognised task categories: **13.1 kB**. Data Skills average **8.1 kB**. Design Skills also contain more reference files on average: **3.75**, compared with **1.49** for data Skills.
+
+::skill-size-study{chart="topics"}
+::
+
+The file-size column includes YAML frontmatter and the Markdown body. Its neutral bars show bytes. The first chart's colour bands use body tokens.
+
+Jev assigned task categories from the descriptions. It could not confidently place **4,068** of them. We kept those rows under “Task category uncertain”, rather than dropping a third of the corpus.
+
+We counted files under `references/`, excluding scripts, assets, and other supporting files. **12,091 inventories** were complete enough to count. Fifty remain missing. A complete inventory with no references contributes zero.
+
+The mean reference count is **2.27**, while the median is **zero**.
+
+## Short descriptions are not automatically clearer
+
+The specification caps a description at **1,024 characters** and asks authors to explain the task and when it applies. It does not prescribe an ideal length within that limit.
+
+We assessed four description properties with Jev 1.13:
+
+- Does it identify a concrete task and its subject or output?
+- Does it identify a recognisable request or selection situation?
+- Does it define a bounded scope?
+- Does it contain substantial repetition or filler?
+
+::skill-size-study{chart="descriptions"}
+::
+
+In the group below 100 characters, **45.5%** met the model's clear-guidance threshold. In the 400 to 599 character group, **81.4%** met it.
+
+This comparison does not show that adding words improves a description. Task complexity, author habits, and the rubric can also affect the result.
+
+Across all descriptions, **73.4%** met the clear-guidance threshold, **25.7%** remained uncertain, and **0.9%** received “Needs work”.
+
+We have not calibrated these model judgements against an independent human-labelled registry sample or tested actual Agent selection accuracy. Treat the labels as prompts for inspection.
+
+The median description contains **288 characters**. Its mean is **345**, and its 90th percentile is **664**. **111 descriptions** exceed the specification's 1,024-character cap, regardless of how clear they are.
+
+## The sample matters as much as the average
+
+The source export contains **20,510 registry rows**. Of those, **19,348** have a resolved source, a successful render, and a nonblank string description.
+
+The main study applies skilld's existing aggregator exclusion. That removes **7,207 descriptions across 35 repositories**, leaving **12,141 Skills**. The excluded group accounts for **37.2%** of usable descriptions.
+
+The aggregator flags predate this study. We did not independently verify that every flagged repository mirrors another source. The downloadable rows include the flags, so you can compare either population.
+
+| Description population | Skills | Repositories | Mean characters | Median characters |
+| --- | ---: | ---: | ---: | ---: |
+| All usable source descriptions | 19,348 | 2,332 | 334 | 287 |
+| Existing aggregator flags excluded | 12,141 | 2,297 | 345 | 288 |
+| Excluded aggregator group | 7,207 | 35 | 315 | 286 |
+| Unique normalised descriptions, filtered | 11,760 | 2,281 | 346 | 288 |
+
+To find duplicate descriptions, we trim leading and trailing whitespace and collapse whitespace runs. Comparing the exact text instead gives **11,764** unique descriptions.
+
+Deduplication barely moves the mean description length. Repository weighting changes it more.
+
+| Weighting | Mean description characters | Mean body tokens | Bodies at or above 5,000 tokens |
+| --- | ---: | ---: | ---: |
+| Each Skill contributes equally | 345 | 2,331 | 8.9% |
+| Each repository contributes equally | 383 | 2,593 | 10.8% |
+
+For repository weighting, we calculate each repository's mean or proportion, then average those results. A repository with one Skill contributes as much as one with hundreds.
+
+Body weighting covers **2,296 repositories** with at least one matched body. Description weighting covers all **2,297** filtered repositories.
+
+We also removed duplicate source files using their raw SHA-256 hashes. Of **11,983 measured unique files**, **8.9%** reach 5,000 tokens. The result barely changes.
+
+This is a registry snapshot. We did not weight by installs, GitHub stars, active usage, or Agent sessions. The averages cannot tell you what a typical developer has installed.
+
+## What Skill authors put in descriptions
+
+“Code” appears in **1,837 descriptions**, “review” in **1,252**, and “build” in **1,181**. Each word counts once per description, even when repeated.
+
+::skill-size-study{chart="words"}
+::
+
+The cloud uses English alphanumeric words, lowercase, with a minimum length of three characters. We remove a published common-word list and apply no stemming. “Review” and “reviewing” remain separate.
+
+The cloud shows common vocabulary in the English tokens we extracted. It cannot tell you which words improve Skill selection or describe every language.
+
+## What to change in your own Skill
+
+Start with selection guidance. Name the task, its input or output, and the situation where the Skill applies. Remove repeated claims before removing useful constraints.
+
+If the body approaches the recommended token or line budget, look for details you can move into reference files. Keep the common workflow in `SKILL.md`. Link the extra material from the step that needs it.
+
+The Agent may still read every reference file. Splitting the files lets it load details as needed, without guaranteeing lower total context use.
+
+Run [`skills-ref validate ./my-skill`](https://agentskills.io/specification#validation) to check frontmatter and naming conventions. It does not test task completion. We have not run the validator across this corpus.
+
+Then test real requests, including requests where the Skill should stay inactive. [Claude's authoring guidance](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices) recommends testing Skills through real usage.
+
+Our [guide to creating Skills for Claude Code, Codex and Gemini CLI](/learn/create-agent-skills) covers the authoring steps. You can also [browse Skills](/skills) and read their source before a run.
+
+## Method and downloads
+
+**Snapshot:** `2026-10-09-v1`, frozen skilld registry export. The population differs from the skills.sh top-installed sample.
+
+**Source identity:** each measurement carries repository, Skill name, source path, source commit, Git blob SHA, and raw SHA-256. Body measurements require the matching source version. Missing historical bodies remain null.
+
+**Description length:** Unicode code points from the frontmatter description. File size uses UTF-8 bytes. One kB equals 1,000 bytes.
+
+**Body size:** YAML frontmatter removed before counting. `gpt-tokenizer` 4.0.0 with `o200k_base` estimates tokens. Line counts refer to the body, separately from the whole-file line recommendation.
+
+**Reference inventories:** count paths beneath `references/` only when the stored supporting-file inventory matches its recorded file count. Missing inventories never become zero.
+
+**Assessment:** Jev 1.13.0, rubric `2026-10-09-v1`, using description text only. Clear guidance requires task, activation, and scope scores of at least 0.8, with filler at most 0.2. Needs work requires a score of at most 0.2 on any required property, or filler at least 0.8. All remaining results are uncertain. These scores are model outputs, not calibrated correctness probabilities.
+
+**Task categories:** use the model's primary category only when its score reaches 0.8. Otherwise retain “Task category uncertain”. The rubric link below includes the prompts.
+
+**Statistics:** arithmetic means and nearest-rank percentiles. Skill-weighted results are the default. Repository-weighted results average per-repository statistics. Description duplicates use normalised-text hashes. File duplicates use raw SHA-256. No confidence intervals are claimed for this nonrandom sample.
+
+**Reproduction:** download the measurements and run `pnpm exec tsx scripts/research/build-size-study.ts` in the [site repository](https://github.com/skilld-dev/skilld.dev). The generator rebuilds the summary from the committed frozen measurements. The manifest records export and measurement hashes. Source commits let readers inspect the original files.
+
+- [Measurements as CSV](/research/skill-md-size-2026-10-09/measurements.csv)
+- [Measurements as JSON](/research/skill-md-size-2026-10-09/measurements.json)
+- [Summary and weighting comparisons](/research/skill-md-size-2026-10-09/summary.json)
+- [Snapshot provenance and hashes](/research/skill-md-size-2026-10-09/provenance.json)
+- [Word counts and common-word list](/research/skill-md-size-2026-10-09/words.json)
+- [Shareable size infographic](/research/skill-md-size-2026-10-09/avg-skill-md-size.png)
+- [Assessment rubric and thresholds](https://github.com/skilld-dev/skilld.dev/blob/main/scripts/lib/description-review.ts)
+
+Codex prepared the analysis, charts, and article from skilld's source measurements. We measured source properties and model judgements. We did not test Skill safety, Agent performance, or how description length affects task success.
+
+When citing this study, use **skilld, “SKILL.md size and descriptions: 12,141 Skills measured”, 9 October 2026**, and include the snapshot identifier `2026-10-09-v1`.
diff --git a/migrations/0151_original_research_snapshots.sql b/migrations/0151_original_research_snapshots.sql
new file mode 100644
index 00000000..ec7e0da1
--- /dev/null
+++ b/migrations/0151_original_research_snapshots.sql
@@ -0,0 +1,11 @@
+-- Preserve original research independently of changing registry source rows.
+CREATE TABLE research_snapshots (
+  id TEXT PRIMARY KEY,
+  observed_at TEXT NOT NULL,
+  measurements_sha256 TEXT NOT NULL CHECK (length(measurements_sha256) = 64),
+  summary TEXT NOT NULL CHECK (json_valid(summary)),
+  provenance TEXT NOT NULL CHECK (json_valid(provenance))
+);
+
+INSERT INTO research_snapshots (id,observed_at,measurements_sha256,summary,provenance)
+VALUES ('skill-md-size:2026-10-09-v1','2026-10-09','0f8124ca1ba00bb71b12da39e9caca8d4542b0ac89e0a0f8e1eeedef77b445f7','{"snapshot":"2026-10-09-v1","measurementsSha256":"0f8124ca1ba00bb71b12da39e9caca8d4542b0ac89e0a0f8e1eeedef77b445f7","all":{"skills":19348,"repositories":2332,"descriptionMean":333.58791606367583,"descriptionMedian":287,"descriptionP90":619,"descriptionP95":788,"descriptionOver1024":115,"fileMeanKB":10.24333621612721,"fileMedianKB":7.313,"fileP90KB":20.617,"bodyMeasured":12138,"bodyMean":2330.82435327072,"bodyMedian":1600,"bodyP90":4748,"bodyP95":6828,"bodyAtLeast5000":1078,"bodyAtLeast5000Percent":8.881199538638985,"bodyAtLeast500Lines":752,"referenceMeasured":19296,"referenceMean":1.9732068822553896,"referenceMedian":0,"assessmentClearPercent":73.35474837328061},"excluded":{"skills":7207,"repositories":35,"descriptionMean":314.9178576384071,"descriptionMedian":286,"descriptionP90":500,"descriptionP95":686,"descriptionOver1024":4,"fileMeanKB":null,"fileMedianKB":null,"fileP90KB":null,"bodyMeasured":0,"bodyMean":null,"bodyMedian":null,"bodyP90":null,"bodyP95":null,"bodyAtLeast5000":0,"bodyAtLeast5000Percent":null,"bodyAtLeast500Lines":0,"referenceMeasured":7205,"referenceMean":1.4677307425399029,"referenceMedian":0,"assessmentClearPercent":null},"included":{"skills":12141,"repositories":2297,"descriptionMean":344.6706202125031,"descriptionMedian":288,"descriptionP90":664,"descriptionP95":827,"descriptionOver1024":111,"fileMeanKB":10.24333621612721,"fileMedianKB":7.313,"fileP90KB":20.617,"bodyMeasured":12138,"bodyMean":2330.82435327072,"bodyMedian":1600,"bodyP90":4748,"bodyP95":6828,"bodyAtLeast5000":1078,"bodyAtLeast5000Percent":8.881199538638985,"bodyAtLeast500Lines":752,"referenceMeasured":12091,"referenceMean":2.2744189893309072,"referenceMedian":0,"assessmentClearPercent":73.35474837328061},"uniqueDescriptions":{"skills":11760,"repositories":2281,"descriptionMean":345.6173469387755,"descriptionMedian":288,"descriptionP90":665,"descriptionP95":826,"descriptionOver1024":110,"fileMeanKB":10.23621615646262,"fileMedianKB":7.29,"fileP90KB":20.597,"bodyMeasured":11757,"bodyMean":2330.813983158969,"bodyMedian":1594,"bodyP90":4747,"bodyP95":6832,"bodyAtLeast5000":1044,"bodyAtLeast5000Percent":8.87981627966318,"bodyAtLeast500Lines":727,"referenceMeasured":11712,"referenceMean":2.298582650273224,"referenceMedian":0,"assessmentClearPercent":73.80952380952381},"uniqueSourceFiles":{"skills":11986,"repositories":2289,"descriptionMean":344.52911730352076,"descriptionMedian":287,"descriptionP90":664,"descriptionP95":826,"descriptionOver1024":110,"fileMeanKB":10.22252945102623,"fileMedianKB":7.296,"fileP90KB":20.597,"bodyMeasured":11983,"bodyMean":2326.7184344488023,"bodyMedian":1596,"bodyP90":4740,"bodyP95":6817,"bodyAtLeast5000":1061,"bodyAtLeast5000Percent":8.854210131018943,"bodyAtLeast500Lines":741,"referenceMeasured":11937,"referenceMean":2.2845773644969425,"referenceMedian":0,"assessmentClearPercent":73.46904722175871},"repositoryWeighted":{"repositories":2297,"descriptionMean":383.49164022524855,"bodyRepositories":2296,"bodyMean":2592.8508093383825,"bodyAtLeast5000Percent":10.845141130319213},"topics":[{"topic":"coding","skills":4055,"repositories":1085,"descriptionMean":341.75856966707767,"descriptionMedian":297,"descriptionP90":628,"descriptionP95":765,"descriptionOver1024":30,"fileMeanKB":10.033560295930984,"fileMedianKB":7.366,"fileP90KB":19.695,"bodyMeasured":4054,"bodyMean":2258.876418352245,"bodyMedian":1616,"bodyP90":4473,"bodyP95":6443,"bodyAtLeast5000":308,"bodyAtLeast5000Percent":7.597434632461766,"bodyAtLeast500Lines":277,"referenceMeasured":4043,"referenceMean":2.566658421963888,"referenceMedian":0,"assessmentClearPercent":68.3600493218249,"bands":{"green":2817,"amber":929,"red":308,"missing":1}},{"topic":"writing","skills":833,"repositories":401,"descriptionMean":341.124849939976,"descriptionMedian":270,"descriptionP90":724,"descriptionP95":888,"descriptionOver1024":3,"fileMeanKB":9.759247298919561,"fileMedianKB":7.58,"fileP90KB":18.761,"bodyMeasured":833,"bodyMean":2261.6938775510203,"bodyMedian":1636,"bodyP90":4755,"bodyP95":6949,"bodyAtLeast5000":72,"bodyAtLeast5000Percent":8.643457382953182,"bodyAtLeast500Lines":39,"referenceMeasured":830,"referenceMean":1.8590361445783132,"referenceMedian":0,"assessmentClearPercent":87.27490996398559,"bands":{"green":596,"amber":165,"red":72,"missing":0}},{"topic":"design","skills":700,"repositories":362,"descriptionMean":397.2442857142857,"descriptionMedian":345,"descriptionP90":731,"descriptionP95":847,"descriptionOver1024":8,"fileMeanKB":13.111121428571431,"fileMedianKB":8.42,"fileP90KB":25.792,"bodyMeasured":699,"bodyMean":3059.9327610872674,"bodyMedian":1912,"bodyP90":6199,"bodyP95":8504,"bodyAtLeast5000":89,"bodyAtLeast5000Percent":12.73247496423462,"bodyAtLeast500Lines":57,"referenceMeasured":691,"referenceMean":3.7539797395079595,"referenceMedian":0,"assessmentClearPercent":71.14285714285714,"bands":{"green":439,"amber":171,"red":89,"missing":1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diff --git a/public/research/skill-md-size-2026-10-09/avg-skill-md-size.png b/public/research/skill-md-size-2026-10-09/avg-skill-md-size.png
new file mode 100644
index 00000000..5a3862b2
Binary files /dev/null and b/public/research/skill-md-size-2026-10-09/avg-skill-md-size.png differ
diff --git a/public/research/skill-md-size-2026-10-09/measurements.csv b/public/research/skill-md-size-2026-10-09/measurements.csv
new file mode 100644
index 00000000..25555841
--- /dev/null
+++ b/public/research/skill-md-size-2026-10-09/measurements.csv
@@ -0,0 +1,19349 @@
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diff --git a/public/research/skill-md-size-2026-10-09/measurements.json b/public/research/skill-md-size-2026-10-09/measurements.json
new file mode 100644
index 00000000..6a2dca52
--- /dev/null
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new file mode 100644
index 00000000..fb68ca68
--- /dev/null
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+    "bodyAtLeast5000Percent": 8.854210131018943,
+    "bodyAtLeast500Lines": 741,
+    "referenceMeasured": 11937,
+    "referenceMean": 2.2845773644969425,
+    "referenceMedian": 0,
+    "assessmentClearPercent": 73.46904722175871
+  },
+  "repositoryWeighted": {
+    "repositories": 2297,
+    "descriptionMean": 383.49164022524855,
+    "bodyRepositories": 2296,
+    "bodyMean": 2592.8508093383825,
+    "bodyAtLeast5000Percent": 10.845141130319213
+  },
+  "topics": [
+    {
+      "topic": "coding",
+      "skills": 4055,
+      "repositories": 1085,
+      "descriptionMean": 341.75856966707767,
+      "descriptionMedian": 297,
+      "descriptionP90": 628,
+      "descriptionP95": 765,
+      "descriptionOver1024": 30,
+      "fileMeanKB": 10.033560295930984,
+      "fileMedianKB": 7.366,
+      "fileP90KB": 19.695,
+      "bodyMeasured": 4054,
+      "bodyMean": 2258.876418352245,
+      "bodyMedian": 1616,
+      "bodyP90": 4473,
+      "bodyP95": 6443,
+      "bodyAtLeast5000": 308,
+      "bodyAtLeast5000Percent": 7.597434632461766,
+      "bodyAtLeast500Lines": 277,
+      "referenceMeasured": 4043,
+      "referenceMean": 2.566658421963888,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 68.3600493218249,
+      "bands": {
+        "green": 2817,
+        "amber": 929,
+        "red": 308,
+        "missing": 1
+      }
+    },
+    {
+      "topic": "writing",
+      "skills": 833,
+      "repositories": 401,
+      "descriptionMean": 341.124849939976,
+      "descriptionMedian": 270,
+      "descriptionP90": 724,
+      "descriptionP95": 888,
+      "descriptionOver1024": 3,
+      "fileMeanKB": 9.759247298919561,
+      "fileMedianKB": 7.58,
+      "fileP90KB": 18.761,
+      "bodyMeasured": 833,
+      "bodyMean": 2261.6938775510203,
+      "bodyMedian": 1636,
+      "bodyP90": 4755,
+      "bodyP95": 6949,
+      "bodyAtLeast5000": 72,
+      "bodyAtLeast5000Percent": 8.643457382953182,
+      "bodyAtLeast500Lines": 39,
+      "referenceMeasured": 830,
+      "referenceMean": 1.8590361445783132,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 87.27490996398559,
+      "bands": {
+        "green": 596,
+        "amber": 165,
+        "red": 72,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "design",
+      "skills": 700,
+      "repositories": 362,
+      "descriptionMean": 397.2442857142857,
+      "descriptionMedian": 345,
+      "descriptionP90": 731,
+      "descriptionP95": 847,
+      "descriptionOver1024": 8,
+      "fileMeanKB": 13.111121428571431,
+      "fileMedianKB": 8.42,
+      "fileP90KB": 25.792,
+      "bodyMeasured": 699,
+      "bodyMean": 3059.9327610872674,
+      "bodyMedian": 1912,
+      "bodyP90": 6199,
+      "bodyP95": 8504,
+      "bodyAtLeast5000": 89,
+      "bodyAtLeast5000Percent": 12.73247496423462,
+      "bodyAtLeast500Lines": 57,
+      "referenceMeasured": 691,
+      "referenceMean": 3.7539797395079595,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 71.14285714285714,
+      "bands": {
+        "green": 439,
+        "amber": 171,
+        "red": 89,
+        "missing": 1
+      }
+    },
+    {
+      "topic": "operations",
+      "skills": 629,
+      "repositories": 297,
+      "descriptionMean": 369.5834658187599,
+      "descriptionMedian": 316,
+      "descriptionP90": 719,
+      "descriptionP95": 901,
+      "descriptionOver1024": 4,
+      "fileMeanKB": 10.752138314785377,
+      "fileMedianKB": 8.051,
+      "fileP90KB": 21.007,
+      "bodyMeasured": 629,
+      "bodyMean": 2455.1383147853735,
+      "bodyMedian": 1761,
+      "bodyP90": 4813,
+      "bodyP95": 7161,
+      "bodyAtLeast5000": 59,
+      "bodyAtLeast5000Percent": 9.379968203497615,
+      "bodyAtLeast500Lines": 34,
+      "referenceMeasured": 626,
+      "referenceMean": 2.7220447284345046,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 80.60413354531002,
+      "bands": {
+        "green": 408,
+        "amber": 162,
+        "red": 59,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "data",
+      "skills": 533,
+      "repositories": 228,
+      "descriptionMean": 332.0375234521576,
+      "descriptionMedian": 267,
+      "descriptionP90": 662,
+      "descriptionP95": 854,
+      "descriptionOver1024": 6,
+      "fileMeanKB": 8.112480300187608,
+      "fileMedianKB": 5.778,
+      "fileP90KB": 17.841,
+      "bodyMeasured": 533,
+      "bodyMean": 1871.3189493433397,
+      "bodyMedian": 1299,
+      "bodyP90": 4299,
+      "bodyP95": 5927,
+      "bodyAtLeast5000": 42,
+      "bodyAtLeast5000Percent": 7.879924953095685,
+      "bodyAtLeast500Lines": 25,
+      "referenceMeasured": 531,
+      "referenceMean": 1.4858757062146892,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 79.36210131332082,
+      "bands": {
+        "green": 406,
+        "amber": 85,
+        "red": 42,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "planning",
+      "skills": 481,
+      "repositories": 222,
+      "descriptionMean": 340.45738045738045,
+      "descriptionMedian": 254,
+      "descriptionP90": 757,
+      "descriptionP95": 926,
+      "descriptionOver1024": 5,
+      "fileMeanKB": 10.385291060291056,
+      "fileMedianKB": 7.992,
+      "fileP90KB": 20.981,
+      "bodyMeasured": 481,
+      "bodyMean": 2248.848232848233,
+      "bodyMedian": 1753,
+      "bodyP90": 4401,
+      "bodyP95": 5854,
+      "bodyAtLeast5000": 39,
+      "bodyAtLeast5000Percent": 8.108108108108109,
+      "bodyAtLeast500Lines": 32,
+      "referenceMeasured": 481,
+      "referenceMean": 1.5405405405405406,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 72.55717255717256,
+      "bands": {
+        "green": 329,
+        "amber": 113,
+        "red": 39,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "security",
+      "skills": 310,
+      "repositories": 151,
+      "descriptionMean": 345.0548387096774,
+      "descriptionMedian": 302,
+      "descriptionP90": 598,
+      "descriptionP95": 768,
+      "descriptionOver1024": 2,
+      "fileMeanKB": 11.083667741935484,
+      "fileMedianKB": 7.399,
+      "fileP90KB": 23.578,
+      "bodyMeasured": 310,
+      "bodyMean": 2541.6451612903224,
+      "bodyMedian": 1585,
+      "bodyP90": 5228,
+      "bodyP95": 7823,
+      "bodyAtLeast5000": 35,
+      "bodyAtLeast5000Percent": 11.290322580645162,
+      "bodyAtLeast500Lines": 24,
+      "referenceMeasured": 309,
+      "referenceMean": 1.7766990291262137,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 77.41935483870968,
+      "bands": {
+        "green": 208,
+        "amber": 67,
+        "red": 35,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "research",
+      "skills": 303,
+      "repositories": 184,
+      "descriptionMean": 367.52805280528054,
+      "descriptionMedian": 275,
+      "descriptionP90": 772,
+      "descriptionP95": 953,
+      "descriptionOver1024": 7,
+      "fileMeanKB": 9.388511551155117,
+      "fileMedianKB": 7.233,
+      "fileP90KB": 20.554,
+      "bodyMeasured": 303,
+      "bodyMean": 2062.825082508251,
+      "bodyMedian": 1626,
+      "bodyP90": 4688,
+      "bodyP95": 6083,
+      "bodyAtLeast5000": 28,
+      "bodyAtLeast5000Percent": 9.24092409240924,
+      "bodyAtLeast500Lines": 15,
+      "referenceMeasured": 303,
+      "referenceMean": 1.5016501650165017,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 79.53795379537954,
+      "bands": {
+        "green": 229,
+        "amber": 46,
+        "red": 28,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "other",
+      "skills": 229,
+      "repositories": 152,
+      "descriptionMean": 441.9781659388646,
+      "descriptionMedian": 370,
+      "descriptionP90": 812,
+      "descriptionP95": 889,
+      "descriptionOver1024": 4,
+      "fileMeanKB": 12.290598253275116,
+      "fileMedianKB": 8.838,
+      "fileP90KB": 26.098,
+      "bodyMeasured": 229,
+      "bodyMean": 2759.056768558952,
+      "bodyMedian": 2039,
+      "bodyP90": 5695,
+      "bodyP95": 6918,
+      "bodyAtLeast5000": 31,
+      "bodyAtLeast5000Percent": 13.537117903930131,
+      "bodyAtLeast500Lines": 13,
+      "referenceMeasured": 228,
+      "referenceMean": 2.6315789473684212,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 79.03930131004367,
+      "bands": {
+        "green": 133,
+        "amber": 65,
+        "red": 31,
+        "missing": 0
+      }
+    },
+    {
+      "topic": "unclear",
+      "skills": 4068,
+      "repositories": 1307,
+      "descriptionMean": 330.3446411012783,
+      "descriptionMedian": 269,
+      "descriptionP90": 652,
+      "descriptionP95": 817,
+      "descriptionOver1024": 42,
+      "fileMeanKB": 10.126215585054062,
+      "fileMedianKB": 6.848,
+      "fileP90KB": 20.884,
+      "bodyMeasured": 4067,
+      "bodyMean": 2321.8627981313007,
+      "bodyMedian": 1495,
+      "bodyP90": 4809,
+      "bodyP95": 7034,
+      "bodyAtLeast5000": 375,
+      "bodyAtLeast5000Percent": 9.220555692156381,
+      "bodyAtLeast500Lines": 236,
+      "referenceMeasured": 4049,
+      "referenceMean": 2.0123487280810077,
+      "referenceMedian": 0,
+      "assessmentClearPercent": 72.95968534906588,
+      "bands": {
+        "green": 2931,
+        "amber": 761,
+        "red": 375,
+        "missing": 1
+      }
+    }
+  ],
+  "descriptionBands": [
+    {
+      "min": 0,
+      "max": 99,
+      "total": 1103,
+      "clear": 502,
+      "uncertain": 515,
+      "needsWork": 86
+    },
+    {
+      "min": 100,
+      "max": 199,
+      "total": 2653,
+      "clear": 1794,
+      "uncertain": 840,
+      "needsWork": 19
+    },
+    {
+      "min": 200,
+      "max": 399,
+      "total": 4541,
+      "clear": 3540,
+      "uncertain": 995,
+      "needsWork": 6
+    },
+    {
+      "min": 400,
+      "max": 599,
+      "total": 2254,
+      "clear": 1834,
+      "uncertain": 420,
+      "needsWork": 0
+    },
+    {
+      "min": 600,
+      "max": 999,
+      "total": 1382,
+      "clear": 1079,
+      "uncertain": 301,
+      "needsWork": 2
+    },
+    {
+      "min": 1000,
+      "max": null,
+      "total": 208,
+      "clear": 157,
+      "uncertain": 51,
+      "needsWork": 0
+    }
+  ]
+}
diff --git a/public/research/skill-md-size-2026-10-09/words.json b/public/research/skill-md-size-2026-10-09/words.json
new file mode 100644
index 00000000..aa23c726
--- /dev/null
+++ b/public/research/skill-md-size-2026-10-09/words.json
@@ -0,0 +1 @@
+{ "method": "English alphanumeric tokens, lowercase, minimum three characters; count once per description. Existing aggregator exclusion. Common-word list applied. No stemming.", "stopWords": ["a", "about", "above", "across", "after", "again", "against", "all", "also", "am", "an", "and", "any", "are", "as", "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "can", "cannot", "could", "create", "creates", "creating", "do", "does", "doing", "done", "down", "during", "each", "either", "else", "even", "every", "few", "file", "files", "for", "from", "further", "get", "gets", "getting", "had", "has", "have", "having", "here", "how", "i", "if", "in", "into", "is", "it", "its", "itself", "just", "may", "me", "more", "most", "must", "my", "new", "no", "nor", "not", "now", "of", "off", "on", "once", "only", "or", "other", "our", "out", "over", "own", "per", "same", "should", "since", "so", "some", "such", "than", "that", "the", "their", "them", "themselves", "then", "there", "these", "they", "this", "those", "through", "to", "too", "under", "until", "up", "use", "used", "uses", "using", "very", "via", "was", "we", "were", "what", "when", "where", "which", "while", "who", "whom", "why", "will", "with", "within", "without", "would", "you", "your", "yours", "yourself", "skill", "skills", "agent", "agents", "based", "including", "include", "includes", "user", "users", "ask", "asks", "asking", "want", "wants", "wanted", "trigger", "triggers", "triggered", "one", "whether", "existing", "covers", "covered", "covering", "need", "needs", "needed", "help", "helps", "supports", "support", "provides", "provide", "specific", "requests", "request", "tasks", "task", "workflows", "workflow"], "words": [{ "text": "code", "count": 1837 }, { "text": "review", "count": 1252 }, { "text": "build", "count": 1181 }, { "text": "api", "count": 1049 }, { "text": "data", "count": 1007 }, { "text": "design", "count": 998 }, { "text": "work", "count": 925 }, { "text": "run", "count": 910 }, { "text": "project", "count": 879 }, { "text": "writing", "count": 869 }, { "text": "whenever", "count": 745 }, { "text": "building", "count": 732 }, { "text": "patterns", "count": 719 }, { "text": "app", "count": 706 }, { "text": "cli", "count": 703 }, { "text": "audit", "count": 667 }, { "text": "test", "count": 651 }, { "text": "says", "count": 635 }, { "text": "check", "count": 633 }, { "text": "write", "count": 632 }, { "text": "content", "count": 623 }, { "text": "first", "count": 592 }, { "text": "web", "count": 589 }, { "text": "read", "count": 580 }, { "text": "make", "count": 575 }, { "text": "plan", "count": 559 }, { "text": "mentions", "count": 557 }, { "text": "text", "count": 545 }, { "text": "add", "count": 537 }, { "text": "model", "count": 536 }, { "text": "product", "count": 534 }, { "text": "generate", "count": 529 }, { "text": "analysis", "count": 518 }, { "text": "reviewing", "count": 514 }, { "text": "asked", "count": 507 }, { "text": "set", "count": 506 }, { "text": "changes", "count": 505 }, { "text": "local", "count": 504 }, { "text": "search", "count": 504 }, { "text": "claude", "count": 499 }, { "text": "real", "count": 497 }, { "text": "page", "count": 490 }, { "text": "state", "count": 490 }, { "text": "context", "count": 484 }, { "text": "system", "count": 479 }, { "text": "architecture", "count": 464 }, { "text": "source", "count": 463 }, { "text": "working", "count": 462 }, { "text": "change", "count": 459 }, { "text": "tests", "count": 456 }, { "text": "find", "count": 454 }, { "text": "like", "count": 452 }, { "text": "github", "count": 443 }, { "text": "mcp", "count": 443 }, { "text": "tool", "count": 442 }, { "text": "fix", "count": 437 }, { "text": "time", "count": 437 }, { "text": "style", "count": 434 }, { "text": "multi", "count": 428 }, { "text": "performance", "count": 427 }, { "text": "full", "count": 426 }, { "text": "react", "count": 426 }, { "text": "rules", "count": 426 }, { "text": "video", "count": 421 }, { "text": "setup", "count": 415 }, { "text": "security", "count": 414 }, { "text": "components", "count": 411 }, { "text": "runs", "count": 407 }, { "text": "update", "count": 406 }, { "text": "tools", "count": 403 }] }
diff --git a/scripts/research/build-size-study.ts b/scripts/research/build-size-study.ts
new file mode 100644
index 00000000..493d7d23
--- /dev/null
+++ b/scripts/research/build-size-study.ts
@@ -0,0 +1,106 @@
+import { createHash } from 'node:crypto'
+import { readFileSync, writeFileSync } from 'node:fs'
+import { fileURLToPath } from 'node:url'
+
+interface Measurement {
+  repository: string
+  name: string
+  included: boolean
+  rawSha256: string
+  descriptionSha256: string
+  normalizedDescriptionSha256: string
+  descriptionCharacters: number
+  skillBytes: number | null
+  bodyTokens: number | null
+  bodyLines: number | null
+  referenceFiles: number | null
+  assessment: 'clear' | 'needs-work' | 'uncertain' | null
+  topic: string | null
+}
+
+const directory = fileURLToPath(new URL('../../public/research/skill-md-size-2026-10-09/', import.meta.url))
+const source = readFileSync(`${directory}measurements.json`, 'utf8')
+const rows: Measurement[] = JSON.parse(source)
+const included = rows.filter(row => row.included)
+const mean = (values: number[]) => values.length ? values.reduce((sum, value) => sum + value, 0) / values.length : null
+function quantile(values: number[], probability: number) {
+  const sorted = values.toSorted((a, b) => a - b)
+  return sorted.length ? sorted[Math.ceil(sorted.length * probability) - 1]! : null
+}
+const numeric = (values: (number | null)[]) => values.filter((value): value is number => value !== null)
+const unique = (values: Measurement[], key: (row: Measurement) => string) => [...new Map(values.map(row => [key(row), row])).values()]
+function grouped(values: Measurement[]) {
+  const groups = new Map<string, Measurement[]>()
+  for (const row of values) {
+    const group = groups.get(row.repository) ?? []
+    group.push(row)
+    groups.set(row.repository, group)
+  }
+  return [...groups.values()]
+}
+function stats(values: Measurement[]) {
+  const lengths = values.map(row => row.descriptionCharacters)
+  const tokens = numeric(values.map(row => row.bodyTokens))
+  const bytes = numeric(values.map(row => row.skillBytes))
+  const references = numeric(values.map(row => row.referenceFiles))
+  const assessments = values.filter(row => row.assessment !== null)
+  return {
+    skills: values.length,
+    repositories: grouped(values).length,
+    descriptionMean: mean(lengths),
+    descriptionMedian: quantile(lengths, 0.5),
+    descriptionP90: quantile(lengths, 0.9),
+    descriptionP95: quantile(lengths, 0.95),
+    descriptionOver1024: lengths.filter(n => n > 1024).length,
+    fileMeanKB: mean(bytes.map(n => n / 1000)),
+    fileMedianKB: quantile(bytes.map(n => n / 1000), 0.5),
+    fileP90KB: quantile(bytes.map(n => n / 1000), 0.9),
+    bodyMeasured: tokens.length,
+    bodyMean: mean(tokens),
+    bodyMedian: quantile(tokens, 0.5),
+    bodyP90: quantile(tokens, 0.9),
+    bodyP95: quantile(tokens, 0.95),
+    bodyAtLeast5000: tokens.filter(n => n >= 5000).length,
+    bodyAtLeast5000Percent: mean(tokens.map(n => n >= 5000 ? 100 : 0)),
+    bodyAtLeast500Lines: numeric(values.map(row => row.bodyLines)).filter(n => n >= 500).length,
+    referenceMeasured: references.length,
+    referenceMean: mean(references),
+    referenceMedian: quantile(references, 0.5),
+    assessmentClearPercent: mean(assessments.map(row => row.assessment === 'clear' ? 100 : 0)),
+  }
+}
+const groups = grouped(included)
+const bodyGroups = groups.filter(group => group.some(row => row.bodyTokens !== null))
+const bounds = [0, 100, 200, 400, 600, 1000, Infinity]
+const summary = {
+  snapshot: '2026-10-09-v1',
+  measurementsSha256: createHash('sha256').update(source).digest('hex'),
+  all: stats(rows),
+  excluded: stats(rows.filter(row => !row.included)),
+  included: stats(included),
+  uniqueDescriptions: stats(unique(included, row => row.normalizedDescriptionSha256)),
+  uniqueSourceFiles: stats(unique(included, row => row.rawSha256)),
+  repositoryWeighted: {
+    repositories: groups.length,
+    descriptionMean: mean(numeric(groups.map(group => mean(group.map(row => row.descriptionCharacters))))),
+    bodyRepositories: bodyGroups.length,
+    bodyMean: mean(numeric(bodyGroups.map(group => mean(numeric(group.map(row => row.bodyTokens)))))),
+    bodyAtLeast5000Percent: mean(numeric(bodyGroups.map(group => mean(numeric(group.map(row => row.bodyTokens)).map(n => n >= 5000 ? 100 : 0))))),
+  },
+  topics: ['coding', 'writing', 'design', 'operations', 'data', 'planning', 'security', 'research', 'other', 'unclear'].map((topic) => {
+    const values = included.filter(row => row.topic === topic)
+    return { topic, ...stats(values), bands: {
+      green: values.filter(row => row.bodyTokens !== null && row.bodyTokens <= 2500).length,
+      amber: values.filter(row => row.bodyTokens !== null && row.bodyTokens > 2500 && row.bodyTokens < 5000).length,
+      red: values.filter(row => row.bodyTokens !== null && row.bodyTokens >= 5000).length,
+      missing: values.filter(row => row.bodyTokens === null).length,
+    } }
+  }),
+  descriptionBands: bounds.slice(0, -1).map((min, index) => {
+    const max = bounds[index + 1]!
+    const values = included.filter(row => row.descriptionCharacters >= min && row.descriptionCharacters < max)
+    return { min, max: Number.isFinite(max) ? max - 1 : null, total: values.length, clear: values.filter(row => row.assessment === 'clear').length, uncertain: values.filter(row => row.assessment === 'uncertain').length, needsWork: values.filter(row => row.assessment === 'needs-work').length }
+  }),
+}
+writeFileSync(`${directory}summary.json`, `${JSON.stringify(summary, null, 2)}\n`)
+console.log(JSON.stringify({ included: summary.included, repositoryWeighted: summary.repositoryWeighted, uniqueDescriptions: summary.uniqueDescriptions, uniqueSourceFiles: summary.uniqueSourceFiles, excluded: summary.excluded }, null, 2))
diff --git a/test/e2e/og-image.spec.ts b/test/e2e/og-image.spec.ts
index edd203e4..5798d123 100644
--- a/test/e2e/og-image.spec.ts
+++ b/test/e2e/og-image.spec.ts
@@ -9,6 +9,15 @@ import { expect, test } from './test-utils'
  */
 
 // Page-level tests: validates meta tag presence + image rendering
+test('research article shares a rendered PNG', async ({ page, baseURL }) => {
+  await page.goto(`${baseURL}/learn/research/skill-md-size-study`, { waitUntil: 'domcontentloaded' })
+  const imageUrl = await page.locator('meta[property="og:image"]').getAttribute('content')
+  const response = await page.request.get(new URL(new URL(imageUrl!).pathname, baseURL!).href)
+  expect(response.status()).toBe(200)
+  expect(response.headers()['content-type']).toContain('image/png')
+  expect([...new Uint8Array(await response.body()).slice(0, 8)]).toEqual([137, 80, 78, 71, 13, 10, 26, 10])
+})
+
 const pageCases = [
   { path: '/', label: 'home page' },
   { path: '/skills', label: 'skills index' },