cluster-corpus-by-theme
$
npx mdskill add lyndonkl/claude/cluster-corpus-by-themeCluster posts by theme using Braun & Clarke's six-phase analysis.
- Reveals candidate sections from full post bodies without titles.
- Depends on the substacker corpus of published posts.
- Groups semantic codes via axial similarity and validates clusters.
- Outputs YAML with candidate handles, membership, and cohesion.
SKILL.md
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--- name: cluster-corpus-by-theme description: Performs axial-coding-style thematic clustering over the substacker corpus of published posts to surface candidate sections. Uses Braun & Clarke's six-phase thematic analysis — familiarization, initial coding, searching for themes, reviewing themes, defining themes, naming. Reads full bodies, not titles. Use when re-opening the section question. Trigger keywords: cluster, theme, axial coding, thematic analysis, candidate sections. --- # Cluster Corpus by Theme ## Workflow ``` Per Curator run: - [ ] Step 1: Read every post in corpus/published/** end-to-end (not just titles) - [ ] Step 2: Extract 3-5 codes per post (concepts, methods, domains) - [ ] Step 3: Group codes across posts by semantic similarity (axial) - [ ] Step 4: Validate clusters — split or merge where needed - [ ] Step 5: Report candidate clusters with membership, cohesion, outliers ``` ## Output ```yaml cluster_1: candidate_handle: "kalshi-log" posts: [list of slugs] cohesion: high | medium | low centroid_codes: [top 5 codes] outlier_posts: [weakly-attached members] rejected_clusters: [clusters with <3 posts] ``` ## Heuristics - Cohesion `high`: ≥5 posts, shared centroid, clear register. - Cohesion `medium`: 3-4 posts or mixed register. - Cohesion `low`: cluster exists but coherence is weak. - Reject any cluster with <3 posts (below the 3-post floor for real sections). ## Guardrails 1. Read full post bodies. Titles are marketing; bodies are the beat. 2. Do not force every post into a cluster. Outliers are legitimate. 3. If >30% of corpus doesn't cluster coherently (all cohesion low), emit "corpus too heterogeneous" → Curator abandons section proposals this run. 4. Include rejected clusters in output — they feed `recommend-prune` and "watch" candidates. 5. Single-threaded. Don't race on the corpus.
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