corpus-snapshot
Generate a corpus snapshot report — computes dimensions, topology, degree distribution, delta from previous. Helps with cluster, chain, and gap analysis sections.
What this skill does
# Corpus Snapshot Generate a point-in-time snapshot of the research corpus with computed metrics and analysis. Reads a snapshot template, fills `[COMPUTE]` sections with data, assists with `[ANALYZE]` sections, and writes the completed report. ## Triggers - "take a corpus snapshot" - "generate corpus report" - "snapshot the research" - "corpus snapshot" - `/corpus-snapshot` ## Parameters ### `--compute-only` (optional) Only compute data sections — skip analysis sections. Faster, fully automated. ### `--delta-only` (optional) Only compute the delta from the previous snapshot. Useful for tracking session progress. ### `--template <path>` (optional) Custom template path. Default: `.aiwg/reports/corpus-snapshot-template.md`. ### `--format` (optional) Output format: `full` (default for the report file), `summary` (terminal), `json` (programmatic). ## Prerequisites Before generating a snapshot, the following should be current: | Prerequisite | Command | Gates on | |-------------|---------|----------| | Citation edges complete | `/citation-backfill` | Topology metrics | | Indices up to date | `/corpus-index-build` | Group counts, hub analysis | | Stub rate < 10% | `/research-quality-audit` | Snapshot validity | If prerequisites are stale, the snapshot will include warnings. ## Execution Flow ### Phase 1: Collect Raw Metrics Scan the corpus and compute: **Dimensions:** - Total papers (node count) - Total citation edges (edge count) - Topics (unique tag count) - Authors (unique author count) - Year range (oldest → newest) - Source types distribution **Topology (from citation-network index):** - Graph density: edges / (nodes * (nodes-1)) - Average degree (mean edges per node) - Max hub (node with most connections) - Connected components count - Isolated nodes (degree 0) - Diameter estimate (longest shortest path in largest component) **Degree Distribution:** - Histogram: how many nodes have degree 0, 1-2, 3-5, 6-10, 11-20, 20+ - Power law fit (if applicable) **Quality Distribution:** - GRADE breakdown: High / Moderate / Low / Very Low - Doc depth: Full / Adequate / Stub / Skeleton (from quality-audit) - Source availability: PDF present / Full text extracted / Missing ### Phase 2: Compute Delta (if previous snapshot exists) Compare current metrics against the most recent snapshot: ``` Delta from previous snapshot (2026-04-10): Papers: +12 (360 → 372) Edges: +87 (1,160 → 1,247) Density: +0.001 (0.008 → 0.009) New topics: +2 (gui-agents, code-generation) Stubs fixed: 23 (88 → 65) New hubs: REF-364 (entered top 10) ``` ### Phase 3: Fill Template Sections Read the snapshot template and fill sections: **`[COMPUTE]` sections** — fully automated: - Dimensions table - Topology metrics - Degree distribution histogram - GRADE distribution - Delta table **`[ANALYZE]` sections** — agent-assisted: - **Cluster narrative**: describe the main clusters and their themes - **Chain analysis**: identify citation chains (A→B→C→D) and their significance - **Gap narrative**: summarize disconnected areas and bridge opportunities - **Trend analysis**: what's growing, what's stagnant ### Phase 4: Write Report Write the completed snapshot to: ``` .aiwg/reports/corpus-snapshot-YYYY-MM-DD.md ``` With frontmatter: ```yaml --- type: corpus-snapshot date: 2026-04-13 papers: 372 edges: 1247 density: 0.009 components: 9 stub_rate: 0.17 previous: corpus-snapshot-2026-04-10.md --- ``` ### Phase 5: Report Summary ``` Corpus Snapshot Generated ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Papers: 372 (+12) | Edges: 1,247 (+87) Density: 0.009 | Components: 9 Hub: REF-016 (34) | Isolated: 3 GRADE: 33% High, 24% Mod, 26% Low, 16% VLow Stubs: 65 (17%) | Full text: 54% Delta highlights: +12 papers inducted +87 citation edges (backfill) -23 stubs (expanded) +2 new topics Written to: .aiwg/reports/corpus-snapshot-2026-04-13.md ``` ## Template Format The default template uses markers for computed vs analyzed sections: ```markdown # Corpus Snapshot — [DATE] ## Dimensions [COMPUTE: dimensions-table] ## Topology [COMPUTE: topology-metrics] ## Degree Distribution [COMPUTE: degree-histogram] ## Quality Distribution [COMPUTE: grade-distribution] [COMPUTE: depth-distribution] ## Delta [COMPUTE: delta-from-previous] ## Cluster Analysis [ANALYZE: describe main clusters, their themes, and notable papers] ## Citation Chains [ANALYZE: identify significant citation chains and their meaning] ## Gaps and Opportunities [ANALYZE: summarize disconnected areas and bridge opportunities] ## Recommendations [ANALYZE: what should be inducted next, what needs expansion] ``` ## Integration Points | Component | Relationship | |-----------|-------------| | `corpus-index-build` | Reads index metrics (topology, hubs, components) | | `research-quality-audit` | Reads depth distribution; gates if stub rate > 10% | | `citation-backfill` | Must run before snapshot for accurate topology | | `research-gap-detect` | Cluster data feeds into gap narrative | | `research-status` | Snapshot is the detailed version of the health score | ## Examples ```bash # Full snapshot with analysis /corpus-snapshot # Just data, no analysis sections /corpus-snapshot --compute-only # Delta from previous snapshot only /corpus-snapshot --delta-only # Custom template /corpus-snapshot --template .aiwg/reports/custom-template.md # JSON metrics for dashboards /corpus-snapshot --format json ``` ## References - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/corpus-index-build/SKILL.md — Index metrics source - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-quality-audit/SKILL.md — Depth distribution source - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/citation-backfill/SKILL.md — Prerequisite for topology - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-gap-detect/SKILL.md — Cluster data for narrative - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-status/SKILL.md — Health scoring complement
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