research-quality-audit
Audit research corpus for shallow stubs, incomplete sections, missing source files, and doc depth issues. Detects docs written from abstracts rather than full papers and optionally auto-dispatches expansion agents.
Best use case
research-quality-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Audit research corpus for shallow stubs, incomplete sections, missing source files, and doc depth issues. Detects docs written from abstracts rather than full papers and optionally auto-dispatches expansion agents.
Teams using research-quality-audit should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/research-quality-audit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How research-quality-audit Compares
| Feature / Agent | research-quality-audit | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Audit research corpus for shallow stubs, incomplete sections, missing source files, and doc depth issues. Detects docs written from abstracts rather than full papers and optionally auto-dispatches expansion agents.
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Research Quality Audit Audit the research corpus for shallow stubs, incomplete documentation, and missing source files. Detects analysis docs written from abstracts alone (the root cause of the 88-stub incident) and reports doc depth metrics across the corpus. ## Triggers - "audit research quality" - "check for stubs" - "find shallow docs" - "research quality audit" - "how deep are the analysis docs?" - `/research-quality-audit` ## Parameters ### `--range REF-XXX:YYY` (optional) Audit a specific range of REF identifiers. Default: entire corpus. ### `--fix` (optional) Auto-dispatch expansion agents to deepen stubs. Each stub gets a focused agent that reads the full PDF/source and rewrites the analysis doc. ### `--threshold N` (optional) Minimum line count for a doc to be considered non-stub. Default: 80. ### `--format` (optional) Output format: `full` (default), `summary`, or `json`. ### `--pdf-check` (optional) Also verify that each REF has an actual PDF or source file, not just metadata. ## Execution Flow ### Phase 1: Corpus Scan 1. **Glob** all finding docs: `.aiwg/research/findings/REF-*.md` (and/or `documentation/references/REF-*.md` depending on corpus layout) 2. For each doc, collect: - **Line count** (total lines) - **Content lines** (non-empty, non-frontmatter, non-heading lines) - **Section count** (number of `##` headings) - **Key quote count** (blockquotes or inline quotes) - **Source availability** — does the PDF exist at the referenced `pdf_location`? - **Full text available** — does `sources/text/REF-XXX.txt` exist? - **Frontmatter completeness** — required fields present? ### Phase 2: Classification Classify each doc into quality tiers: | Tier | Content Lines | Sections | Quotes | Verdict | |------|-------------|----------|--------|---------| | **Full** | >= 150 | >= 8 | >= 3 | Comprehensive analysis | | **Adequate** | 80-149 | >= 5 | >= 1 | Meets minimum depth | | **Stub** | 40-79 | >= 3 | 0 | Written from abstract — needs expansion | | **Skeleton** | < 40 | any | 0 | Placeholder only — needs full rewrite | Additional flags: - **No PDF**: analysis exists but source PDF is missing - **No full text**: PDF exists but text extraction was not run - **Abstract-only indicators**: doc mentions "abstract" but no methodology/results sections ### Phase 3: Report ``` Research Quality Audit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Corpus: 372 documents Threshold: 80 content lines Quality Distribution: Full (150+): 124 (33%) ████████████████░░░░░░░░░░ Adequate (80-149): 89 (24%) ████████████░░░░░░░░░░░░░░ Stub (40-79): 98 (26%) █████████████░░░░░░░░░░░░░ Skeleton (<40): 61 (16%) ████████░░░░░░░░░░░░░░░░░░ Statistics: Mean content lines: 112 Median: 94 Min: 12 (REF-299) Max: 591 (REF-018) Source Availability: PDF present: 348 / 372 (94%) Full text extracted: 201 / 372 (54%) Missing PDF: 24 papers Missing text: 171 papers Stubs Requiring Expansion (159): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ REF-253 22 lines skeleton No PDF "Agentic Design Patterns" REF-254 35 lines skeleton Has PDF "Multi-Agent Debate" REF-255 45 lines stub Has PDF "Language Agent Tree Search" REF-256 48 lines stub No text "ReAct: Synergizing Reasoning" ... Top 10 Shallowest (candidates for immediate expansion): 1. REF-299 12 lines skeleton "Toolformer: Language Models Can..." 2. REF-312 15 lines skeleton "WebArena: A Realistic Web..." 3. REF-253 22 lines skeleton "Agentic Design Patterns..." ... ``` ### Phase 4: Auto-Fix (if --fix) When `--fix` is specified: 1. **Filter fixable stubs** — only expand docs that have a PDF or full text available 2. **Batch by priority** — shallowest docs first, batch into groups of 10 3. **Dispatch expansion agents** — each agent: - Reads the full PDF/extracted text for the source - Rewrites the analysis doc with comprehensive content - Target: 150+ content lines with methodology, findings, limitations, key quotes 4. **Re-audit after expansion** — run Phase 1-3 again to verify improvements 5. **Report** — docs expanded, mean line improvement, remaining stubs ``` Auto-Fix Results: Dispatched: 10 expansion agents (batch 1 of 16) Expanded: 10 / 10 Mean improvement: 77 → 161 lines (+109%) Remaining stubs: 149 Run again with --fix to process next batch. ``` ## Integration Points | Component | Relationship | |-----------|-------------| | `induct-research` | Quality audit should auto-run after batch induction | | `corpus-snapshot` | Gates on stub rate > 10% (#814) | | `research-lint` | `ref-frontmatter` rule catches incomplete metadata; quality-audit catches shallow content | | `research-status` | Doc depth is a component of corpus health scoring | | `research-acquire` | For stubs with missing PDFs, triggers acquisition before expansion | ## Distinction from Other Tools | Tool | What it checks | |------|---------------| | `research-lint` | **Structural** — frontmatter fields, naming, references resolve | | `research-quality-audit` | **Depth** — is the content substantive? Was the source actually read? | | `research-quality` | **Evidence** — GRADE assessment of the source's research quality | | `corpus-health` | **Aggregate** — overall corpus metrics including depth, structure, coverage | ## Examples ```bash # Full corpus audit /research-quality-audit # Audit specific range /research-quality-audit --range REF-253:372 # Auto-expand stubs (batch of 10) /research-quality-audit --fix # Strict threshold (120 lines minimum) /research-quality-audit --threshold 120 # Check source file availability /research-quality-audit --pdf-check # JSON for programmatic use /research-quality-audit --format json ``` ## References - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/induct-research/SKILL.md — Source of stubs when acquisition is skipped - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-acquire/SKILL.md — Acquires PDFs for stub expansion - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-lint/SKILL.md — Structural validation (complementary) - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-quality/SKILL.md — GRADE evidence assessment (complementary) - @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-status/SKILL.md — Health scoring includes depth metrics
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