| name | claim-evidence-matrix |
| description | Build a section-by-section claim–evidence matrix (`outline/claim_evidence_matrix.md`) from the outline and paper notes.
**Trigger**: claim–evidence matrix, evidence mapping, 证据矩阵, 主张-证据对齐.
**Use when**: 写 prose 之前需要把每个小节的可检验主张与证据来源显式化(outline + paper notes 已就绪)。
**Skip if**: 缺少 `outline/outline.yml` 或 `papers/paper_notes.jsonl`。
**Network**: none.
**Guardrail**: bullets-only(NO PROSE);每个 claim 至少 2 个证据来源(或显式说明例外)。
|
Claim–Evidence Matrix
Make the survey’s claims explicit and auditable before writing prose.
This should stay bullets-only (NO PROSE). The goal is to make later writing easy and to prevent “template prose” from sneaking in.
Inputs
outline/outline.yml
papers/paper_notes.jsonl
- Optional:
outline/mapping.tsv
Output
outline/claim_evidence_matrix.md
Workflow (heuristic)
Uses: outline/outline.yml, outline/mapping.tsv.
- For each subsection, write 1–3 claims that are:
- specific (mechanism / assumption / empirical finding)
- falsifiable (“X reduces tool errors under Y evaluation”, not “X is important”)
- For each claim, list ≥2 evidence sources:
- prefer different styles of evidence (method paper + eval/benchmark paper, or two competing approaches)
- Keep it tight: claim → evidence → (optional) caveat/limitations.
- If evidence is weak or only abstract-level, say so explicitly (don’t overclaim).
- If
bibkey exists in papers/paper_notes.jsonl, include [@BibKey] next to evidence items to make later prose/LaTeX conversion smoother.
Quality checklist
Helper script (optional)
Quick Start
uv run python .codex/skills/claim-evidence-matrix/scripts/run.py --help
uv run python .codex/skills/claim-evidence-matrix/scripts/run.py --workspace <workspace>
All Options
- See
--help (this helper is intentionally minimal)
Examples
- Generate a first-pass matrix, then refine manually:
- Run the helper once, then refine
outline/claim_evidence_matrix.md by tightening claims and adding caveats when evidence is abstract-level.
Notes
- The helper generates a baseline matrix (claims + evidence) and never overwrites non-placeholder work; in
pipeline.py --strict it will be blocked only if placeholder markers remain.
Troubleshooting
Issue: claims are generic or read like outline boilerplate
Fix:
- Tighten each claim to a falsifiable statement and add an explicit caveat if evidence is abstract-only.
Issue: you cannot add [@BibKey] because keys are missing
Fix:
- Run
citation-verifier to generate citations/ref.bib, then use the produced keys in the matrix.