EV

evidence-selfloop

Analyzes and fixes gaps in evidence-based writing pipelines.

Install

mkdir -p .claude/skills/evidence-selfloop && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12748" && unzip -o skill.zip -d .claude/skills/evidence-selfloop && rm skill.zip

Installs to .claude/skills/evidence-selfloop

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Evidence self-loop for surveys: read evidence bindings + evidence packs, then write an actionable upstream TODO plan (which stage/skill to fix) before writing more prose. Writes `output/EVIDENCE_SELFLOOP_TODO.md`. **Trigger**: evidence self-loop, evidence loop, evidence gaps, binding gaps, blocking_missing, 证据自循环, 证据缺口回路. **Use when**: C4 outputs exist (`outline/evidence_bindings.jsonl`, `outline/evidence_drafts.jsonl`) but writing looks hollow or C5 is BLOCKED due to thin evidence. **Skip if**: you are still pre-C3 (no notes/evidence bank yet), or you want to draft anyway and accept a lower evidence bar. **Network**: none. **Guardrail**: analysis-only; do not edit evidence/writing artifacts; do not invent facts/citations; only write the TODO report.
760 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Identify under-supported subsections in drafts
  • Determine if problem is mapping, extraction, or binding
  • Generate an actionable TODO list for fixing evidence
  • Route fixes to upstream evidence skills
  • Summarize subsections with blocking_missing or binding_gaps
  • Provide a per-subsection fix path

How it works

This skill analyzes intermediate evidence artifacts to identify gaps and generates a TODO list for rerunning upstream skills to strengthen the evidence base.

Inputs & outputs

You give it
Intermediate evidence artifacts (subsection_briefs.jsonl, evidence_bindings.jsonl, evidence_drafts.jsonl)
You get back
output/EVIDENCE_SELFLOOP_TODO.md (report-class)

When to use evidence-selfloop

  • Fixing gaps in research documents
  • Identifying missing evidence for reports
  • Improving output quality in research pipelines

About this skill

Evidence Self-loop (C3/C4 fix → rebind → redraft)

Purpose: make the evidence-first pipeline converge without writing filler prose.

This skill reads the intermediate evidence artifacts (briefs/bindings/packs) and produces an actionable TODO list that answers:

  • Which subsections are under-supported?
  • Is the problem mapping/coverage (C2) or evidence extraction (C3) or binding/planning (C4)?
  • Which skill(s) should be rerun, in what order, to unblock high-quality writing?

Inputs

  • outline/subsection_briefs.jsonl
  • outline/evidence_bindings.jsonl (expects binding_gaps / binding_rationale if available)
  • outline/evidence_drafts.jsonl (expects blocking_missing, comparisons, eval protocol, limitations)
  • Optional (improves routing):
    • outline/evidence_binding_report.md
    • outline/anchor_sheet.jsonl
    • papers/paper_notes.jsonl
    • papers/fulltext_index.jsonl
    • queries.md

Outputs

  • output/EVIDENCE_SELFLOOP_TODO.md (report-class; always written)

Self-loop contract (what “fixing evidence” means)

  • Prefer fixing upstream evidence, not writing around gaps.
  • If an evidence pack has blocking_missing, treat it as a STOP signal: strengthen notes/fulltext/mapping, then regenerate packs.
  • If bindings show binding_gaps, treat it as a ROUTING signal: either enrich the evidence bank for the mapped papers, expand mapping coverage, or adjust required_evidence_fields if unrealistic.

Recommended rerun chain (minimal):

  • If C3 evidence is thin: pdf-text-extractorpaper-notesevidence-binderevidence-draftanchor-sheetwriter-context-pack
  • If C2 coverage is weak: section-mapperoutline-refiner → (then rerun C3/C4 evidence skills)

Workflow (analysis-only)

  1. Read queries.md (if present)
  • Use it only as a soft config hint (evidence_mode / draft_profile); do not override the artifact contract.
  1. Read outline/subsection_briefs.jsonl
  • For each sub_id, capture axes + required_evidence_fields (what evidence types this subsection expects).
  1. Read outline/evidence_bindings.jsonl
  • For each sub_id, surface binding_rationale and binding_gaps (what the binder could/could not cover from the evidence bank).
  1. (Optional) Read outline/evidence_binding_report.md
  • Use it as a human-readable summary; treat it as a view of outline/evidence_bindings.jsonl, not a separate truth source.
  1. Read outline/evidence_drafts.jsonl
  • Surface blocking_missing (STOP signals), and check for missing comparisons / eval protocol / limitations that would force hollow writing.
  1. (Optional) Read outline/anchor_sheet.jsonl
  • Check whether each subsection has at least a few citation-backed anchors (numbers / evaluation / limitations).
  1. (Optional) Read papers/paper_notes.jsonl and papers/fulltext_index.jsonl
  • Use these to route fixes: if evidence is abstract-only and missing eval tokens, prefer enriching notes/fulltext before drafting prose.

What the report contains

  • Summary counts: subsections with blocking_missing, with binding_gaps, and common failure reasons.
  • Per-subsection TODO: the smallest upstream fix path (skills + artifacts) to make the subsection writeable.

Status semantics (unblock rules)

This skill is the prewrite router for evidence quality. Treat its Status: line as the unblock contract:

  • PASS: no blocking_missing and no binding_gaps -> proceed to C5 writing (but still scan non-blocking writability smells: low comparisons/eval/anchors often predict hollow prose).
  • OK: no blocking_missing, but some binding_gaps -> you may draft, but expect weaker specificity; prefer fixing gaps first.
  • FAIL: missing inputs OR any blocking_missing -> do not write filler prose; fix upstream and rerun C3/C4.

Routing matrix (symptom -> root cause -> upstream fix)

Use this as a semantic routing table (not a script checklist). The goal is to fix the earliest broken intermediate artifact.

Symptom (where you see it)Likely root causeInspect firstSmallest upstream fix chain
evidence_drafts.blocking_missing: no usable citation keysmapped papers lack bibkey / bibkeys not in citations/ref.bibpapers/paper_notes.jsonl (bibkey fields), citations/ref.bibC3 paper-notes (ensure bibkeys) -> C4 citation-verifier -> rerun evidence-binder -> rerun evidence-draft
blocking_missing: title-only evidenceretrieval/metadata lacks abstracts (or aggressive filtering)papers/papers_raw.jsonl abstracts, papers/paper_notes.jsonl evidence_levelC1 literature-engineer (enrich metadata) OR C3 pdf-text-extractor (fulltext) -> rerun paper-notes
blocking_missing: no evidence snippets extractablenotes are too thin / evidence bank empty for mapped paperspapers/evidence_bank.jsonl (counts), papers/paper_notes.jsonlC3 paper-notes (richer extraction; prefer fulltext when possible) -> rerun C4 packs
blocking_missing: no concrete evaluation tokensnotes/bank did not extract benchmarks/metrics/budgetspapers/paper_notes.jsonl (metrics/benchmarks fields), outline/anchor_sheet.jsonlC3 paper-notes (extract eval anchors) -> rerun anchor-sheet + evidence-draft
evidence pack comparisons are sparse (signals: comparisons low)clusters are not contrastable OR mapping coverage too weakoutline/subsection_briefs.jsonl (clusters), outline/mapping.tsvC2 section-mapper (coverage) OR C3 subsection-briefs (better clusters) -> rerun evidence-draft
bindings.binding_gaps mentions benchmarks/metrics/protocolbinder cannot find evaluation-tagged evidence for this subsectionoutline/evidence_binding_report.md (tag mix), papers/evidence_bank.jsonl tagsC3 paper-notes (tag/evidence extraction) OR C2 expand mapping for that subsection -> rerun evidence-binder
binding_gaps mentions security/threat model/attacksmapped set lacks security-focused works or notes lack threat-model detailoutline/mapping.tsv, papers/paper_notes.jsonlC2 expand mapping (+ C1 queries if needed) OR C3 enrich notes -> rerun binder/packs
binding report looks mechanically uniform across H3 (same mix, low tag variance)binder selection too recipe-like OR evidence bank tags too coarseoutline/evidence_binding_report.md (tag mix), evidence bank tagstighten required_evidence_fields + improve evidence bank tags, then rerun binder; avoid writing around non-specific bindings

Interface with the writer self-loop (avoid writing around evidence)

  • If writer-selfloop is FAIL due to missing anchors/comparisons and the corresponding writer pack has pack_warnings, stop and run this evidence self-loop: the section is telling you the pack is not writeable.
  • Prefer fixing evidence gaps once, upstream, rather than patching every H3 with generic filler.

What this skill does NOT do

  • It does not edit papers/*, outline/*, or sections/*.
  • It does not invent new facts/citations.
  • It does not "relax" quality by changing thresholds; it routes you to the earliest artifact to fix.

Script

Quick Start

  • uv run python .codex/skills/evidence-selfloop/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <U###> (optional)
  • --inputs <semicolon-separated> (optional override)
  • --outputs <semicolon-separated> (optional override; default writes output/EVIDENCE_SELFLOOP_TODO.md)
  • --checkpoint <C#> (optional)

Examples

  • Generate an evidence TODO list after C4 packs are generated:
    • uv run python .codex/skills/evidence-selfloop/scripts/run.py --workspace <workspace>

When not to use it

  • When still pre-C3 (no notes/evidence bank yet)
  • When wanting to draft prose with a lower evidence bar
  • When the goal is to edit evidence/writing artifacts

Limitations

  • Does not edit papers/*, outline/*, or sections/*
  • Does not invent new facts/citations
  • Does not change quality thresholds

How it compares

This workflow prioritizes fixing evidence gaps upstream before writing prose, ensuring factual accuracy and avoiding filler content, unlike drafting directly with incomplete evidence.

Compared to similar skills

evidence-selfloop side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
evidence-selfloop (this skill)05moReviewIntermediate
market-research-reports387moReviewAdvanced
competitive-intelligence156moNo flagsBeginner
clinical-decision-support42moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

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