mcpbeat Sign in

Evidence Selfloop Skill for Codex

|

6k tokens
context cost
the whole folder, loaded on every use
2
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
496
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/WILLOSCAR/research-units-pipeline-skills --skill evidence-selfloop

What comes with it

14 755 bytes besides the instruction
scripts/run.py

The instruction itself

14 sections, as written by the author

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.

2) Read outline/subsection_briefs.jsonl

  • For each sub_id, capture axes + required_evidence_fields (what evidence types this subsection expects).

3) 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).

4) (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.

5) Read outline/evidence_drafts.jsonl

  • Surface blocking_missing (STOP signals), and check for missing comparisons / eval protocol / limitations that would force hollow writing.

6) (Optional) Read outline/anchor_sheet.jsonl

  • Check whether each subsection has at least a few citation-backed anchors (numbers / evaluation / limitations).

7) (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 cause | Inspect first | Smallest upstream fix chain |

|---|---|---|---|

| evidence_drafts.blocking_missing: no usable citation keys | mapped papers lack bibkey / bibkeys not in citations/ref.bib | papers/paper_notes.jsonl (bibkey fields), citations/ref.bib | C3 paper-notes (ensure bibkeys) -> C4 citation-verifier -> rerun evidence-binder -> rerun evidence-draft |

| blocking_missing: title-only evidence | retrieval/metadata lacks abstracts (or aggressive filtering) | papers/papers_raw.jsonl abstracts, papers/paper_notes.jsonl evidence_level | C1 literature-engineer (enrich metadata) OR C3 pdf-text-extractor (fulltext) -> rerun paper-notes |

| blocking_missing: no evidence snippets extractable | notes are too thin / evidence bank empty for mapped papers | papers/evidence_bank.jsonl (counts), papers/paper_notes.jsonl | C3 paper-notes (richer extraction; prefer fulltext when possible) -> rerun C4 packs |

| blocking_missing: no concrete evaluation tokens | notes/bank did not extract benchmarks/metrics/budgets | papers/paper_notes.jsonl (metrics/benchmarks fields), outline/anchor_sheet.jsonl | C3 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 weak | outline/subsection_briefs.jsonl (clusters), outline/mapping.tsv | C2 section-mapper (coverage) OR C3 subsection-briefs (better clusters) -> rerun evidence-draft |

| bindings.binding_gaps mentions benchmarks/metrics/protocol | binder cannot find evaluation-tagged evidence for this subsection | outline/evidence_binding_report.md (tag mix), papers/evidence_bank.jsonl tags | C3 paper-notes (tag/evidence extraction) OR C2 expand mapping for that subsection -> rerun evidence-binder |

| binding_gaps mentions security/threat model/attacks | mapped set lacks security-focused works or notes lack threat-model detail | outline/mapping.tsv, papers/paper_notes.jsonl | C2 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 coarse | outline/evidence_binding_report.md (tag mix), evidence bank tags | tighten 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>

Other skills for the same job

different authors, same section of the catalogue
Protocolsio Integration
by christophacham
×4

Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.

16k tokens
Tailored Resume Generator
by frostant
×4

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances

3k tokens
Excalidraw Diagram Generator
by github
vendor ×3

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

36k tokens scripts
Expo Dev Client
by openai
vendor ×3

Build and distribute Expo development clients locally or via TestFlight

961 tokens
Executing Plans
by ZhanlinCui
×3

Use when you have a written implementation plan to execute in a separate session with review checkpoints

542 tokens
Anndata
by christophacham
×3

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

16k tokens
Benchling Integration
by christophacham
×3

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

14k tokens
Biopython
by christophacham
×3

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

24k tokens

How to use it

Copy the folder

Take willoscar/evidence-selfloop from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.