mcpbeat

Outline Refiner

willoscar/outline-refiner

| Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`.

7k 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 outline-refiner

What comes with it

24 595 bytes besides the instruction
scripts/run.py

The instruction itself

11 sections, as written by the author

Outline Refiner (Planner pass, NO PROSE)

Goal: make the outline *auditable* by adding an explicit planner stage that answers:

  • Do we have enough mapped evidence per H3?
  • Are the same few papers reused everywhere?
  • Are subsection axes still generic/scaffold-y?
  • Is the outline likely to produce a paper-like structure (final ToC budget: ~6–8 H2; fewer, thicker H3s)?

This is a deterministic “planner” unit: it must not write survey prose.

Inputs

Required:

  • outline/outline.yml
  • outline/mapping.tsv

Optional (best-effort diagnosis; may be missing early in the pipeline):

  • outline/OUTLINE_BUDGET_REPORT.md (if present: explains recent merges; helps interpret mapping/coverage changes)
  • papers/paper_notes.jsonl (for evidence levels)
  • outline/subsection_briefs.jsonl (for axis specificity)
  • GOAL.md (for scope drift hints)

Outputs

  • outline/coverage_report.md (bullets + small tables; NO PROSE)
  • outline/outline_state.jsonl (append-only JSONL; one record per run)

Workflow (planner pass, NO PROSE)

  • Parse outline/outline.yml to enumerate H2 sections + H3 subsections (section sizing / budget).
  • If outline/OUTLINE_BUDGET_REPORT.md exists, use it as the merge/change log so the coverage report can explain *why* structure changed.
  • Read outline/mapping.tsv and compute per-H3 coverage and reuse hotspots.
  • If papers/paper_notes.jsonl exists, summarize evidence levels (fulltext/abstract/title) for mapped papers.
  • If outline/subsection_briefs.jsonl exists, compute axis specificity (generic vs specific axes) per H3.
  • Optionally use GOAL.md to flag obvious scope drift (keywords not reflected in outline).
  • Write outline/coverage_report.md and append a run record to outline/outline_state.jsonl.

Freeze policy

  • If outline/coverage_report.refined.ok exists, the script will not overwrite outline/coverage_report.md.

Script

Quick Start

  • uv run python .codex/skills/outline-refiner/scripts/run.py --help
  • uv run python .codex/skills/outline-refiner/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>: workspace root
  • --unit-id <U###>: unit id (optional; for logs)
  • --inputs <semicolon-separated>: override inputs (rare; prefer defaults)
  • --outputs <semicolon-separated>: override outputs (rare; prefer defaults)
  • --checkpoint <C#>: checkpoint id (optional; for logs)

Examples

  • Planner-pass diagnostics after section-mapper:
  • uv run python .codex/skills/outline-refiner/scripts/run.py --workspace <workspace>

Troubleshooting

Issue: report is missing evidence-level or axis-specificity columns

Cause:

  • Optional inputs are missing (no papers/paper_notes.jsonl and/or no outline/subsection_briefs.jsonl).

Fix:

  • Run paper-notes and/or subsection-briefs, then rerun outline-refiner.

How to use it

Copy the folder

Take willoscar/outline-refiner 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.