Safely applies research improvements suggested by reviews through automated pipelines or guided manual steps.

Install

mkdir -p .claude/skills/rumil-clean && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12308" && unzip -o skill.zip -d .claude/skills/rumil-clean && rm skill.zip

Installs to .claude/skills/rumil-clean

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.

Guided cleanup of research on a rumil question. Two modes — (a) pipeline mode wraps rumil's existing grounding/feedback clean pipelines given an evaluate call id; (b) interactive mode (default) walks a punch list conversationally, proposes each mutation, talks it through with the user, and applies accreting-only moves via the chat envelope after explicit consent. Use when rumil-review has produced a punch list and the user wants to act on it safely.
453 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Run rumil's grounding pipeline
  • Run rumil's feedback update pipeline
  • Walk a punch list conversationally
  • Propose mutations to research data
  • Apply accreting-only moves after user consent
  • Show validated payload of complex moves

How it works

The skill operates in two modes: a pipeline mode that wraps rumil's internal clean pipelines for grounding and feedback, and an interactive mode that walks a punch list, proposing and applying accreting-only mutations with explicit user consent.

Inputs & outputs

You give it
Rumil question ID, evaluate call ID, punch list items
You get back
Applied fixes to research data, updated source pages, grounded claims, updated feedback

When to use rumil-clean

  • Cleaning up research findings
  • Applying grounding feedback
  • Updating research claims
  • Fixing links in research documents

About this skill

rumil-clean

Turns a review into applied fixes, safely. There's a fast lane and a careful lane.

Two modes

Mode A — pipeline (rumil-internal)

When you already have a completed evaluate call on a question and want rumil's own clean pipelines to run against its evaluation text, this mode wraps them:

PYTHONPATH=.claude/lib uv run python -m rumil_skills.run_clean_pipeline \
    grounding <eval_call_id>

PYTHONPATH=.claude/lib uv run python -m rumil_skills.run_clean_pipeline \
    feedback <eval_call_id>

grounding runs clean.grounding.run_grounding_feedback — creates source pages and grounds claims in real evidence.

feedback runs clean.feedback.run_feedback_update — applies proposed changes from a feedback-style evaluation (new claims, new investigations, link fixes).

Both are rumil-mediated: the actual mutations are chosen by a rumil-internal LLM pipeline, not by you. You're just the trigger. The run is tagged origin=claude-code skill=rumil-clean pipeline=... and gets its own trace URL. Use this when you trust the rumil clean pipeline to do the right thing and just want it to run.

Prereq: the user must have already run /rumil-dispatch evaluate <question_id> to produce an EVALUATE call with evaluation text in its review_json.

Mode B — interactive (cc-mediated, accreting-only)

This is the conversational lane. The user typically lands here after running /rumil-review <question_id> and getting back a punch list. Your job is to walk the punch list one item at a time, proposing each mutation, talking it through, and applying it after explicit user consent. Strict rules:

  1. Accreting-only. Every applied move goes through apply_move.py --accreting-only. The allowlist is: CREATE_* moves, non-destructive LINK_* moves, FLAG_FUNNINESS, REPORT_DUPLICATE, PROPOSE_CONCEPT, LOAD_PAGE. Never propose or apply REMOVE_LINK, UPDATE_EPISTEMIC, CHANGE_LINK_ROLE, or PROMOTE_CONCEPT from this skill — they modify or migrate existing state. If a punch-list item requires one of those, say so and suggest the user use the destructive tool manually or run the rumil-mediated clean pipeline instead.

  2. Talk through, then act. Never apply a move without stating what you're about to do and giving the user a chance to object. Format:

    "Proposing: FLAG_FUNNINESS on be6d1a1d (the AI-governance-determines-space-allocation claim) — the headline frames it as a claim but the body reads as a meta-reframe. OK to apply?"

  3. Dry-run first for anything non-trivial. For moves with complex payloads (links with reasoning, claims with long content), run apply_move.py --dry-run first so you can show the user what the validated payload looks like before committing.

  4. One move per turn. Don't batch five mutations in a single response. Each move should be its own little propose-apply cycle so the user can stop or redirect anytime.

  5. Always gloss IDs. When you cite a page or call, include a 3-8 word summary in parens: be6d1a1d (the claim about power-driven allocation). Never drop bare hex.

Invocation

There's no single ! block here because the skill dispatches based on args. Parse $ARGUMENTS yourself:

  • If the first arg is --pipeline grounding|feedback followed by an eval call id → run Mode A via run_clean_pipeline.
  • If the first arg is a question ID → run Mode B: first load the question context via gather_review_context, then walk whatever punch list is already in the conversation (or ask the user for one).

If no punch list exists yet, suggest the user run /rumil-review <qid> first, unless they explicitly want you to produce one here.

What to show the user after each applied move

Just the one-line skill output:

⚙ cc-mediated move: <type>
• created page <short_id>
<one-line headline of result.message>

Plus (once per session) the envelope trace URL, so they can open the rumil frontend to see the whole envelope Call and the moves flowing into it.

Escape hatches

  • If the user changes their mind mid-session, stop applying and summarize what's been done + what's still pending from the punch list.
  • If a proposed mutation would require destructive capability (edit an existing claim, remove a link, change a link role), say so and skip it — do not escalate to non-accreting moves. The user can handle those manually.
  • If the user wants to switch to Mode A, just exit the conversational loop and fire run_clean_pipeline directly.

When not to use it

  • When proposing or applying destructive moves like REMOVE_LINK
  • When batching multiple mutations in a single response
  • When the user wants to switch to Mode A mid-session

Limitations

  • Interactive mode is accreting-only; destructive moves are not supported
  • Requires an `evaluate` call to have been run for pipeline mode
  • Requires a punch list to be present for interactive mode

How it compares

This skill offers both automated and interactive methods for applying fixes to research data, providing a controlled and transparent process for modifying information, unlike direct, unreviewed changes.

Compared to similar skills

rumil-clean side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
rumil-clean (this skill)04moReviewIntermediate
literature-review5592moReviewAdvanced
openalex-database487moReviewIntermediate
market-research-reports387moReviewAdvanced

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Example prompts that trigger this skill in your AI assistant.

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