rumil-clean
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.zipInstalls 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.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
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:
-
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. -
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_FUNNINESSonbe6d1a1d(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?" -
Dry-run first for anything non-trivial. For moves with complex payloads (links with reasoning, claims with long content), run
apply_move.py --dry-runfirst so you can show the user what the validated payload looks like before committing. -
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.
-
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|feedbackfollowed by an eval call id → run Mode A viarun_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_pipelinedirectly.
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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| rumil-clean (this skill) | 0 | 4mo | Review | Intermediate |
| literature-review | 559 | 2mo | Review | Advanced |
| openalex-database | 48 | 7mo | Review | Intermediate |
| market-research-reports | 38 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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