agentleFS
fkguofkguo/nullius/skills/review-swarm/SKILL.md

review-swarm

Skill20 starsChanged 22 days agoMITView on GitHub

Use when a result, manuscript, derivation, or diff needs independent review. Runs clean-room multi-agent review loops across Claude/Gemini/Codex/OpenCode/Kimi with strict review-contract checks, fallback policy, and convergence gates.

6 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read each applicable reference before the named operation.

    line 21

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Review Swarm

Use this skill for independent review of a result, manuscript, derivation or diff. New multi-reviewer work uses scripts/bin/run_multi_task.py; a single advisory reviewer uses scripts/bin/review_one.py, which delegates to that same launcher. Resolve commands and code paths from this skill directory. The canonical public skill name is review-swarm.

Mandatory boundaries

  • Match the review investment to the artifact. Result-bearing, public-surface and irreversible artifacts warrant a second seat; cross-family review is preferred, not universally required. Internal implementation work closes on an independent single-family review plus local tests, never the author's tests alone. Record honest degradation and each independence axis.
  • A completed seat must be successful, contract-valid, uncontaminated and supported by exact identity evidence. Different routes or aliases do not establish different model families. Roster availability cannot substitute for completed quorum. Native reviewers stay outside launcher aggregation unless deliberately executed through its configured runner.
  • Give reviewers the material and neutral question, without prior verdicts or the author's initial judgment. Sealed derivation/recomputation receives only frozen raw inputs, persists its result and terminates before the candidate-visible comparison. Tool mode, ambient context, operator instructions and actual post-fallback execution affect independence; absence of a contamination flag is no attestation.
  • Acceptance requires zero BLOCKING findings, HIGH findings fixed or attached to their named acceptance point, and every LOW dispositioned. Apply the stricter grade when schemes overlap. Batch fixes and obtain independent confirmation on the current artifact; default maximum is five candidate review rounds, then narrow or escalate. Lexical Jaccard similarity is not scientific acceptance.
  • A verdict applies only to its frozen inputs. Require FRESH immediately before folding; changed bytes invalidate acceptance. A malformed delivery is unfinished delivery: bounded same-model normalization first, then documented fallback or honest unavailability. The leader cannot transcribe it into a valid vote. Missing, partial, unchecked, unavailable or stale evidence never counts as acceptance.
  • Static reading does not certify runtime preconditions, numerical reproduction or load-bearing structural claims. Use the applicable executing / candidate-withheld roles below and record what each check could falsify. A well-formed report or signed runtime capture proves neither provider truth nor scientific correctness.

Read only the relevant procedure

Read each applicable reference before the named operation. References retain the detailed binding contracts and executable examples.

OperationRequired reading
Select panel size, decide whether a second seat is owed, or scope an internal diffreview-scope.md
Choose native versus CLI seats, establish identity/family quorum, or degrade an unavailable rosterexecution-and-identity.md
Launch multiple seats or interpret convergence similaritylauncher-invocation.md
Launch one reviewer or assemble a source-extraction packetsingle-reviewer-invocation.md and applicable review-one-flags.md
Fix, confirm, reuse or fold a verdictgate-loop-and-freshness.md and delivery-and-runtime-evidence.md
Establish current full-notebook acceptancenotebook-content-review.md; whole current original text and dependencies are always required, including confirmation
Establish a method precondition or runtime invariantexecution-adversary.md; execute at the result's production configuration
Transcribe or extract source materialsource-fidelity-review.md; candidate-withheld extraction precedes comparison, including source/correction provenance and visual PDF checks when applicable
Review rendered artifacts, claim a published-value match, or certify a load-bearing structural claimresult-and-artifact-roles.md; choose every applicable role, not only one
Dispatch any independent reviewer, especially a sealed derivation or recomputationreviewer-isolation.md
Select model defaults, handle failures or override runner tools/prompts/deadlinesmodel-selection.md, then fallback-and-outages.md or backend-overrides-and-tool-modes.md as applicable
Request opt-in criteria-first --two-phase reviewtwo-phase-protocol.md; this does not seal a derivation
Locate outputs or compare runner delivery capabilitiesoutputs-and-runner-parity.md

For a single correctness reviewer, after reading the applicable scope, identity and isolation contracts:

python3 scripts/bin/review_one.py \
  --model codex/default \
  --artifact /path/to/notes.md \
  --role correctness

Omitted/default model selections use the backend's configured default; never inject a historical model name. This one-seat result remains advisory where the artifact warrants another seat.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.