Skip to main content

multi-agent-code-review

Multi-agent consensus-driven code review. Audits changes using security, performance, and style checkers.

Informations de source

Dépôt
chromium/chromium
Dernière activité de la source
13 août 2026 à 11:47
Langue détectée de SKILL.md
anglais
Étoiles
24 500
Forks
9 140

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Explorateur de fichiers
12 fichiers

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
name
multi-agent-code-review
description
Multi-agent consensus-driven code review. Audits changes using security, performance, and style checkers.
# Multi-Agent Code Review Protocol This skill implements a consensus-driven multi-agent review loop to audit code changes against specialized checklists (Security, Performance, Style). ## Stages Overview - **Stage 0: Environment Grounding & Safety Verification** - **Stage 1: Parallel Audits (Review)** - **Stage 2: Feedback Consolidation (Consolidate)** - **Stage 3: Upgrades (Optional Training)** ______________________________________________________________________ ## Stage 0: Environment Grounding & Safety Verification 1. **Verify Environment:** Discover and verify the active repository root, VCS (`JJ` or `GIT`), and availability of required build and test tools. 2. **Initialize State:** Create or read `review_state.magi.json` (complying with `schema.json`). If creating the file, initialize `iteration` to 1. If reading, increment `iteration`. 3. **Load Specs:** Read the goal and target files from `project.magi.json` if available. If running standalone, extract target files from the active git diff or gerrit CL. 4. **Transition:** Move to Stage 1. ______________________________________________________________________ ## Stage 1: Parallel Audits (Review) 1. **Select Scanners:** Read `ROUTING.md` (and merge any `routing_overlay` specified in `project.magi.json` if present) to select the appropriate Scanners based on the project spec. By default, select `Core Scanner`, `Security Scanner`, and `Performance Scanner`. If refactoring, add `Refactoring Scanner`. 2. **Execute Reviews:** Invoke the selected Scanners in parallel. Instruct each Scanner to review the target files against its domain-specific checklist in `personas/core/`. 3. **Format Output:** Each Scanner must output a `ReviewFeedback` JSON object (conforming to `schema.json#definitions/ReviewFeedback`) to a unique file in the temporary directory, named `review_feedback_<scanner_role>.json` (where `<scanner_role>` is the lowercased role name, e.g., `security_scanner`). 4. **Transition:** Once all Scanners complete, transition to Stage 2. ______________________________________________________________________ ## Stage 2: Feedback Consolidation (Consolidate) 1. **Invoke Consolidation:** Invoke the Consolidation subagent (conforming to the instructions in `references/consolidation.md`). 2. **Consolidation Task:** The Consolidation subagent must: - Read all `review_feedback_*.json` files in the temporary directory. - Perform a **Logical AND** across all scanner checklists. A checklist item in the consolidated state is only `true` if all Scanners evaluating that key asserted `true`. - Convert any failed checklist items (`false` values) or `unlisted_issues_found` into a list of actionable `constraints`. - Compare the new constraints with previous iterations (if any). If a checklist item is toggling state or if constraints are identical across iterations, set `oscillation_detected` to `true`. - If all checklist items are `true` and no issues are found, output `verdict: ACCEPT` and set `next_stage: COMPLETED` in `review_state.magi.json`. - If there are failures, output `verdict: REJECT`, write the consolidated `Constraints` object (conforming to `schema.json#/definitions/Constraints`) containing the compiled list of issues to `constraints.magi.json`, and set `next_stage: SYNTHESIS` (signaling that refinement is needed). - If `oscillation_detected` is `true`, set `next_stage: ESCALATION` (requires human intervention). 3. **Read Verdict:** The Review skill orchestrator reads the output `review_state.magi.json` to determine the next step. ______________________________________________________________________ ## Stage 3: Upgrades (Optional Training) 1. **Invoke Training:** If manually invoked after a review session, invoke the [multi-agent-skill-trainer](../multi-agent-skill-trainer/SKILL.md) skill to analyze feedback and upgrade the checklists. ______________________________________________________________________ ## Stage Handoff & Loop Limits - **Maximum Iterations:** The review loop (synthesis -> review -> synthesis) is limited to a maximum of **3 iterations**. If consensus is not reached by the 3rd iteration, the skill must abort and escalate to the user with a detailed conflict report. - **Handoff:** The review skill writes its output to `review_state.magi.json` and exits. The parent orchestrator is responsible for reading `next_stage` and invoking the synthesis phase if `REJECT` was returned. ## Evaluation & Testing When modifying this skill's workflow, routing, or schemas, ensure that the corresponding Promptfoo evaluation test suite is updated and passing: - [eval.promptfoo.yaml](../../prompts/eval/multi-agent-code-review/eval.promptfoo.yaml)
Voir sur GitHub