AGENT SKILL

autonomous-fix-review

Run autonomous multi-model code review on a finding. Invokes claude/codex/gemini via reviewd, evaluates consensus, and auto-commits or escalates to human.

使用场景代码审查
License 许可
MIT
最近更新
2026-08-03
Stars
★ 1
Forks
0

01 前言 Front Matter

name
autonomous-fix-review
context
fork
description
Run autonomous multi-model code review on a finding. Invokes claude/codex/gemini via reviewd, evaluates consensus, and auto-commits or escalates to human.
argument-hint
<finding-file-or-description>
user-invocable
true

02 资源文件 2

03 SKILL.md 正文

Autonomous Fix-Review Pipeline

Give it a finding, walk away, come back to a committed fix that passed multi-model review.

Usage

/autonomous-fix-review "Fix the null pointer in src/main.py:42"
/autonomous-fix-review docs/research/trail/findings/finding-001.yaml

7-Phase Pipeline

Phase Action Tool
1. Research Gather context (docs, code) /find-docs, /context7-mcp
2. Implement Dispatch coder in worktree with TDD subagent-driven-development
3. Review Invoke claude + codex + gemini reviewd.invoke_cli()
4. Consensus Evaluate 75% threshold mde.consensus.evaluate_consensus()
5. Decision Apply autonomy mode ConsensusGate.apply_autonomy()
6. Quality gate Run lint + test + validate uv run mde-py quality
7. Commit Commit on feature branch or escalate git commit

Autonomy Modes

  • supervised: Human approves everything except unanimous approval
  • semi-autonomous (default): Auto-retry on warnings, escalate on disagreement
  • autonomous: Proceed on any non-abort consensus

Decision Matrix

Consensus Supervised Semi-Autonomous Autonomous
>= 90% approve, no criticals PROCEED PROCEED PROCEED
>= 75% approve, no criticals ESCALATE RETRY PROCEED
Any critical findings ESCALATE ESCALATE ESCALATE
< 75% approve ESCALATE ESCALATE ESCALATE
All reject ABORT ABORT ABORT

Workflow

# Step 1: Parse the finding
finding=$(echo "$ARGUMENTS")

# Step 2: Research context
# Invoke /find-docs and /context7-mcp for the affected code area

# Step 3: Implement fix (if not already done)
# Dispatch coder subagent in worktree with TDD

# Step 4-5: Run multi-model review + consensus
uv run mde-py review "$finding" --autonomy semi-autonomous

# Step 6: Quality gate (auto-runs unless --skip-quality)
uv run mde-py quality

# Step 7: Based on decision output:
# PROCEED → commit on feature branch
# ESCALATE → present findings to human for decision
# RETRY → re-implement and re-review (up to max_retries)
# ABORT → stop, finding may be invalid

Constraints

  • Zero API keys — all LLM calls via subscription CLIs (claude, codex, gemini)
  • Worktree isolation for implementation phase
  • Human interview ONLY on consensus disagreement (ESCALATE)
  • Quality gate (6/6) required before commit

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