This file is byte-identical to the first copy the registry indexed, one of 6 indexed copies. Same content hash, so the same checks give both the same grade.
mkl-adversarial-review from 00200200/maintainer-skills-lab is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
Conduct a bounded 3-turn adversarial code review debate (Author vs. Skeptic) to uncover subtle edge cases and race conditions under 1,500 tokens.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Multi-agent adversarial code review
Conduct a disciplined, bounded 3-turn adversarial code review debate between an Author agent (defending the patch and underlying invariants) and a Skeptic agent (actively attempting to construct disproving inputs and failure modes). Prevent sycophantic rubber-stamping and endless bike-shedding by enforcing strict token budgets and stopping rules.
## Protocol & Turn Budget
The debate is strictly bounded to 3 turns and under 1,500 total output tokens (<= 300 tokens per turn):
1. **Turn 1: Skeptic Attack (<= 300 tokens)**:
- Identify up to 2 concrete failure scenarios (such as concurrent race conditions, unhandled `None` states, resource leaks, or silent contract breaks).
- Every claimed defect MUST include a concrete trigger scenario, input sequence, or counterexample. Vague style complaints are forbidden.
2. **Turn 2: Author Defense & Rebuttal (<= 300 tokens)**:
- Cite specific invariants, types, or guards that prevent the claimed failure mode.
- If the counterexample is valid and unhandled, explicitly concede the defect and propose the minimal guard.
3. **Turn 3: Joint Consensus Synthesis (<= 300 tokens)**:
…
The line at the top of this page pins the label git:20261015.deeb4bf, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_im3ljzl4avfman7b
GET https://markdownregistry.com/api/v1/resolve?ref=00200200/maintainer-skills-lab/mkl-adversarial-review
GET https://markdownregistry.com/api/v1/blob/c3d0b77ac7cd2ccd74a0eabe9e5230e92aa761ece6aa76b63f8890a3691be75f
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-architect-editor/SKILL.md · Two-model pipeline routing high-level design to an Architect model, and mechanical search/replace edits to an Editor…
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-bisect-regression/SKILL.md · Pinpoint regressions using automated git bisect test scripts with zero prompt token waste.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-bump-version/SKILL.md · Evaluate conventional commits since latest tag, determine next SemVer, and surgically update version files.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-humanize/SKILL.md · Edit stiff or AI-sounding prose into natural writing while preserving meaning, facts, citations, and the author's point…
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-localize-fault/SKILL.md · Isolate suspect files and line ranges before code repair using AST skeletons and stack traces (< 300 tokens). Use…
