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adversarial-coach

by Vaskin Kissoyan · GitHub ↗ · v0.9.0
cross-platform ✓ Security Clean
1709
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Install in OpenClaw
/install adversarial-coach
Description
Adversarial implementation review based on Block's g3 dialectical autocoding research. Use when validating implementation completeness against requirements with fresh objectivity.
README (SKILL.md)

/coach - Adversarial Implementation Review

Usage

/coach [requirements-file]
  • /coach - Infer requirements from context
  • /coach requirements.md - Validate against specific file

Coach-Player Loop

You orchestrate this dialectical loop between implementing agent (player) and reviewer (coach):

  1. You (player) implement features
  2. /coach invokes adversarial review with independent evaluation of compliance to requirements
  3. Coach returns: IMPLEMENTATION_APPROVED or specific fixes
  4. Address feedback, loop until approved

Review Process

Step 1: Identify Requirements

Check (in order):

  • Specified requirements file or issue/ticket mentioned
  • requirements.md, REQUIREMENTS.md, SPEC.md, TODO.md
  • Conversation context; ask user if nothing found

Step 2: Adversarial Review

Review with fresh objectivity - discard prior knowledge, don't rationalize shortcuts.

Check Category Items
Requirements Each item: implemented or missing with specific gap
Compilation Compiles? Tests pass? Runs?
Common Gaps Auth on endpoints, token refresh endpoint, HTTPS, bcrypt for passwords, error handling, input validation
Functional Test actual flows (not just compilation), verify edge cases work
Test Coverage Auth error cases (401/403), token expiry, invalid inputs, rate limits

Step 3: Return Verdict

If approved (>95% complete):

IMPLEMENTATION_APPROVED

- [Requirement 1]: Verified
- [Requirement 2]: Verified
- Compilation: Success
- Tests: All passing

If fixes needed:

REQUIREMENTS COMPLIANCE:
- [Requirement]: Implemented
- [Requirement]: Missing - [specific gap]

IMMEDIATE ACTIONS NEEDED:
1. [Specific fix with file/line if known]
2. [Specific fix]

Key Principles

Rigorous but fair:

  • Catch real gaps (security, logic, data flow), not style preferences
  • Functionality over aesthetics; always flag security issues (auth, crypto, validation)

Concise:

  • Bullets, not essays; specific issues, not vague concerns
  • No file contents or verbose analysis in output

Fresh context is your superpower:

  • Review as if you've never seen this code; validate against requirements, not intentions

Magic Signal

IMPLEMENTATION_APPROVED = termination signal

Only use when: all requirements met, code compiles/runs, tests pass, no significant gaps.

If in doubt, don't approve.

Minimal Example

User: /coach SPEC.md

Claude:
REQUIREMENTS COMPLIANCE:
- User registration endpoint (bcrypt hashing)
- Login with JWT tokens
- Logout endpoint - not implemented
- Token refresh - missing /auth/refresh endpoint

IMMEDIATE ACTIONS NEEDED:
1. Add POST /auth/logout endpoint
2. Add POST /auth/refresh for token renewal
3. Add tests for 401 on expired tokens

---

User: [fixes] /coach SPEC.md

Claude:
IMPLEMENTATION_APPROVED
- All auth endpoints verified (register, login, logout, refresh)
- 18 tests passing including auth error cases

Research

Usage Guidance
This skill is an instruction-only code-review helper and appears internally consistent. Before installing: 1) Be aware the coach will ask to inspect your repository/workspace and may ask you to run builds/tests — grant only the minimal access required. 2) If you plan to let the agent run commands or CI, ensure build environments don't expose secrets (API keys, private tokens). 3) Review any file contents or code the skill outputs before sharing externally. 4) Because the skill can be invoked autonomously by agents, consider who can grant it access to your codebase; revoke access if you see unexpected behavior. Overall, the skill itself does not request credentials or install software and looks safe for use as a review assistant.
Capability Analysis
Type: OpenClaw Skill Name: adversarial-coach Version: 0.9.0 The OpenClaw skill 'adversarial-coach' is designed for an AI agent to perform code reviews against specified requirements. The `SKILL.md` file contains instructions for the agent to identify requirements from common files like `requirements.md` or `SPEC.md` and then conduct an 'adversarial review' focusing on security, compilation, and functional gaps. There is no evidence of malicious intent, data exfiltration, unauthorized execution, persistence mechanisms, or prompt injection attempting to subvert the agent's core purpose. All instructions align with the stated goal of a rigorous code review.
Capability Assessment
Purpose & Capability
Name/description (adversarial implementation review) align with the SKILL.md instructions: locate requirements, perform independent checks (compilation, tests, edge cases), and return a verdict. The skill requests no unrelated binaries, env vars, or config paths.
Instruction Scope
Instructions tell the agent to look for requirements files, use conversation context, run compilation/tests, and report specific gaps. They do not instruct reading unrelated system files, exfiltrating secrets, or posting results to external endpoints. The guidance is appropriately scoped to code-review tasks.
Install Mechanism
No install spec and no code files — instruction-only. This minimizes disk-write and supply-chain risk.
Credentials
The skill requires no environment variables, credentials, or config paths. The checks it describes (compilation, tests) are proportional to a review task and do not imply access to unrelated services or secrets.
Persistence & Privilege
always is false and the skill does not request persistent system modifications or cross-skill configuration changes. Autonomous invocation is allowed by default but not combined with other high-risk behaviors.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install adversarial-coach
  3. After installation, invoke the skill by name or use /adversarial-coach
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.9.0
From Foundry: Adversarial implementation review based on Block's g3 dialectical autocoding res
Metadata
Slug adversarial-coach
Version 0.9.0
License
All-time Installs 4
Active Installs 3
Total Versions 1
Frequently Asked Questions

What is adversarial-coach?

Adversarial implementation review based on Block's g3 dialectical autocoding research. Use when validating implementation completeness against requirements with fresh objectivity. It is an AI Agent Skill for Claude Code / OpenClaw, with 1709 downloads so far.

How do I install adversarial-coach?

Run "/install adversarial-coach" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is adversarial-coach free?

Yes, adversarial-coach is completely free (open-source). You can download, install and use it at no cost.

Which platforms does adversarial-coach support?

adversarial-coach is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created adversarial-coach?

It is built and maintained by Vaskin Kissoyan (@killerapp); the current version is v0.9.0.

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