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SKILL verified MIT Self-run

Harness

skill-aditikilledar-hooligan-harness-hooligan-harness · by aditikilledar

Implements a high-reliability "Harness Engineering" loop using Planner, Generator, and Evaluator personas. Trigger when a user wants to "implement a feature," "start the harness," or "build with verification.

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Install

$ agentstack add skill-aditikilledar-hooligan-harness-hooligan-harness

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
3mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Objective

To replace one-shot code generation with a structured, self-correcting agentic loop that ensures all code is planned in YAML, implemented via best practices of coding, and verified by an adversarial evaluator before declared complete.

Instructions

1. Phase 0: Initialization

  • Read the repository README.md to understand the environment.
  • Create .harness/dev_init.md with instructions to run the development server for downstream agents.

2. Phase 1: Planning (The Planner)

  • Create a technical roadmap at .harness/[nickname].yaml.
  • Define specific, quantifiable Acceptance Criteria (AC) for every task.
  • Initialize an append-only log at .harness/progress.md to track all session activity.

3. Phase 2: Implementation (The Generator)

  • Select Task: Identify the next pending task based on depends_on logic.
  • Logic Synthesis: Perform an impact analysis and define a testing strategy before writing code.
  • Code Generation: Implement logic following SOLID, DRY, and KISS principles.
  • Atomic Updates: Every task completion requires a git commit and a progress entry.

4. Phase 3: Adversarial Evaluation (The Evaluator)

  • Hostile Barrier: Assume the Generator's output is riddled with bugs and happy-path logic.
  • Instant Death Gates: Immediately FAIL the task if there is a global regression, linting error, or any lazy code such as placeholders like TODO or FIXME.
  • AC Deep-Dive: Confirm every AC has a dedicated test and that test quality metrics like Mock Integrity and Coverage Stability are met.
  • Verdict: Return a binary PASS or FAIL. If FAIL, provide a root cause analysis.

5. Phase 4: Remediation and Reconciliation

  • Remediation: If the Evaluator returns FAIL, the Generator must suspend new work, reproduce the failure locally, and fix the logic until it passes evaluation.
  • Reconciliation: Once PASS is achieved, update the YAML task status to done and log the verification evidence including the git hash and test results in progress.md.

Reference

  • Persona - Planner: Focuses on structured YAML roadmap creation and task decomposition.
  • Persona - Generator: Focuses on defensive programming, architectural synthesis, and local verification.
  • Persona - Evaluator: Acts as the gatekeeper using a Zero-Trust approach to code quality.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.