effective-harnesses

name	effective-harnesses
description	Long-running agent project harness for Codex, OpenClaw, Claude Code, and other coding agents. Use to initialize or maintain projects that need feature decomposition, resumable progress tracking, test-gated completion, session recovery, and git-backed checkpoints through files such as feature_list.json, init.sh, progress logs, and coding standards.

Effective Harnesses

Purpose

Use this skill to keep long-running implementation or research projects reliable across many agent sessions. Maintain a small set of durable project files so any compatible agent can resume work without relying on hidden chat context.

Core Principles

• Work on one feature at a time unless the user explicitly requests parallel work.
• Track every feature in feature_list.json.
• Run each feature's test/check command before marking it complete.
• Keep a human-readable progress log for recovery.
• Commit meaningful checkpoints when the environment and user instructions require git commits.
• Prefer portable files and commands over host-specific slash commands.

Harness Files

Create or maintain these files at the project root:

File	Purpose
feature_list.json	Canonical list of features, priorities, statuses, and test results.
init.sh / init.ps1 / init.py	Optional project bootstrap/start command for future sessions. Prefer init.ps1 or init.py on Windows native.
agent-progress.md	Chronological progress notes, decisions, blockers, and next steps.
CODING_STANDARDS.md	Optional project-specific coding/test/review conventions.

If the project already has Claude-specific names such as claude-progress.txt, keep them if users rely on them, but prefer the portable agent-progress.md name for new projects.

Initialize a Project

1. Identify the project goal, stack, default test command, and startup command.
2. Create feature_list.json from the schema below.
3. Create a repeatable bootstrap if useful: init.sh for Unix-like shells, init.ps1 or init.py for Windows native. Do not require Bash on Windows.
4. Create agent-progress.md with initial context, assumptions, and next feature.
5. Create or update CODING_STANDARDS.md only when project conventions are known or requested.
6. Run the bootstrap and/or test command if safe in the current environment.

Add a Feature

Append a new feature object with:

• Unique sequential ID such as feat-001.
• Category: functional, bugfix, refactor, research, or another user-approved category.
• Priority: lower number means higher priority.
• Clear description and concrete steps.
• Test/check command that proves completion.
• Initial status: pending and passes: false.

Resume a Session

At the beginning of a resumed project:

1. Read feature_list.json.
2. Read recent git history if available.
3. Read agent-progress.md or legacy progress logs.
4. Select the highest-priority feature with status other than completed.
5. Run init.sh, init.ps1, init.py, or the documented startup command when appropriate for the host OS.
6. Run a baseline test/check when safe, then begin work.

Complete a Feature

Before marking a feature complete:

1. Verify implementation/research artifacts exist.
2. Run the feature's test_command or a justified equivalent.
3. Store a concise test_output summary.
4. Set test_status to passed only on success; otherwise use failed and leave passes: false.
5. Update agent-progress.md with what changed, validation, and next steps.
6. Commit if required by user or project policy.

File-Mode Research Harness Pattern

For large research tasks, prefer a file-mode harness instead of chat-heavy handoff:

1. Create a domain data directory such as data/<domain>/ plus a depth/ subdirectory.
2. Track run status and convergence in data/<domain>/index.json.
3. Split research into independent dimensions and write each dimension to depth/<dimension>.json.
4. If the user explicitly requests/allows parallel agents and the host supports them, workers should write JSON files and reply only DONE; otherwise perform the same dimensions sequentially.
5. Merge depth files into a main JSON data file, export any required CSV tables, then generate deliverables.
6. Validate JSON/CSV/report files before marking the feature complete.

For renewable-energy market intelligence, use $renewable-market-research, which specializes this pattern for country + technology research and full/lite report generation.

feature_list.json Schema

{
  "version": "1.1",
  "project": "Project name",
  "description": "One-sentence project goal",
  "test_command": "npm test",
  "created": "YYYY-MM-DD",
  "features": [
    {
      "id": "feat-001",
      "category": "functional",
      "priority": 1,
      "description": "Feature description",
      "steps": ["Step 1", "Step 2"],
      "test_command": "npm test",
      "test_status": "pending",
       
       
       
    
  

Allowed status values: pending, in_progress, blocked, completed. Allowed test_status values: pending, running, passed, failed, skipped. Use skipped only with a clear reason in test_output.

Progress Log Format

Append entries like:

## YYYY-MM-DD — feat-001 short title

- Status: in_progress/completed/blocked
- Changed: ...
- Validation: `command` → result summary
- Decisions: ...
- Next: ...

Host Portability Notes

• Codex: use normal file edits, terminal commands, plan updates, and git commits according to the current instructions.
• OpenClaw: keep the same project files; place this skill under an OpenClaw skills directory or workspace skills directory.
• Windows native: prefer PowerShell (.ps1) or Python (.py) startup/validation scripts; avoid Bash-only make.sh, sed, awk, chmod, or Unix path assumptions.
• Claude Code: legacy slash commands may be used if present, but do not require them for portability.