harness-status
Restore state, health check, determine current phase, suggest next step
Skill by zxdxjtu
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/zxdxjtu-harness-status
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/harness-status
What's inside
- Skill name
harness-status- Description
- Restore state, health check, determine current phase, suggest next step
- License
- MIT
- Instructions
- 81 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Harness Status Recover context and determine the current development phase. ## Step 1: Check Harness State ```bash test -d .harness && echo "HARNESS_EXISTS" || echo "NO_HARNESS" ``` If no `.harness/` directory exists, tell the user to start with `/proposal`. ## Step 1.5: Check Configuration ```bash test -f .harness/config.yaml && echo "CONFIG_EXISTS" || echo "NO_CONFIG" ``` If no config exists, suggest running `/harness-init` first for optimal experience. ## Step 2: Read State Files 1. List all specs: `ls .harness/specs/ 2>/dev/null` 2. Read config: Read `.harness/config.yaml` (if exists) for project context 3. Read tasks: Read `.harness/tasks.md` (if exists) 4. Read progress (last 50 lines): Read `.harness/progress.md` offset from end 5. Check sprint loop: Read `.harness/sprint-loop.md` (if exists) 6. Read pitfalls: Read `.harness/pitfalls.md` (if exists) — lightweight project pitfalls 7. Check invariants: Read `.harness/invariants.md` (if exists) for learned constraints 8. Check traces: Count events in `.harness/traces/events.jsonl` (if exists) 9. Recent git history: `git log --oneline -20` ## Step 3: Output Summary For each feature, report: - Spec status (draft/approved/tests-aligned) - Test status (how many RED/GREEN) - Task status (pending/in_progress/completed/failed counts per wave) - Sprint loop status (active/inactive, iteration count) - Pitfalls count (N known pitfalls recorded) — list them if ≤ 10, otherwise show count + categories ## Step 4: Determine Phase | State | Current Phase | Next Step | |-------|--------------|-----------| | No specs | Phase 1 | `/proposal` | | Spec approved, no tests | Phase 2 | `/tdd-align FXXX` | | Tests exist, no task decomposition | Pre-Phase 3 | `/decompose FXXX` | | Tasks decomposed, pending tasks exist | Phase 3 | `/sprint FXXX` | | All tasks done, not verified | Phase 4 | `/verify FXXX` | | Feature verified | Complete | Start next feature | ## Step 5: Health Check - Dependencies installed? (`npm ci` / `pip install` / etc.) - Build passes? - Core tests green? - If anything fails → **fix regression first** ## Step 6: Evolution Suggestions Based on project state, suggest evolution actions: | Condition | Suggestion | |-----------|-----------| | 3+ features completed, evolution log empty | "建议运行 `/harness evolve` 分析失败模式、发现可封装的重复工作" | | No config.yaml | "建议运行 `/harness init` 适配项目规范" | | Sprint had failures | "建议运行 `/harness evolve` 分析失败原因" | | Eval scores trending down | "建议运行 `/adversarial-review` 深度审查" | ## Step 7: Output Plan State clearly: - Current phase - Project configuration status (initialized? norms? invariants?) - Specific goal for this session - Which tasks/waves to work on - Verification method - Evolution status (invariants count, last evolution date)
From zxdxjtu/harness, MIT.
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tdd-align
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entropy-clean
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Post-wave entropy cleanup — remove debug artifacts, enforce style, ensure git hygiene
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eval-fix
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Adversarial fix loop: read eval report → fix gaps → re-evaluate → repeat until convergence
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evaluate
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Adversarial evaluation: compare dev product against reference baseline using Playwright MCP
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harness-init
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Initialize Harness for your project — learn repo, team norms, generate customized framework
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proposal
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Start a new feature proposal — guided spec + design generation
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sdd-init
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Initialize SDD harness for a project — scan codebase, generate config, full-spec, full-design
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spec-review
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Multi-person spec review with line-by-line comments and resolution tracking
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steward
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Module stewardship guardian — check code changes against AGENT.md boundaries and contracts
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archive
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Phase 3: merge spec/design deltas, code review, atomic commits, push, and trigger self-evolution
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baseline
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Capture baseline of a reference product using Playwright MCP for clone/replicate scenarios
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help
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Explain Harness plugin and available commands
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Signals
- Stars
- 8
- Forks
- 3
- Open issues
- 1
- Last commit (this folder)
- Apr 12, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/zxdxjtu/harness
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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- 0.466score
eval-fix
zxdxjtu
Adversarial fix loop: read eval report → fix gaps → re-evaluate → repeat until convergence
SkillDFreshness: D★ 8Claude Code +9 - 0.466score
evaluate
zxdxjtu
Adversarial evaluation: compare dev product against reference baseline using Playwright MCP
SkillDFreshness: D★ 8Claude Code +9 - 0.466score
baseline
zxdxjtu
Capture baseline of a reference product using Playwright MCP for clone/replicate scenarios
SkillDFreshness: D★ 8Claude Code +9 - 0.466score
entropy-clean
zxdxjtu
Post-wave entropy cleanup — remove debug artifacts, enforce style, ensure git hygiene
SkillDFreshness: D★ 8Claude Code +9 - 0.466score
archive
zxdxjtu
Phase 3: merge spec/design deltas, code review, atomic commits, push, and trigger self-evolution
SkillDFreshness: D★ 8Claude Code +9 - 0.466score
harness-init
zxdxjtu
Initialize Harness for your project — learn repo, team norms, generate customized framework
SkillDFreshness: D★ 8Claude Code +9
Done for you
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Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
harness-status skill. Agent Mart. https://agentmart.dev/skills/zxdxjtu-harness-status (last updated 2026-10-24)