project-health

📁 jezweb/claude-skills 📅 14 days ago
152
总安装量
152
周安装量
#1663
全站排名
安装命令
npx skills add https://github.com/jezweb/claude-skills --skill project-health

Agent 安装分布

claude-code 122
gemini-cli 102
opencode 101
cursor 95
codex 89
replit 85

Skill 文档

Project Health: AI-Agent Readiness Auditing

Status: Active Updated: 2026-01-30 Focus: Ensuring documentation and workflows are executable by AI agents

Overview

This skill evaluates project health from an AI-agent perspective – not just whether docs are well-written for humans, but whether future Claude Code sessions can:

  1. Understand the documentation without ambiguity
  2. Execute workflows by following instructions literally
  3. Resume work effectively with proper context handoff

When to Use

  • Before handing off a project to another AI session
  • When onboarding AI agents to contribute to a codebase
  • After major refactors to ensure docs are still AI-executable
  • When workflows fail because agents “didn’t understand”
  • Periodic health checks for AI-maintained projects

Agent Selection Guide

Situation Use Agent Why
“Will another Claude session understand this?” context-auditor Checks for ambiguous references, implicit knowledge, incomplete examples
“Will this workflow actually execute?” workflow-validator Verifies steps are discrete, ordered, and include verification
“Can a new session pick up where I left off?” handoff-checker Validates SESSION.md, phase tracking, context preservation
Full project health audit All three Comprehensive AI-readiness assessment

Key Principles

1. Literal Interpretation

AI agents follow instructions literally. Documentation that works for humans (who fill in gaps) may fail for agents.

Human-friendly (ambiguous):

“Update the config file with your settings”

AI-friendly (explicit):

“Edit wrangler.jsonc and set account_id to your Cloudflare account ID (find it at dash.cloudflare.com → Overview → Account ID)”

2. Explicit Over Implicit

Never assume the agent knows:

  • Which file you mean
  • What “obvious” next steps are
  • Environment state or prerequisites
  • What success looks like

3. Verification at Every Step

Agents can’t tell if something “feels right”. Include verification:

  • Expected output after each command
  • How to check if a step succeeded
  • What to do if it failed

Agents

context-auditor

Purpose: Evaluate AI-readability of documentation

Checks:

  • Instructions use imperative verbs (actionable)
  • File paths are explicit (not “the config file”)
  • Success criteria are measurable
  • No ambiguous references (“that thing”, “as discussed”)
  • Code examples are complete (not fragments)
  • Dependencies/prerequisites stated explicitly
  • Error handling documented

Output: AI-Readability Score (0-100) with specific issues

workflow-validator

Purpose: Verify processes are executable when followed literally

Checks:

  • Steps are discrete and ordered
  • Each step has clear input/output
  • No implicit knowledge required
  • Environment assumptions documented
  • Verification step after each action
  • Failure modes and recovery documented
  • No “obvious” steps omitted

Output: Executability Score (0-100) with step-by-step analysis

handoff-checker

Purpose: Ensure session continuity for multi-session work

Checks:

  • SESSION.md or equivalent exists
  • Current phase/status clear
  • Next actions documented
  • Blockers/decisions needed listed
  • Context for future sessions preserved
  • Git checkpoint pattern in use
  • Architecture decisions documented with rationale

Output: Handoff Quality Score (0-100) with continuity gaps

Templates

AI-Readable Documentation Template

See templates/AI_READABLE_DOC.md for a template that ensures AI-readability.

Key sections:

  • Prerequisites (explicit environment/state requirements)
  • Steps (numbered, discrete, with verification)
  • Expected Output (what success looks like)
  • Troubleshooting (common failures and fixes)

Handoff Checklist

See templates/HANDOFF_CHECKLIST.md for ensuring clean session handoffs.

Anti-Patterns

1. “See Above” References

# Bad
As mentioned above, configure the database.

# Good
Configure the database by running:
`npx wrangler d1 create my-db`

2. Implicit File Paths

# Bad
Update the config with your API key.

# Good
Add your API key to `.dev.vars`:

API_KEY=your-key-here

3. Missing Verification

# Bad
Run the migration.

# Good
Run the migration:
`npx wrangler d1 migrations apply my-db --local`

Verify with:
`npx wrangler d1 execute my-db --local --command "SELECT name FROM sqlite_master WHERE type='table'"`

Expected output: Should show your table names.

4. Assumed Context

# Bad
Now deploy (you know the drill).

# Good
Deploy to production:
`npx wrangler deploy`

Verify deployment at: https://your-worker.your-subdomain.workers.dev

Relationship to Other Tools

Tool Focus Audience
project-docs-auditor Traditional doc quality (links, freshness, structure) Human readers
project-health skill AI-agent readiness (executability, clarity, handoff) Claude sessions
docs-workflow skill Creating/managing specific doc files Both

Quick Start

  1. Full audit: “Run all project-health agents on this repo”
  2. Check one aspect: “Use context-auditor to check AI-readability”
  3. Before handoff: “Use handoff-checker before I end this session”

Success Metrics

A healthy project scores:

  • Context Auditor: 80+ (AI can understand without clarification)
  • Workflow Validator: 90+ (steps execute literally without failure)
  • Handoff Checker: 85+ (new session can resume immediately)

Projects below these thresholds have documentation debt that will slow future AI sessions.