project-health

name

project-health

description

AI-agent readiness auditing for project documentation and workflows. Evaluates whether future Claude Code sessions can understand docs, execute workflows literally, and resume work effectively. Use when onboarding AI agents to a project or ensuring context continuity. Includes three specialized agents: context-auditor (AI-readability), workflow-validator (process executability), handoff-checker (session continuity). Use PROACTIVELY before handing off projects to other AI sessions or team members.

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.