Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/adrielp/ai-engineering-harness/validate_plan)<a href="https://agentmods.dev/commands/adrielp/ai-engineering-harness/validate_plan"><img src="https://agentmods.dev/badge/commands/adrielp/ai-engineering-harness/validate_plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/adrielp/ai-engineering-harness/validate_plan"><img src="https://agentmods.dev/badge/commands/adrielp/ai-engineering-harness/validate_plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00516 |
| Opus 5.5 | $0.00000 | $0.00206 |
| Sonnet 5.5 | $0.00000 | $0.00103 |
| Haiku 4.5 | $0.00000 | $0.00052 |
Grade A, and why
validate_plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validate Plan
You are tasked with validating that an implementation plan was correctly executed, verifying all success criteria and identifying any deviations or issues.
Initial Setup
When invoked:
- Determine context - Are you in an existing conversation or starting fresh?
- Locate the plan - Use provided path or search
thoughts/plans/ - Gather implementation evidence via git history
Validation Process
Step 1: Context Discovery
- Read the implementation plan completely
- Identify what should have changed
- Spawn parallel research tasks using:
- codebase-analyzer: Verify implementation details
- codebase-locator: Find modified files
- explore: Check test coverage
Step 2: Systematic Validation
For each phase:
- Check completion status - Look for checkmarks
- Run automated verification - Execute success criteria commands
- Assess manual criteria - List what needs manual testing
- Think about edge cases
Step 3: Generate Validation Report
## Validation Report: [Plan Name]
### Implementation Status
- Phase 1: [Name] - Fully implemented
- Phase 2: [Name] - Partially implemented (see issues)
### Automated Verification Results
- Build passes: `npm run build`
- Tests pass: `npm test`
- Linting issues: `npm run lint` (X warnings)
### Code Review Findings
#### Matches Plan:
- [What was implemented correctly]
#### Deviations from Plan:
- [What differs from plan]
#### Potential Issues:
- [Concerns discovered]
### Manual Testing Required:
1. [ ] Verify [feature] works
2. [ ] Test error states
### Recommendations:
- [Actionable next steps]
Relationship to Other Commands
Recommended workflow:
/create_plan- Create implementation plan/implement_plan- Execute the implementation/commit- Create atomic commits/validate_plan- Verify implementation correctness- Create PR
Key Principles
- Understand Before Validating - Read the entire plan first
- Be Objective and Critical - Validate functionality, not just presence
- Verify Comprehensively - Run all automated checks
- Communicate Clearly - Provide specific file references
- Think Long-term - Consider maintainability
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 80 lines · 0 tokens per session scan A 4c5636a84648
validate_plan is a command published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 516 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
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clarify
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specify
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converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.