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.
npx skills add adrielp/ai-engineering-harness --skill implement_plangit 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/skills/adrielp/ai-engineering-harness/implement_plan)<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/implement_plan"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/implement_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/skills/adrielp/ai-engineering-harness/implement_plan"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/implement_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 | $0.00018 | $0.00701 |
| Opus 5.5 | $0.00007 | $0.00280 |
| Sonnet 5.5 | $0.00004 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
implement_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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Plan
You are tasked with implementing an approved technical plan from thoughts/plans/. These plans contain phases with specific changes and success criteria.
Directory Structure:
thoughts/tickets/- Original feature requests and task descriptionsthoughts/plans/- Implementation plans (the files you'll be executing)thoughts/research/- Supporting research and investigation notes
Getting Started
When given a plan path:
- Read the plan completely and check for any existing checkmarks (- [x])
- Read the original ticket and all files mentioned in the plan
- Read files fully - never use limit/offset parameters
- Create a todo list to track your progress
- Start implementing if you understand what needs to be done
If no plan path provided, ask for one.
Implementation Philosophy
Plans are carefully designed, but reality can be messy. Your job is to:
- Follow the plan's intent while adapting to what you find
- Implement each phase fully before moving to the next
- Verify your work makes sense in the broader codebase context
- Update checkboxes in the plan as you complete sections
If you encounter a mismatch:
- STOP and present the issue clearly:
Issue in Phase [N]: Expected: [what the plan says] Found: [actual situation] Why this matters: [explanation] How should I proceed?
Verification Approach
After implementing a phase:
1. Run Success Criteria Checks
Use technology-appropriate commands:
Node.js/JavaScript: npm test, npm run lint, npm run build
Python: pytest, black --check ., mypy .
Go: go test ./..., golangci-lint run, go build
Rust: cargo test, cargo clippy, cargo build
Make-based: make test, make lint, make build
2. Fix Issues and Update Progress
- Address any failures before moving to the next phase
- Update checkboxes in the plan file using the Edit tool
- Update your TodoWrite list
If You Get Stuck
- Investigate First - Read all relevant code completely
- Use Sub-tasks for targeted help:
- codebase-locator: Find specific files
- codebase-analyzer: Understand how code works
- codebase-pattern-finder: Find similar implementations
- Present Issues Clearly - Don't guess, ask for clarification
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 · 90 lines · 18 tokens per session scan A 9e4622ccc42a
implement_plan is a skill published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 701 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…