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 agentmods add skills/robhowley/py-pit-skills/implementation-protocolnpx skills add robhowley/py-pit-skills --skill implementation-protocolgit clone --depth 1 https://github.com/robhowley/py-pit-skillsWhat 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 | $0.00116 | $0.02156 |
| Opus 5 | $0.00058 | $0.01078 |
| Sonnet 5 | $0.00023 | $0.00431 |
| Haiku 4.5 | $0.00012 | $0.00216 |
Grade A, and why
implementation-protocol 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 2d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
implementation-protocol
This skill orchestrates how you turn a feature request into a verified, reviewable change. It is not about writing code — it is about enforcing a contract between intent, change, and proof.
The discipline here exists because the most common failure mode for AI-driven implementation is overbuilding: hallucinated architecture, speculative abstractions, scope creep, and "looks correct" completion without proof. This skill prevents all of that by anchoring every action to observable behavior and failing signals.
When to use
- User asks you to implement a feature, endpoint, capability, or behavior
- User describes something they want working and expects you to build it
- Any non-trivial code change (more than a one-line fix)
When not to use
- Pure refactoring with no behavior change (use code-quality or simplify)
- Scaffolding a new project from scratch (use fastapi-init)
- One-line bug fixes where the fix is obvious
Phase 1 — Define the behavior
Never start with implementation. Start by defining what success looks like in concrete, observable terms.
Before touching any code, establish:
- Inputs and outputs — what goes in, what comes out. Be specific: request shape, response shape, status codes, return types.
- API contract — if this is an endpoint, what's the route, method, request/response schema?
- Side effects — what changes in the world? DB writes, log entries, events emitted, files created.
- Error cases — what happens when inputs are invalid, dependencies are unavailable, or preconditions aren't met?
The implementation ledger
Before proceeding, produce a short ledger that makes the rest of the process auditable. Write it in conversation (or in .dev/todos/<feature>.md if the user prefers a file). The ledger has four sections:
## Behaviors
- [ ] B1: POST /items returns 201 with created item
- [ ] B2: POST /items with missing name returns 422
- ...
## Files to change
- src/api/routes/items.py (new router)
- src/models/item.py (new model)
- ...
## Tests to add/update
- tests/test_items.py::test_create_item (B1)
- tests/test_items.py::test_create_item_missing_name (B2)
- ...
## Proof commands
- uv run pytest tests/test_items.py
- uv run pytest tests/ (full suite)
- uv run ruff check .
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.
- 2d ago First seen · 194 lines · 116 tokens per session scan A 88d9bf0d0345
implementation-protocol is a skill published in the GitHub repository robhowley/py-pit-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 116 tokens to every session and 2,156 once invoked, about $0.0006 per session on Opus 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 2026-08-31.
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