Borrowing it
Nothing to install: this file belongs to strikersam/autonomous-ai-agency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/strikersam/autonomous-ai-agency/master/.agents/skills/implementation-planner/SKILL.mdgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/implementation-planner)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/implementation-planner"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/implementation-planner/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/strikersam/autonomous-ai-agency/implementation-planner"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/implementation-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.00667 |
| Opus 5 | $0.00019 | $0.00333 |
| Sonnet 5 | $0.00008 | $0.00133 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
implementation-planner 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 11d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: implementation-planner
When to Use
Use this skill before writing any code when:
- The change touches more than 2 files
- A new endpoint, agent capability, or routing behaviour is being added
- The approach is not obvious from the existing code structure
- You are unsure whether a change will break existing tests
Instructions
Step 1 — Understand the current state
- Read the relevant module
AGENTS.mdif one exists. - Run
pytest -xto confirm the baseline is green. - Read the key files you expect to touch.
Step 2 — Write the plan
Produce a plan in this structure:
## Goal
One sentence: what does this change accomplish?
## Approach
2-3 sentences: the technical approach chosen and why.
## Files to change
- `path/to/file.py` — what changes and why
- `tests/test_something.py` — new or updated tests
## Files to read first
- `path/to/related.py` — to understand the interface
## Risks
- Any behaviour that might regress
- Any risky module being touched (triggers risky-module-review skill)
## Acceptance checks
- [ ] pytest -x passes
- [ ] changelog entry added
- [ ] no hardcoded secrets
- [ ] new tests cover the new behaviour
Step 3 — Get implicit approval before coding
If the plan involves a risky module (auth, key store, agent tools), invoke the
risky-module-review skill before proceeding.
Step 4 — Implement
Follow the plan step by step. Update the plan if reality diverges from the
written plan (annotate with # REVISED: comments).
Step 5 — Verify
After implementing:
- Run
pytest -x. - If tests fail, fix and re-run before reporting done.
- Update
docs/changelog.md. - Update
.Codex/state/agent-state.jsonwith completed steps.
Acceptance Checks
- Plan was written before implementation
- All listed files were changed
- All tests pass
- Changelog updated
- No risky module changed without risky-module-review
Failure / Retry Behaviour
If a step fails (syntax error, test failure, unexpected interface):
- Do NOT silently swallow the failure.
- Annotate the plan with the failure.
- Diagnose before retrying — read the error carefully.
- If blocked, write the blocker to
.Codex/state/NEXT_ACTION.mdand stop cleanly.
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.
- 11d ago First seen · 94 lines · 38 tokens per session scan A bc843d5b7e3d
implementation-planner is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 667 once invoked, about $0.0002 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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