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 neuromechanist/research-skills --skill implementation-planninggit clone --depth 1 https://github.com/neuromechanist/research-skillsWrote 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/neuromechanist/research-skills/implementation-planning)<a href="https://agentmods.dev/skills/neuromechanist/research-skills/implementation-planning"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/implementation-planning/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/neuromechanist/research-skills/implementation-planning"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/implementation-planning.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 | $0.00082 | $0.01552 |
| Opus 5 | $0.00041 | $0.00776 |
| Sonnet 5 | $0.00016 | $0.00310 |
| Haiku 4.5 | $0.00008 | $0.00155 |
Grade A, and why
implementation-planning 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Planning
Plans that a different (or weaker) model could execute without re-deriving your reasoning, with success criteria fixed before any result is seen.
Model handoff
Use the strongest available model for problem framing, macro architecture,
irreversible decisions, and plan approval. In Codex this is Sol; in Claude it
is Fable when available, otherwise Opus. After the architecture is approved, a
bounded phase planner may expand it on the intermediate tier (Codex Terra;
Claude Sonnet when no design choice remains). Write the resulting brief so a
clear-task worker (Codex Luna or Claude Sonnet) can implement it without
reconstructing design intent. See agent-fanout for the full routing and
escalation policy.
Pick the register (by stakes, not size)
| Situation | Register |
|---|---|
| Hard to reverse after shipping; touches privacy, auth, what data leaves the device, or public contracts; strategic pivot | Heavyweight: full plan document, user approval gate before executing (plan mode where available). |
| Known engineering backlog; the work is clear, there is just a lot of it | Lightweight: tracked issues carrying the full spec (template below); no separate plan document. |
| Single ordinary change | No formal plan; the engineering-loop skill's step order is the plan. |
The dividing line is reversibility and blast radius, not effort. A large backlog of clear fixes needs no plan document; a small change to what an external partner receives does.
Heavyweight plan: required sections
# Plan: <title> (issue #N)
## Context
[The evidence forcing this work, with numbers. What the previous phase or
investigation proved.]
## What already exists to reuse (do not rebuild)
[Named files/functions. Every plan must check this before proposing new code.]
## Approach
[The design, and one paragraph on why this and not the leading alternative.]
## Files
[New and modified, explicit paths.]
## Decision gate (set BEFORE looking at results)
[Crisp pass/fail: "X ships as default if metric A >= threshold on every
case AND metric B does not regress; if it fails, honest conclusion + named
fallback. No adoption of a non-passing candidate."]
## Prerequisites (user actions)
[Only the human can do these: account registrations, credentials, hardware.]
## Agent budget
[How many subagents this plan will spawn, worst case; see agent-fanout cap.]
## Open judgment calls
[Every ambiguity you resolved unilaterally, as a flagged list the reviewer
can veto. Silence on ambiguity is a defect.]
## Verification
[How each deliverable is proven: commands, thresholds, who runs them. For a
behavior-preserving refactor, the FIRST deliverable is the pin/
characterization test captured against the original code, per the
engineering-loop skill.]
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 · 134 lines · 82 tokens per session scan A 3c2d8e134aaf
implementation-planning is a skill published in the GitHub repository neuromechanist/research-skills (45 stars, last pushed 8d ago), licensed BSD-3-Clause. It adds 82 tokens to every session and 1,552 once invoked, about $0.0004 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 2026-08-30.
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