Borrowing it
Nothing to install: this file belongs to NickZaitsev/ru-normalizr. 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/NickZaitsev/ru-normalizr/main/.claude/skills/make-plan/SKILL.mdgit clone --depth 1 https://github.com/NickZaitsev/ru-normalizrWrote 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/nickzaitsev/ru-normalizr/make-plan)<a href="https://agentmods.dev/skills/nickzaitsev/ru-normalizr/make-plan"><img src="https://agentmods.dev/badge/skills/nickzaitsev/ru-normalizr/make-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/nickzaitsev/ru-normalizr/make-plan"><img src="https://agentmods.dev/badge/skills/nickzaitsev/ru-normalizr/make-plan.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 | $0.00072 | $0.01006 |
| Opus 5 | $0.00036 | $0.00503 |
| Sonnet 5 | $0.00014 | $0.00201 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
make-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 8d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create an implementation plan for the requested work (feature, refactor, migration, fix, infra change — any task), written in English (it will be consumed by coding agents — Claude Code / Codex), and save it to docs/agents/plans/YYYY-MM-DD-<kebab-case-slug>.md (date = plan creation date, today; it never changes afterwards — the living state is the Status: line).
Languages: the plan FILE is written in English (it is consumed by coding agents). ALL communication with the user — clarifying questions, decision discussions, the final report — is in Russian.
Process
- Research first. Read the relevant code before writing anything. The plan must reference real files, functions, and line-level facts of THIS codebase — not generic advice. Verify every claim (paths, function names, existing patterns) against the actual code.
- Grill the user before writing the plan. Interview the user in Russian about every open product/architecture decision (storage, flags, UX, defaults, scope cuts) until you reach a shared understanding. Rules of the interview:
- Walk down each branch of the decision tree, resolving dependencies between decisions one by one — an answer often opens the next fork.
- For each question, provide your recommended answer with a short reason.
- If a question can be answered by exploring the codebase, explore the codebase instead of asking; the decisions themselves are always the user's.
- Do not start writing the plan until the interview is finished. Record the answers in the "Approved decisions" section (in English, like the rest of the plan).
- Write the plan (in English) using the structure below.
- Report the created file path and a short summary of the plan to the user in Russian.
Plan file structure
# Plan: <task title>
Status: plan, YYYY-MM-DD.
> Note for agents: this plan is a point-in-time snapshot — its "codebase facts" describe the code as of the date above and may be outdated. Do NOT treat it as current architecture docs; verify every fact against the actual code before relying on it.
## Context
Why this is needed; current behavior; constraints. Relevant external services/APIs with exact endpoints and costs if any.
## Approved decisions
Numbered list of decisions confirmed with the user (flags, defaults, style, storage, scope cuts).
## Key codebase facts
Bullet list of concrete facts an implementing agent needs: file paths, existing patterns to follow, schemas, auth conventions, gotchas. Only verified facts.
## Implementation
Numbered/step-by-step sections (0., 1., 2., …), each naming the exact files to create or modify and what goes in them. Include function signatures, env vars, error-handling and retry policy where relevant.
## Testing & verification
What tests to add/update, what to run, and how to verify end-to-end. Never call paid external APIs from tests.
## Out of scope
What is deliberately NOT done (prevents scope creep).
---
**Maintenance note (for the implementing agent):** when this plan is implemented, update the `Status:` line above, e.g. `Status: implemented YYYY-MM-DD in branch `feat/<name>``. If the plan changes during implementation, update the affected sections too — the plan must not lie about what was built.
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.
- 8d ago First seen · 68 lines · 72 tokens per session scan A c6178faedf82
make-plan is a skill published in the GitHub repository NickZaitsev/ru-normalizr (22 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 1,006 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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