Borrowing it
Nothing to install: this file belongs to raffertyuy/repo-of-repos. 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/raffertyuy/repo-of-repos/main/.agents/skills/create-plan/SKILL.mdgit clone --depth 1 https://github.com/raffertyuy/repo-of-reposWrote 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/raffertyuy/repo-of-repos/create-plan)<a href="https://agentmods.dev/skills/raffertyuy/repo-of-repos/create-plan"><img src="https://agentmods.dev/badge/skills/raffertyuy/repo-of-repos/create-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/raffertyuy/repo-of-repos/create-plan"><img src="https://agentmods.dev/badge/skills/raffertyuy/repo-of-repos/create-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 | $0.00021 | $0.00939 |
| Opus 5 | $0.00010 | $0.00469 |
| Sonnet 5 | $0.00004 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
create-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 12d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Plan
Create a detailed implementation plan before any coding begins. The plan is a markdown file with checkboxes that /implement-plan will later execute and update.
Arguments
The user provides a description of the work to be done. For example:
/create-plan Add Google OAuth to the login flow/create-plan Migrate user table to new schema/create-plan Update API rate limiting across all services
Steps
1. Determine Scope
Based on the user's description, determine which repos are involved:
- Read
repos/repos.yamlto get the list of repos/folders - Identify which repos are relevant to the work
- Ask the user to clarify scope only if it's genuinely ambiguous
2. Generate the Filename
Format: YYYYMMDD-<plan-name>.plan.md
- Use today's date as the prefix (e.g.,
20260409) - Create a short, descriptive plan name from the description:
- Lowercase, hyphen-separated
- 2-4 words maximum
- Example: "Add Google OAuth to login" ->
20260409-google-oauth.plan.md
3. Gather Context
Use the explorer agent pattern (read-only) to gather relevant context from the repos in scope:
- Read
repos/repos.mdfor an overview - For each repo in scope, identify and read:
- API endpoints or routes relevant to the work
- Type definitions, interfaces, or schemas involved
- Key files that will need changes
- Any existing tests for the affected code
- Distill this into compact summaries — don't paste entire files
4. Write the Plan File
Create _plans/YYYYMMDD-<plan-name>.plan.md with this structure:
---
status: draft
repos: [<repo-names>]
created: <today's date>
---
# Plan: <descriptive title>
## Context
<1-3 sentences: what needs to happen and why>
## Repo Context
<embedded API surfaces, types, schemas from relevant repos>
### <repo-name>
- <key files with brief descriptions>
- <exported types or endpoints relevant to this work>
- <architectural constraints or patterns to follow>
(repeat for each repo in scope)
## Steps
- [ ] Step 1: <brief title>
- **Task**: <detailed explanation of what to do>
- **Files**: <list of files to create or modify, with full paths>
- **Pseudocode**: <high-level pseudocode, NOT real code>
- [ ] Step 2: <brief title>
- **Task**: <detailed explanation>
- **Files**: <file list>
- **Pseudocode**: <pseudocode>
(continue for all steps)
- [ ] Step N: Validate
- **Task**: Run the application and tests, verify everything works
- **Files**: <test files>
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.
- 12d ago First seen · 123 lines · 21 tokens per session scan A 261ba55e0247
create-plan is a skill published in the GitHub repository raffertyuy/repo-of-repos (12 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 939 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…