Borrowing it
Nothing to install: this file belongs to xiaoyuge886/aigc. 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/xiaoyuge886/aigc/main/.claude/skills/planning-with-files/SKILL.mdgit clone --depth 1 https://github.com/xiaoyuge886/aigcWrote 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/xiaoyuge886/aigc/planning-with-files)<a href="https://agentmods.dev/skills/xiaoyuge886/aigc/planning-with-files"><img src="https://agentmods.dev/badge/skills/xiaoyuge886/aigc/planning-with-files/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/xiaoyuge886/aigc/planning-with-files"><img src="https://agentmods.dev/badge/skills/xiaoyuge886/aigc/planning-with-files.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 | $0.00056 | $0.01792 |
| Opus 5 | $0.00028 | $0.00896 |
| Sonnet 5 | $0.00011 | $0.00358 |
| Haiku 4.5 | $0.00006 | $0.00179 |
Grade A, and why
planning-with-files 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.
This is a copy
89% identical to planning-with-files — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning with Files
Work like Manus: Use persistent markdown files as your "working memory on disk."
FIRST: Check for Previous Session (v2.2.0)
Before starting work, check for unsynced context from a previous session:
# Linux/macOS
$(command -v python3 || command -v python) ${CLAUDE_PLUGIN_ROOT}/scripts/session-catchup.py "$(pwd)"
# Windows PowerShell
& (Get-Command python -ErrorAction SilentlyContinue).Source "$env:USERPROFILE\.claude\skills\planning-with-files\scripts\session-catchup.py" (Get-Location)
If catchup report shows unsynced context:
- Run
git diff --statto see actual code changes - Read current planning files
- Update planning files based on catchup + git diff
- Then proceed with task
Important: Where Files Go
- Templates are in
${CLAUDE_PLUGIN_ROOT}/templates/ - Your planning files go in your project directory
| Location | What Goes There |
|---|---|
Skill directory (${CLAUDE_PLUGIN_ROOT}/) |
Templates, scripts, reference docs |
| Your project directory | task_plan.md, findings.md, progress.md |
Quick Start
Before ANY complex task:
- Create
task_plan.md— Use templates/task_plan.md as reference - Create
findings.md— Use templates/findings.md as reference - Create
progress.md— Use templates/progress.md as reference - Re-read plan before decisions — Refreshes goals in attention window
- Update after each phase — Mark complete, log errors
Note: Planning files go in your project root, not the skill installation folder.
The Core Pattern
Context Window = RAM (volatile, limited)
Filesystem = Disk (persistent, unlimited)
→ Anything important gets written to disk.
File Purposes
| File | Purpose | When to Update |
|---|---|---|
task_plan.md |
Phases, progress, decisions | After each phase |
findings.md |
Research, discoveries | After ANY discovery |
progress.md |
Session log, test results | Throughout session |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples.md 4.3 KB
- reference.md 7.9 KB
- scripts/check-complete.ps1 1.2 KB runs code
- scripts/check-complete.sh 1.1 KB runs code
- scripts/init-session.ps1 2.4 KB runs code
- scripts/init-session.sh 2.2 KB runs code
- scripts/session-catchup.py 7.3 KB runs code
- templates/findings.md 3.5 KB
- templates/progress.md 3.9 KB
- templates/task_plan.md 4.5 KB
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 · 231 lines · 56 tokens per session scan A 6342289ed4e8
planning-with-files is a skill published in the GitHub repository xiaoyuge886/aigc (197 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,792 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to planning-with-files, differing in 30 lines, and is treated as a copy.
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…