Agent Skill
Planning With Files skill
planning-with-filesOthmanAdi/planning-with-files.gemini/skills/planning-with-files/SKILL.md
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Gemini lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. The session-end hook reports status only; it does not request continuation or run commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
always on
118 tokname + description
once used
2,514 tokSKILL.md body
bundled files
2012 scripts
repo stars
28kGitHub
Install the Planning With Files skill
Skills CLI (Claude Code, Codex, Cursor and others)
npx skills add https://github.com/OthmanAdi/planning-with-files --skill planning-with-files
Claude Code, by hand (all projects)
git clone --depth 1 https://github.com/OthmanAdi/planning-with-files.git /tmp/planning-with-files mkdir -p ~/.claude/skills cp -R /tmp/planning-with-files/.gemini/skills/planning-with-files ~/.claude/skills/planning-with-files
For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.
What it costs your context
Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.
| When | What loads | Cost |
|---|---|---|
| Every session | Name and description | 118 tok |
| When the agent uses it | The SKILL.md instructions | 2,514 tok |
| Only if opened | 20 bundled files (219 KB) | varies |
Until it is used, planning-with-files costs about 12× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 18 skills from OthmanAdi/planning-with-files would load 2,468 tokens of descriptions. Skills vs MCP study
Checks
- Ships scripts the agent can run. Read them before installing.
Checked 2026-10-22 · method
What's inside
The headings of its SKILL.md. Read the full file on GitHub.
- Planning with Files
- FIRST: Restore Project State
- Important: Where Files Go
- Quick Start
- The Core Pattern
- File Purposes
- Critical Rules
- 1. Create Plan First
- 2. The 2-Action Rule
- 3. Read Before Decide
- 4. Update After Act
- 5. Log ALL Errors
- 6. Never Repeat Failures
- 7. Continue After Completion
- The 3-Strike Error Protocol
- Read vs Write Decision Matrix
- The 5-Question Reboot Test
- When to Use This Pattern
- Templates
- Scripts
- List saved plans
- Parallel task workflow
- Advanced Topics
- Security Boundary
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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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