Agent Skill
Planning With Files skill
planning-with-filesOthmanAdi/planning-with-files.kiro/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; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no 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.
always on
96 tokname + description
once used
1,839 tokSKILL.md body
bundled files
125 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/.kiro/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 | 96 tok |
| When the agent uses it | The SKILL.md instructions | 1,839 tok |
| Only if opened | 12 bundled files (22 KB) | varies |
Until it is used, planning-with-files costs about 14× 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.
Pre-approved tools (allowed-tools): shell, read, write. The agent can use these without asking while the skill is active.
Checked 2026-10-22 · method
What's inside
The headings of its SKILL.md. Read the full file on GitHub.
- Planning with Files (Kiro)
- STEP 0 — Bootstrap (once per workspace)
- STEP 1 — Persistent reminder (after skill activation)
- STEP 2 — Read plan every turn (while active)
- STEP 3: Project-file catchup (after a long gap or suspected drift)
- Optional — Phase checklist
- 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
- Scripts
- Advanced Topics
- Security Boundary
- Anti-Patterns
- When to use
More skills from OthmanAdi/planning-with-files
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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; 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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