According to AlphaMoat, as of 2026-09, planning-with-files has 1,589 downloads and 0 stars — ranked #4,709 of 5,078 AI skills overall, and #915 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 37 | 1.3K | - | - |
| SkillHub ↗ | 106 | 1.6K | - | — |
Platforms count differently and update at different times.
The primary recommends recording a goal, phases, findings, and progress in files so work can resume across phases or sessions. Its initialization script processes the task description; the accompanying card warns unsafe template substitution could turn crafted task text into local command execution. Review the script first, use it only in trusted workspaces, and do not pass untrusted input into an initializer that has not been made data-safe.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 1.6K | — | - |
| 2026-08 | 1.3K | +10.2% | - |
| 2026-07 | 1.2K | +20.8% | - |
| 2026-06 | 997 | +13.3% | - |
| 2026-05 | 880 | — | - |
| 2026-03 | 548 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
name: planning-with-files
description: "This skill should be used when the user asks to start a complex task, research project, multi-step implementation, or any task expected to require more than 5 tool calls. Triggers: 'plan this', 'start planning', 'help me build', 'research and implement', 'complex task', '/plan'."
metadata: { "openclaw": { "emoji": "📋", "version": "1.0.0", "author": "shannon/openclaw-port" } }
---
# Planning with Files
Manus-style persistent markdown planning. Filesystem = long-term memory. Context window = working RAM.
## Core Principle
```
Context Window = RAM (volatile, limited)
Filesystem = Disk (persistent, unlimited)
→ Anything important MUST be written to disk.
```
## When to Use
- Multi-step tasks (3+ phases or 5+ tool calls)
- Research + implementation tasks
- Tasks that span multiple sessions
- Any task where goal-drift is a risk
Skip for: single-file edits, quick lookups, simple questions.
## Session Start Protocol
**ALWAYS do this first — no exceptions:**
1. Run the init script to create the three planning files:
```
exec: bash {baseDir}/scripts/init-session.sh "<task description>"
```
2. Fill in the `## Goal` section in `task_plan.md` with the full task description.
3. Break the task into 3–7 phases and add them under `## Phases`.
4. Confirm files exist before proceeding.
If exec is not available, create the three files manually using the templates in `{baseDir}/templates/`.
## The Three Files
### task_plan.md — Goal tracker (MOST IMPORTANT)
- Created at session start, never deleted
- Re-read before every major decision
- Update when: phase completes, decision made, error encountered
### findings.md — Knowledge store
- Research results, discoveries, technical decisions
- **2-Action Rule**: after every 2 view/search/fetch operations → update findings.md immediately
### progress.md — Session log
- Actions taken, test results, errors encountered
- Append-only; never rewrite history
## Work Loop
```
BEFORE every major action:
→ Re-read task_plan.md (keeps goal in attention)
EVERY 2 view/search/fetch operations:
→ Save key findings to findings.md NOW
AFTER completing a phase:
→ Update phase status in task_plan.md to "complete"
→ Append phase summary to progress.md
WHEN an error occurs:
→ Log it in task_plan.md under ## Errors table
→ Never repeat the same failing action
BEFORE stopping:
→ Run: exec bash {baseDir}/scripts/check-complete.sh
→ Only stop if all phases show "complete"
```
## File Update Rules
| Trigger | File to update | Section |
|---------|---------------|---------|
| Start task | task_plan.md | Goal + Phases |
| 2nd research op | findings.md | Key Findings |
| Make decision | findings.md | Technical Decisions |
| Phase done | task_plan.md | Phase status → complete |
| Phase done | progress.md | Phase summary |
| Error | task_plan.md | Errors table |
| Test result | progress.md | Test Results |
## Error Recovery Protocol
```
ATTEMPT 1: Diagnose & Fix
→ Read error carefully → identify root cause → targeted fix
ATTEMPT 2: Alternative Approach
→ Same error? Try different tool/library/method
→ NEVER repeat the exact same failing action
ATTEMPT 3: Broader Rethink
→ Question assumptions → search for solutions → update plan
AFTER 3 FAILURES:
→ Escalate to user: explain what was tried, share the error, ask for guidance
```
## Completion Verification
Before telling the user the task is done:
1. Run `exec bash {baseDir}/scripts/check-complete.sh`
2. If any phase is NOT "complete" → continue working
3. Only deliver result when all phases are checked off
## Session Recovery
If context was cleared (e.g., `/clear`), resume by:
1. Reading `task_plan.md` to restore goal and phase status
2. Reading `progress.md` to see what was done
3. Reading `findings.md` to restore research context
4. Continuing from the first incomplete phase
## Key Principles
- **Plan is Required**: Never start a complex task without `task_plan.md`
- **Files are Memory**: Context is volatile. Filesystem is permanent. Write important things down.
- **Never Repeat Failures**: Track what was tried. Mutate the approach.
- **Error Recovery = Agentic Behavior**: How you handle failures defines task quality.
- **Re-read Before Deciding**: Attention manipulation — keep the goal visible.
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
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A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.