Agent skill
planning-with-files
Implements file-based planning for complex multi-step tasks. Creates task_plan.md, findings.md, and progress.md as persistent working memory. Use when starting tasks requiring >5 tool calls, multi-phase projects, research, or any work where losing track of goals and progress would be costly.
Install this agent skill to your Project
npx add-skill https://github.com/trailofbits/skills-curated/tree/main/plugins/planning-with-files/skills/planning-with-files
SKILL.md
Planning with Files
Use persistent markdown files as working memory on disk.
Quick Start
- Create planning files -- use
/planor create manually from templates - Create
task_plan.mdwith goal, phases, and key questions - Create
findings.mdfor research and decisions - Create
progress.mdfor session logging - Re-read the plan before decisions -- refreshes goals in attention
- Update after each phase -- mark status, log errors
When to Use
- Multi-step tasks (3+ phases)
- Research projects requiring many searches
- Building or creating projects with multiple files
- Tasks spanning many tool calls (>10)
- Any work where losing track of goals would be costly
- Tasks that may span multiple sessions
When NOT to Use
- Simple questions or quick lookups
- Single-file edits with obvious scope
- Tasks completable in under 5 tool calls
- Conversational exchanges without implementation
Core Pattern
Context Window = RAM (volatile, limited)
Filesystem = Disk (persistent, unlimited)
Anything important gets written to disk.
After many tool calls, the original goal drifts out of the attention
window. Reading task_plan.md brings it back. This is the single
most important pattern in file-based planning.
File Purposes
| File | Purpose | When to Update |
|---|---|---|
task_plan.md |
Phases, progress, decisions | After each phase completes |
findings.md |
Research, discoveries, decisions | After ANY discovery |
progress.md |
Session log, test results | Throughout the session |
All three files go in the project root, not the plugin directory.
Critical Rules
1. Create Plan First
Never start a complex task without task_plan.md. This is
non-negotiable. The plan is your persistent memory.
2. The 2-Action Rule
After every 2 search, browse, or read operations, immediately save
key findings to findings.md. Multimodal content (images, browser
results, PDF contents) does not persist in context -- capture it as
text before it is lost.
3. Read Before Decide
Before any major decision, re-read task_plan.md. This pushes
goals and context back into the recent attention window, counteracting
the "lost in the middle" effect that occurs after ~50 tool calls.
[Original goal -- far away in context, forgotten]
...many tool calls...
[Recently read task_plan.md -- gets ATTENTION]
→ Now make the decision with goals fresh in context
4. Update After Act
After completing any phase:
- Mark phase status:
in_progress->complete - Log any errors encountered in the Errors table
- Note files created or modified in
progress.md
5. Log ALL Errors
Every error goes in task_plan.md. Include the attempt number and
resolution. This builds knowledge and prevents repeating failures.
## Errors Encountered
| Error | Attempt | Resolution |
|-------|---------|------------|
| FileNotFoundError | 1 | Created default config |
| API timeout | 2 | Added retry logic |
6. Never Repeat Failures
If an action failed, the next action must be different. Track what you tried and mutate the approach.
if action_failed:
next_action != same_action
3-Strike Error Protocol
ATTEMPT 1: Diagnose & Fix
-> Read error carefully
-> Identify root cause
-> Apply targeted fix
ATTEMPT 2: Alternative Approach
-> Same error? Try a different method
-> Different tool? Different library?
-> NEVER repeat the exact same failing action
ATTEMPT 3: Broader Rethink
-> Question assumptions
-> Search for solutions
-> Consider updating the plan
AFTER 3 FAILURES: Escalate to User
-> Explain what you tried (with attempt log)
-> Share the specific error
-> Ask for guidance
Read vs Write Decision Matrix
| Situation | Action | Reason |
|---|---|---|
| Just wrote a file | Don't read it | Content still in context |
| Viewed image/PDF | Write findings NOW | Multimodal content doesn't persist |
| Browser returned data | Write to file | Screenshots don't persist |
| Starting new phase | Read plan/findings | Re-orient if context is stale |
| Error occurred | Read relevant file | Need current state to fix |
| Resuming after gap | Read all planning files | Recover full state |
5-Question Reboot Test
If you can answer these from your planning files, context is solid:
| Question | Answer Source |
|---|---|
| Where am I? | Current phase in task_plan.md |
| Where am I going? | Remaining phases |
| What's the goal? | Goal statement in plan |
| What have I learned? | findings.md |
| What have I done? | progress.md |
Anti-Patterns
| Don't | Do Instead |
|---|---|
| State goals once and forget | Re-read plan before decisions |
| Hide errors and retry silently | Log every error to plan file |
| Stuff everything in context | Store large content in files |
| Start executing immediately | Create plan file FIRST |
| Repeat failed actions | Track attempts, mutate approach |
| Create files in plugin directory | Create files in project root |
References
- Templates -- starter templates for all three planning files
- Principles -- context engineering principles behind this approach
- Examples -- concrete examples and error recovery patterns
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
openai-jupyter-notebook
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook. Originally from OpenAI's curated skills catalog.
openai-pdf
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction. Originally from OpenAI's curated skills catalog.
openai-security-best-practices
Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks. Originally from OpenAI's curated skills catalog.
openai-netlify-deploy
Deploy web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including preview and production deploys. Originally from OpenAI's curated skills catalog.
ffuf-web-fuzzing
Expert guidance for ffuf web fuzzing during authorized penetration testing. Covers directory discovery, subdomain enumeration, parameter fuzzing, authenticated fuzzing with raw requests, auto-calibration, and result analysis. Use when running ffuf scans, analyzing ffuf output, or building fuzzing strategies for web targets.
openai-gh-fix-ci
Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL. Originally from OpenAI's curated skills catalog.
Didn't find tool you were looking for?