Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/luizedupp/rememb/planning-with-filesnpx skills add LuizEduPP/Rememb --skill planning-with-filesgit clone --depth 1 https://github.com/LuizEduPP/RemembWrote 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/luizedupp/rememb/planning-with-files)<a href="https://agentmods.dev/skills/luizedupp/rememb/planning-with-files"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/planning-with-files.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.01861 |
| Opus 5 | $0.00025 | $0.00931 |
| Sonnet 5 | $0.00010 | $0.00372 |
| Haiku 4.5 | $0.00005 | $0.00186 |
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 5d 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
78% identical to planning-with-files — 113 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 — 244 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."
Overview
Use persistent project files to store task state, discoveries, and progress so long-running work survives context limits and session resets.
Optional Host Integration
Some AI hosts support hooks, tool restrictions, or lifecycle automation. Treat those as optional enhancements, not required behavior. The core workflow in this document should still work in any IDE, editor, CLI, or chat app.
First: Restore Context
Before doing anything else, check if planning files exist and read them:
- If
task_plan.mdexists, readtask_plan.md,progress.md, andfindings.mdimmediately. - Then check for unsynced context from a previous session:
# Linux/macOS
$(command -v python3 || command -v python) <skill-root>/scripts/session-catchup.py "$(pwd)"
# Windows PowerShell
& (Get-Command python -ErrorAction SilentlyContinue).Source "<skill-root>/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
Where Files Go
- Templates are in
<skill-root>/templates/ - Your planning files go in your project directory
| Location | What Goes There |
|---|---|
Skill directory (<skill-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
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 4.4 KB
- reference.md 7.9 KB
- scripts/check-complete.ps1 1.8 KB runs code
- scripts/check-complete.sh 1.7 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 15 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.
- 5d ago First seen · 244 lines · 50 tokens per session scan A 34b5d25aa646
planning-with-files is a skill published in the GitHub repository LuizEduPP/Rememb (4 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 1,861 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to planning-with-files, differing in 113 lines, and is treated as a copy.
Other skills, from other repositories
Effective Memory
The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
honcho-integration
Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
honcho-memory
Concepts and strategy for using a connected Honcho as persistent memory of the user — the recall/record loop and session and peer design. Start here to understand how Honcho memory works, then connect — via a first-class integration for your environment if one exists (preferred), or raw MCP tools (covered here) or the…
verify
Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.