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jzw6

Deep Planner

by Jzw6 · GitHub ↗ · v1.0.2 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install deep-planner
Description
A meta-skill that activates before complex tasks to enforce structured planning, step-by-step execution, and self-reflection. Works like Claude Code's TodoLi...
README (SKILL.md)

Deep Planner

A planning, execution, and self-reflection protocol for complex agent tasks.

How It Works

The TodoList exists in two places simultaneously:

  • In the reply — visible to the user, updated as steps complete
  • In .todolist/ — persisted to disk, recoverable if context is truncated

Both stay in sync. The file is the backup; the conversation is the live view.


Execution Protocol

Step 0 — Resume or Start Fresh

Before anything else, check for interrupted tasks:

Scan .todolist/ for any file with status: in-progress
  Found → Read it, show the user the current state, ask: continue or start new?
  Not found → Proceed to Step 1

Step 1 — Parse the Request (internal, not shown to user)

Before generating a plan, resolve the following internally:

□ What is the core goal? (one sentence)
□ What is the final deliverable? (report / code / content / action sequence / ...)
□ What tools or skills are needed? (web search / browser / code execution / ...)
□ Are there any ambiguities that would lead to completely different execution paths?
    Yes → Ask the user first, then generate the TodoList
    No  → Make reasonable assumptions; document them in the plan's Assumptions block

Only stop to ask about blockers — not details you can reasonably infer. Consolidate all questions into a single message. Do not ask one at a time.


Step 2 — Generate the TodoList

Once the request is clear, output the plan and write it to disk simultaneously.

Format shown in the reply:

## 📋 Task Plan: {short task name}

**Goal:** {one-line description of the final deliverable}
**Steps:** {N}

---

- [ ] 1. {step description} `{tool or skill}`
- [ ] 2. {step description} `{tool or skill}`
- [ ] 3. {step description} `internal reasoning`
- [ ] ...

> 💡 Assumptions: {any assumptions made without user confirmation}

---
Starting step 1 →

Write to disk at .todolist/YYYYMMDD-{task-name}.md using the file format below.


Step 3 — Execute Step by Step

Work through the TodoList in order. After each step completes:

  1. Open the next reply with a status update — mark the step [x]
  2. Briefly describe what was produced (1–2 sentences)
  3. Update the file to reflect the new state
  4. Proceed to the next step

Reply header format (concise):

✅ Step 2 done → Starting step 3...

Pause and ask the user when:

  • A step is marked ❓ (critical information is missing)
  • Reality diverges significantly from the plan and replanning is needed
  • A tool call fails and there are multiple recovery paths to choose from

Step 4 — Wrap Up

When all steps are done:

  1. Show the fully checked-off TodoList in the reply
  2. Run the post-completion reflection check (see below)
  3. Update the file: set status: completed — leave the file in place, do not delete

Confidence Levels

Only annotate when uncertain. Do not label every step.

Mark Meaning Action
(default) Confident, proceed Execute directly
⚠️ May involve inference or outdated info Execute, flag uncertainty in output
Critical info missing Pause, ask the user, then continue

Post-Completion Reflection

Run internally after all steps finish. Only surface issues that actually exist.

□ Was the core goal achieved?
□ Did I state anything I believed but didn't verify?  → Flag it
□ Are there ⚠️ steps whose conclusions need a caveat?
□ Is the deliverable complete with nothing skipped?

If issues exist, append to the final reply:

> ⚠️ Note: {X} is based on inference — consider verifying {specific thing}.

Anti-Hallucination Rules

These constraints are non-negotiable:

  1. No fabricated data — statistics, market figures, and research findings must have a source, or be explicitly labeled as estimates
  2. No fabricated citations — do not reference papers, reports, or news articles that may not exist
  3. Flag time-sensitive claims — anything described as "latest", "current", or "now" must note the knowledge cutoff date or recommend the user verify with a live search
  4. Be honest about limits — if a task is out of scope, say so clearly rather than producing low-confidence output

File Format

Path: .todolist/YYYYMMDD-{task-name}.md

# {Task Name}
Created: YYYY-MM-DD HH:MM
Status: in-progress | completed

## Goal
{One-line description of the final deliverable}

## TodoList
- [x] 1. {completed step}
- [x] 2. {completed step}
- [ ] 3. {current step} ← current
- [ ] 4. {upcoming step}

## Assumptions & Confirmations
- Assumed: {things inferred without user confirmation}
- Confirmed: {things the user explicitly answered}

## Progress
{done}/{total} steps completed

Task Type Templates

For common task types, load the matching template from the reference file:

Task Type Reference
Research & analysis references/task-types.md#research
Content creation (articles, posts) references/task-types.md#content
Technical design references/task-types.md#technical
Data processing references/task-types.md#data
Multi-skill pipelines references/task-types.md#multi-skill

Skill Coordination

This is a meta-skill. It plans and monitors; domain skills do the work.

User request
  → [deep-planner] parse + plan + write TodoList to disk
      → [web search / browser] information gathering steps
      → [domain skill A] content or processing steps
      → [domain skill B] platform-specific steps
  → [deep-planner] reflection + mark file as completed

Do not perform content generation, file operations, or network requests inside this skill. Delegate those to the appropriate tools and skills.

Usage Guidance
What to consider before installing/using this skill: - Understand and approve on-disk persistence: the skill will create and update files under a `.todolist/` directory (relative path). Confirm where that directory will be created in your environment and whether you want agent-written files kept by default. - Expect persistent state: completed task files are left in place. Review these files for sensitive content (plans may include inferred assumptions or snippets of user-provided context) and make a policy for deletion/archiving if desired. - Metadata omission: the registry did not declare the `.todolist/` config path. Ask the publisher (or your platform admin) to update the manifest to declare the path and clarify exact location and file format. - Limit exposure: if you are concerned, run the agent with restricted filesystem access (or in an ephemeral/sandboxed environment), or instruct the agent to persist plans only to a user-approved directory each time. - Review included templates: the references/task-types.md content is benign and helps the planner; still check that templates do not cause the agent to pull data from unexpected sources. - Provenance/privacy: the skill source and homepage are unknown. Prefer skills with transparent source repos or vendors. If you proceed, monitor the created files the first few times to ensure no unexpected data is written or exfiltrated. If you want this skill but not on-disk persistence, ask it (or request an updated manifest) to: use an explicit user-specified path, require explicit confirmation before writing files, or operate purely in-memory and output plans only in the conversation.
Capability Analysis
Type: OpenClaw Skill Name: deep-planner Version: 1.0.2 The deep-planner skill is a meta-skill designed to provide structured task planning and persistence for AI agents. It uses a local directory (.todolist/) to track progress and includes robust anti-hallucination and user-confirmation protocols. No indicators of data exfiltration, malicious execution, or unauthorized persistence were found in SKILL.md or the associated templates.
Capability Assessment
Purpose & Capability
The skill's stated purpose (planning/supervision) matches the instructions: generating plans, supervising multi-step tasks, and delegating to other skills. Persisting a todo list to disk is coherent for a planner. However, the metadata lists no required config paths while the instructions explicitly read/write a `.todolist/` path — an omission in the manifest (transparency gap).
Instruction Scope
SKILL.md explicitly instructs the agent to scan, read, and write files under a `.todolist/` directory, recover interrupted tasks, and leave completed files in place. Those filesystem operations are outside the registry metadata's declared scope (no config paths), and the instructions do not require user confirmation for creating/writing files nor clarify exact filesystem location (relative path ambiguity). This is a scope/information-disclosure concern because persisted plans could inadvertently include sensitive context.
Install Mechanism
Instruction-only skill with no install spec or external downloads — lowest installation risk. There is no code to execute or remote fetch indicated in the package.
Credentials
The skill requests no environment variables or credentials. There are no declared secrets requested, which is proportional to a planning/meta skill.
Persistence & Privilege
The skill will create and maintain persistent files (`.todolist/YYYYMMDD-{task-name}.md`) and expressly instructs leaving completed files in place. It does not request elevated privileges or 'always: true', and it does not modify other skills. Nonetheless, persistent on-disk state is a behavioral privilege the user should be comfortable with.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install deep-planner
  3. After installation, invoke the skill by name or use /deep-planner
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
No user-facing or behavioral changes in this version. - Internal version update; no modifications detected in documentation or logic. - All skill protocols and descriptions remain unchanged.
v1.0.1
- 全面本地化为中文,调整表述风格,更贴合中文用户理解和习惯 - 明确了强制激活的典型场景及关键词,补充实际适用边界 - 优化执行流程描述,所有关键步骤均以“Step X|说明”格式清晰拆解 - 精简部分说明,强调用户唯一确认点只在关键路径岔路 - 增加对任务类型模板的调用说明,更便于常见任务复用 - 原安全约束、防幻觉与文件规范未改变,仅表述与格式本地化
v1.0.0
- Initial release of deep-planner: a meta-skill for structured planning and supervision of complex, multi-step tasks. - Enforces clear task breakdown, step-by-step execution, and explicit user confirmation at decision points. - Generates and synchronizes a visible TodoList with persistent storage in `.todolist/`. - Delegates execution to other domain skills; does not produce final outputs itself. - Includes rules for self-reflection, confidence annotation, anti-hallucination, and transparent assumptions. - Supports template-based task planning for common scenarios (research, content, technical design, etc.).
Metadata
Slug deep-planner
Version 1.0.2
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 3
Frequently Asked Questions

What is Deep Planner?

A meta-skill that activates before complex tasks to enforce structured planning, step-by-step execution, and self-reflection. Works like Claude Code's TodoLi... It is an AI Agent Skill for Claude Code / OpenClaw, with 112 downloads so far.

How do I install Deep Planner?

Run "/install deep-planner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Deep Planner free?

Yes, Deep Planner is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Deep Planner support?

Deep Planner is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Deep Planner?

It is built and maintained by Jzw6 (@jzw6); the current version is v1.0.2.

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