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deep-planner

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作者: admin | 来源: ClawHub
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deep-planner

# 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:** ```markdown ## 📋 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` ```markdown # {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.

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skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

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帮我安装 SkillHub 和 deep-planner-1775990581 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 deep-planner-1775990581 技能

通过命令行安装

skillhub install deep-planner-1775990581

下载 Zip 包

⬇ 下载 deep-planner v1.0.2

文件大小: 5.59 KB | 发布时间: 2026-4-13 10:00

v1.0.2 最新 2026-4-13 10:00
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.

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