长任务接力

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

一个 session 聊太长会变蠢?遇到大活一个session干不完怎么办?长任务接力让你把长任务切成一段段干净的对话来做:每次收工留下清楚的交接单(做了啥、定了啥、下一步干啥),下次开工照着接上就能干,不用从头讲、不丢上下文、不重来。用它就把它切成几段,每段都清爽。

一句话选型结论

按资料字段整理

长任务接力是AI 工具类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类AI 工具 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_f6d0b8c5/session-continuity
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

AI 工具
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_f6d0b8c5仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791622303
正文体量10.6 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
组合多个 AI 能力

把写作、检索、总结串成一条流程。

搭工作流

把固定流程做成可重复调用的步骤。

做提示词与评测

整理提示词并对比输出效果。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在AI 工具场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
长任务接力当前 未标注权限 未标注 未标注 未标注 有
执行计划 只读 GitHub 仓库 151.5 万 Codex、Claude、Cursor 有
Playwright MCP 测试 浏览器控制 ClawHub 市场 136.5 万 OpenClaw 有
完成前的验证 未标注权限 GitHub 仓库 131.9 万 Codex、Claude、Cursor 有
子 Agent 驱动开发 只读 GitHub 仓库 131.2 万 Codex、Claude、Cursor 有
前端设计 执行命令 GitHub 仓库 125.6 万 Codex、Claude、Cursor 有

SKILL.md 全文

10.6 KB
模型实际读到的说明书原文,来自技能仓库

Session Continuity

Save your place. Resume exactly where you left off — even days later.


Core Position

Most agents lose all state when sessions die. This skill treats checkpoints as
first-class artifacts: written proactively before context is at risk, read
automatically on session start, consumed to restore full working context.

This is not "note-taking." A checkpoint is a machine-readable resume signal.
If it can't restore the exact next action with enough context to execute it,
the checkpoint is incomplete.


Modes

/checkpoint save <name>

Save current task state to a named checkpoint file. Use before closing any
session with unfinished work, before manual reset, approaching context limits,
or when the task spans multiple sessions.

Trigger when:
- About to close a session with active work
- Context >70% and work is incomplete
- Multi-day task where tomorrow you need to continue
- After a milestone, to mark the resume point

Do not use for: trivial one-shot tasks that complete in a single reply.

/checkpoint save meeting-prep
/checkpoint save build-login-page --task "Build login page with OAuth fallback"
/checkpoint save analyze-q1-data

What gets saved:
- Task name and one-line summary
- Current progress (what was done)
- Blockers and uncertainties
- Exact next action (the very next command or step)
- Relevant file paths and recent decisions
- Context snippets critical to continue

Output: Confirmation with checkpoint name and file path.


/checkpoint list

List all available checkpoints in the workspace.

Use when:
- Starting a new session and want to see what was left open
- User asks "what was I working on?"
- About to start work and want to check for existing resume points

Output: Table of checkpoints with name, created time, task summary,
and age (how long since last saved).


/checkpoint resume <name>

Read a checkpoint and restore the session to that resume point.

Trigger when:
- Session starts and a recent checkpoint exists
- User says "continue where we left off", "resume task X"
- You detect <summary> tag or compaction recovery signals
- User asks "where did we stop?"

Behavior:
1. Read the checkpoint file
2. Present the task summary and last progress
3. State the exact next action
4. Confirm before proceeding — user must approve resumption

Output: "Resuming: [task name]. Last progress: [summary]. Next action: [action]. Ready to continue? (yes/no)"


/checkpoint delete <name>

Delete a named checkpoint. Use when a task is fully complete and the
checkpoint is no longer needed.

Do not use for: archiving — delete only when the task is truly done.


Execution Steps

For /checkpoint save <name>

Before writing, gather context:

  1. Read SESSION-STATE.md — this is the WAL source; it contains active task state, decisions, current goal
  2. Scan conversation history — what has been done since session start?
  3. Verify directory exists — run ls memory/checkpoints/; if missing, create it
  4. Draft checkpoint content — fill every section of the Checkpoint File Format:
    - Task: one-line summary (infer from SESSION-STATE.md and conversation)
    - Progress: specific completed items — file paths, line ranges, command outputs
    - Next Action: exact next step — which file to edit, which command to run
    - Blockers: concrete obstacles with names and error messages
    - Key Decisions: reasoning that led to current state, not just facts
    - Relevant Context: file paths, user preferences, anything not inferable from files alone
  5. Write the completed checkpoint to memory/checkpoints/<name>.md
  6. Confirm to user: checkpoint name, task, next action

Naming rules (strict):
- Use kebab-case: build-login-page, q1-data-analysis
- Must be specific enough to identify the task at a glance
- Forbidden names: task, work, save, untitled, backup

If SESSION-STATE.md is empty: derive checkpoint content entirely from the
conversation. Do not skip the checkpoint just because SESSION-STATE.md is empty.

For /checkpoint resume <name>

  1. Read memory/checkpoints/<name>.md
  2. Verify referenced files still exist — check each file path in the checkpoint;
    flag any that are missing or changed as potentially stale
  3. Present the checkpoint as a structured recovery briefing:
    ```
    📌 Resume: <task name>
    Saved: <date> (<age>)

✅ Progress:
- <item 1>
- <item 2>

🔜 Next action: <exact command or step>

⚠️ Stale warnings (if any): <files that no longer exist>
```
4. Wait for user confirmation ("yes" / "no")
5. If confirmed: execute the next action; if stale, re-verify paths first
6. If rejected: delete the checkpoint and start fresh

/checkpoint auto Protocol (internal)

Not a user-invokable mode. The agent scans every message for these trigger signals
and acts autonomously:

Trigger signals (scan in priority order):
1. Context >70% — call session_status; if usedTokens/contextWindow > 0.70, trigger
2. Close signals — user says "goodnight", "晚安", "logout", "exit", "明天见"
3. Idle with work — session idle >10 min and SESSION-STATE.md is non-empty
4. Long operation — about to run a command expected to take >30s

When triggered:
1. Read current SESSION-STATE.md content
2. Write to memory/checkpoints/autosave.md (overwrite previous autosave)
3. Append entry to memory/checkpoints/autosave-log.md
4. No user-visible output — only write to the log file

Autosave never overwrites named checkpoints. Named checkpoints are preserved
indefinitely until explicitly deleted.


Do Not

  • Do not dump raw chat transcript into a checkpoint
  • Do not write vague progress like "worked on the task" — write exact file/state
  • Do not mark a task complete if next action is undefined
  • Do not save checkpoints for trivial one-shot replies
  • Do not overwrite a named checkpoint with autosave (keep named checkpoints intact)
  • Do not resume from checkpoint without presenting summary and confirming
  • Do not delete a checkpoint while the task is still active
  • Do not present a stale checkpoint as current — flag outdated file paths before resuming
  • Do not save a checkpoint without reading SESSION-STATE.md first (or conversation if empty)
  • Do not use forbidden names (task, work, save, untitled, backup)

Quality Bar

A checkpoint is complete when:

  • [ ] Task name and summary are clear
  • [ ] Progress is specific enough to verify what was done
  • [ ] Next action is executable without any additional context from the previous session
  • [ ] Blockers are listed with concrete details (file paths, error messages, decisions pending)
  • [ ] Relevant decisions are captured (not just facts — the reasoning that led to them)
  • [ ] File paths referenced are actual paths, not generic descriptions
  • [ ] Age of checkpoint is surfaced at resume (via file mtime, shown to user before confirming)

A checkpoint is incomplete if you have to ask "what were we doing?" after reading it.


Good vs. Bad Examples

Aspect Good Bad
Progress "Wrote src/auth.py lines 45-120, login flow complete" "Worked on auth"
Next action "Run pytest tests/auth_test.py to verify login" "Continue testing"
Blockers "Git conflict in src/main.py lines 80-95" "Need to fix some conflicts"
Context "User prefers verbose error messages" nothing
Naming build-login-page, q1-financial-analysis task, work, save, backup
State source Read SESSION-STATE.md first, then conversation Guessed from memory alone
Staleness Verified file paths exist before presenting Presented as if still current

Checkpoint File Format

# Checkpoint: &lt;name&gt;

**Created:** YYYY-MM-DD HH:MM GMT+8
**Task:** &lt;one-line task summary&gt;

## Progress
&lt;!-- What was completed — be specific --&gt;

## Next Action
&lt;!-- Exact next step: file, command, decision --&gt;

## Blockers
&lt;!-- Concrete obstacles: errors, conflicts, pending decisions --&gt;

## Key Decisions
&lt;!-- Reasoning that led to current state, not just facts --&gt;

## Relevant Context
&lt;!-- Files, paths, preferences, anything needed to continue --&gt;

**Age:** Derived from file mtime at resume time (shown automatically by `/checkpoint list` script)

Architecture

memory/
└── checkpoints/
    ├── &lt;name&gt;.md        # Named checkpoints (user-created)
    └── autosave.md       # Auto-created before risky close events

SESSION-STATE.md           # Active working memory (existing WAL target)
memory/working-buffer.md  # Danger zone log (existing compaction recovery)

Relationship to existing files:
- SESSION-STATE.md — active task state, written per WAL Protocol. Primary source when saving a checkpoint — read it first.
- memory/working-buffer.md — danger zone exchanges, consumed on recovery. Check this when checkpoint content is thin.
- memory/checkpoints/ — durable resume artifacts, survive session death. Named checkpoints never expire unless explicitly deleted.

How WAL and checkpoints differ:
- WAL (SESSION-STATE.md): per-message decisions, corrections, preferences — updated on every significant human input
- Checkpoint: task-level resume state — updated at natural break points or when session ends
- WAL feeds into checkpoint: when saving, read SESSION-STATE.md to populate the checkpoint

The checkpoint system does not replace WAL; it amplifies it for multi-session durability.


References

  • references/checkpoint-format.md — full checkpoint template with examples
  • references/resume-flow.md — step-by-step resume decision tree
  • references/auto-checkpoint-signals.md — trigger signals for auto-checkpoint protocol (internal)

常见问题

5 问
答案由本站依据条目字段整理
长任务接力怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_f6d0b8c5/session-continuity。安装方式为未标注。装完刷新技能列表或重开客户端。

长任务接力支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

长任务接力权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

长任务接力收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

长任务接力用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。