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task-loop任务循环

Agent Skill

task-loop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

192

周安装

8

GitHub Stars

14

下载量

64
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:task-loop(任务循环)
来源仓库:https://github.com/connorads/dotfiles
仓库路径:skills/task-loop
安装命令:
npx skills add https://github.com/connorads/dotfiles --skill task-loop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/connorads/dotfiles --skill task-loop

简介

task-loop 用于从 backlog 自动生成自包含任务循环目录,包含 PROMPT.md 和 run-log.md 便于交接执行。

  • 它适用于无记忆 Agent 场景下的任务自动化流转,支持多轮独立运行。
  • 使用时需提供 backlog 文件路径,否则自动查找最近创建的任务清单。
  • 安装前建议确认 Git 忽略规则和日志存储位置权限。
  • task-loop 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Task Loop

Scaffold a self-contained loop directory from an existing backlog. The directory contains everything a fresh agent session needs to pick up a task, execute it, and hand off to the next iteration — with no session memory between runs.

When to use

Invoke when the user has a backlog (from /task-plan, hand-written, or any structured task list) and wants to run it through an automated agent loop. The output is a directory with PROMPT.md, run-log.md, and a shared.gitignore.

Process

1. Locate the backlog

Accept a backlog file path. If not provided, look for:

  • A file the user recently created or discussed
  • Files matching TASKS/*/backlog.md or *TASKS*.md
  • Any markdown file with checkbox tasks

Ask if unclear.

2. Read and understand the backlog

Read the backlog to understand:

  • Task shape (what fields each task has)
  • Verification patterns (global rules, per-task criteria, referenced docs)
  • Dependencies between tasks
  • Any reference material mentioned in the backlog header

This understanding drives how PROMPT.md is adapted.

3. Ask about execution context

Ask the user:

  • "Are there docs or skills that will help execute these tasks?" (e.g. Figma workflow, testing guide, API docs)
  • "Any project-specific verification rules beyond what's in the backlog?"
  • "What timeout per iteration makes sense?" (helps with the suggested run command)

4. Generate the loop directory

If the backlog is already in a TASKS/<name>/ directory, generate files alongside it. If not, ask where to create the directory or suggest TASKS/<name>/ based on the backlog content.

Generate these files:

PROMPT.md

The loop contract. Self-contained — the agent reads this one file and has the complete protocol. Built from the core protocol template (read references/loop-protocol.md) plus task-specific adaptations:

  • File paths — point to this directory's loop-state.md, run-log.md, and backlog.md
  • Loop completion token — emit __PROMISE_RL_DONE__ as a standalone final line when no unchecked tasks remain so the default rl promise-token handling can stop cleanly. This is the Ralph-loop “completion promise” expressed as a plain token.
  • Verification rules — extracted from the backlog's global and per-task verification patterns. Reference external docs if the backlog mentions them
  • Dependency handling — if the backlog has a dependency graph, add instructions to respect it when picking the next task
  • Reference docs — if the user mentioned helpful docs or skills, add them to the prompt so the agent knows where to look

The prompt must start with a level-1 heading (markdown linter requirement).

run-log.md

Empty file with # Run Log header. Entries are appended by the agent during execution — one entry per completed or blocked task.

Entry format:

## <ISO-timestamp> | <task-id> | <done|blocked>
- **Commit:** <sha>
- **Verification:** <what was run>
- **Surprises:** <anything unexpected, or "none">

TASKS/.gitignore

If a TASKS/.gitignore doesn't already exist in the parent directory, create one containing loop-state.md. This keeps ephemeral state out of version control while allowing clean deletion of the entire task directory.

5. Present the output

Show the user:

  • The generated directory structure
  • The PROMPT.md content (or a summary if long)
  • How to run it, e.g.: rl <n> -t 30m -- claude -p "Read and follow TASKS/<name>/PROMPT.md"

PROMPT.md anatomy

Every generated PROMPT.md has these sections:

  1. Heading# Prompt
  2. Preamble — "You are running in an automated loop. Read these files before doing anything else:" followed by the three file paths
  3. Protocol — the state machine:

- No state file or status done → append run-log entry, pick next task - No unchecked tasks remain → emit __PROMISE_RL_DONE__ and exit - Status in_progress or verifying → resume from checklist - Status blocked → log blocker, skip to next task

  1. Verification — rules adapted from the backlog, referencing external docs where appropriate
  2. Completion rule — the four conditions that must all be true before marking a task done
  3. Surprises — instructions to capture unexpected findings

Read references/loop-protocol.md for the core template. Adapt it — don't copy it verbatim. Each backlog has different verification needs and reference material.

loop-state.md

Created at runtime by the agent, not pre-created by this skill. The agent creates it when claiming a task. Format:

---
current_task: <task-id>
status: in_progress | verifying | done | blocked
last_commit: ""
next_task: <task-id>
blockers: ""
---

## Checklist

- [ ] Code changes made
- [ ] Verification passed
- [ ] Backlog checkbox updated
- [ ] Commit created

## Surprises

- (none yet)

Gitignored via TASKS/.gitignore — changes every iteration, noisy in version control. Clean deletion of the task directory removes it too.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.71%
按下载量换算22

Claude

32.58%
按下载量换算21

Cursor

17.56%
按下载量换算11

Gemini CLI

9.27%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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