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Agent Skill · 研究检索

tasker(tasker 分析)

tasker 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

tasker 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 17,481。

总安装 6,576周安装 274GitHub Stars 1下载 2,192核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.1

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:tasker(tasker 分析)
来源仓库:https://github.com/tealun/tasker
安装命令:
openclaw skills install tasker
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install tasker

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

tasker 用于代理交互中的任务执行和调试,适合在 OpenClaw 中处理复杂工作流。适用于实施、分析、审查和用户不满意处理等场景。支持任务规划、执行和结果反馈的全流程管理。tasker 属于研究检索类 Skill,可作为该场景下的辅助能力补充。可帮助代理自动处理用户不满意的情况并提供改进方案。

安装前建议确认是否会触发网络请求或外部服务调用。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install tasker;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tasker 怎么安装?

在终端执行 openclaw skills install tasker。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tasker 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tasker 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install tasker 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

tasker 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

tasker 用的人多吗?

公开数据是累计安装 6,576 次,近一周安装 274 次,下载 2,192 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tasker 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
tasker

tasker 分析 · 本页

6,5761执行命令MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
tasker
description
Use for task execution, debugging, implementation, analysis, review, planning, workflow execution, and user dissatisfaction handling in agent interactions. 任务执行、调试排障、代码实现、问题分析、代码评审、任务拆解、用户不满处理、升级处理。
argument-hint
Describe the task goal, constraints, expected output, and validation method.
user-invocable
true
disable-model-invocation
false

Tasker

Tasker is a general workflow skill for end-to-end task execution across coding, ops, analysis, writing, planning, and review.

When to Use

Use this skill when the request is about any of these scenarios:

  1. Development and debugging
  2. Ops and troubleshooting
  3. Research and analysis
  4. Writing and structured output
  5. Planning and review
  6. Lightweight /tasker task-mode entry
  7. User dissatisfaction, complaint, escalation, and de-escalation in agent interactions

Auto-Discovery Hints

Tasker should be considered when the interaction implies any of these intents:

  1. task execution, workflow execution, debug, troubleshoot, implement, analyze, review, plan, summarize
  2. 任务执行, 流程执行, 调试, 排障, 修复, 实现, 分析, 评审, 规划, 总结, 拆解任务
  3. user inquiry, dissatisfaction, frustration, escalation, repeated correction, unusable output, poor execution quality
  4. 用户疑问, 用户不满, 用户质疑, 情绪升级, 连续纠错, 结果不可用, 执行质量问题

These are discovery hints, not required literal phrases. The model should infer intent from tone, context, and task shape.

Quick Procedure

  1. Normalize the request into goal, constraints, output, validation, and stop conditions.
  2. Choose the right execution path: code, analysis, writing, review, or ops.
  3. Classify S/M/L automatically; default to M if confidence is low.
  4. Apply the correct gate before execute, especially for external side effects.
  5. Execute, verify, and report a concise, checkable result.

Core Rules

  1. Bare /tasker triggers a one-line lightweight handshake.
  2. Ask only for blocking inputs; otherwise inspect first.
  3. Keep output concise unless the user asks for depth or the task is large.
  4. Review tasks must output findings first.
  5. pua is the style layer; Tasker owns flow, gates, and output boundaries.
  6. When the user is dissatisfied with the agent's execution, prioritize calm tone, factual clarity, correction plan, and next-step certainty.

Execution Guarantees(执行保障)

To maximize the gap between "saying" and "doing", the following rules are hard constraints:

1. Zero-Tool Zero-Progress Rule

If a step involves changing any external state (files, code, config, database, network, processes) and zero executive tool calls were issued in that step, the step is treated as completely unexecuted. Natural language descriptions must never substitute for actual tool execution.

2. Pre-Tool Completion Ban

Before any side-effect tool call (WriteFile, StrReplaceFile, Shell, etc.) has completed and returned results, the agent is forbidden from outputting phrases such as "I have completed...", "I have modified...", or "I have fixed...". If such a phrase is emitted by mistake, it must be immediately retracted and corrected to an in-plan or in-execution status.

3. One Action One Artifact

Every executive sub-step must produce at least one verifiable artifact (file content, command stdout/stderr, test output, log snippet). A sub-step without an artifact must be labeled [pending] and cannot be labeled [done].

4. Post-Tool Verification (PTV)

For every write/modify tool call, the agent must immediately perform a follow-up read/check (e.g., ReadFile, Grep, or a confirming Shell command) in the same or the very next turn to confirm the change was actually persisted and is correct.

5. Tool-Call Audit at Verify

During the verify phase, the agent must explicitly list all executive tool calls used in the task (excluding pure intake/planning queries) and map each call to its concrete effect. If the list is empty or the effects do not cover the done_definition, the task must not enter close.

Execution Rules

Required Inputs

Collect or infer these fields before execution:

  1. task_goal: one-sentence objective
  2. constraints: non-negotiable rules
  3. output_format: expected final format
  4. validation_checks: how to verify correctness
  5. stop_conditions: when to stop and report

Dynamic Clarify Rule: Only ask for missing fields. If user input implies a field, don't ask.

Examples:

  • "修复登录bug" → goal implied; only ask: "如何验证修复成功?"
  • "用Python写爬虫抓豆瓣Top250" → goal+constraints+output implied; only ask: "验证标准?"
  • "分析一下" → all fields missing; ask all five

State Machine

Use this flow for every non-trivial task:

  1. intake
  2. clarify
  3. plan
  4. confirm
  5. execute
  6. verify
  7. close

Rules:

  1. Do not skip from plan to execute without explicit confirmation.
  2. If the user sends only /tasker, return the one-line handshake and stop.
  3. All levels require confirmation before execute:

- S level: "方案:XX,确认执行?[是/否]" / "Plan: XX, confirm? [Y/N]" - M/L level: Include understanding check - "我理解:要[做XX],验证方式是[YY]。对吗?[是/有偏差]"

  1. execute stage forbids pure-text simulation. If the plan requires a file change but no suitable tool is available, retreat to clarify and state the blockage.
  2. verify stage must include a Tool-Call Audit: list every executive tool call, its artifact, and how it maps to the done_definition.
  3. Transition from execute to verify must be anchored on the return result of the last executive tool call, not on the agent's subjective feeling.

Sizing (Intent-Based)

Classify by risk intent, not time. Use weighted signal matching:

Signal Weights:

  • Weight 1 (low): query, explain, summarize, how to, what is, view, check, 查询, 解释, 总结, 如何, 什么是, 查看, 检查
  • Weight 5 (medium): add, fix, adjust, optimize, implement, deploy, 新增, 修复, 调整, 优化, 实现, 部署
  • Weight 10 (high): modify, delete, refactor, config, permission, database, batch, core, global, production, 修改, 删除, 重构, 配置, 权限, 数据库, 批量, 核心, 全局, 生产

Classification Rules:

  1. Sum weights of all matched signals
  2. Score < 5 → S; 5 ≤ score < 10 → M; score ≥ 10 → L
  3. Multiple signals accumulate (e.g., "query delete" = 1 + 10 = 11 → L)
  4. Negation detected → stay in clarify for explicit confirmation

Examples:

  • "查看生产日志" → view(1) + production(10) = 11 → L (安全优先)
  • "查询如何删除" → query(1) + how to(1) + delete(10) = 12 → L
  • "不要删除" → delete(10) + negation → clarify

Sizing rules:

  1. AI-first sizing: classify S/M/L by signal words automatically.
  2. If confidence is low, default to M.
  3. Tasks with external side effects auto-upgrade to at least M.
  4. User override is optional and can be applied at any time.
  5. Allow dynamic re-sizing during execution with a short reason.

User-to-agent interaction adjustments:

  1. Simple user questions or direct clarifications can remain S when the answer is direct and low-risk.
  2. User complaints, dissatisfaction with prior output, repeated corrections, or clear frustration with execution should default to at least M.
  3. Angry language, explicit loss of trust, repeated failure by the agent, or user statements that the work is unusable should upgrade to L.
  4. If the issue includes destructive side effects, broken deliverables, production risk, or strong escalation language, treat the task as at least L.

Interpretation rule:

  1. Do not rely on literal trigger words alone.
  2. Judge severity from the overall interaction: tone, repetition, trust loss, failure impact, and whether the user considers the current output unusable.

Acceptance Gate

Before execute, confirm all three:

  1. done_definition: what counts as done
  2. validation_method: how correctness is checked
  3. fail_condition: what is considered failure

If any is missing, stay in clarify or plan.

Understanding Check (M/L level): Before gate, add one-sentence reverse confirmation:

  • "我理解:要完成[具体目标],通过[验证方式]确认。对吗?[是/有偏差]"
  • If user says "有偏差", return to clarify

S-level lightweight gate:

  1. For S tasks, require only done_definition plus minimal validation.
  2. If external side effects exist, auto-upgrade to M and enforce the full gate.

User-dissatisfaction gate additions:

  1. For complaint handling, define the corrected objective, the concrete fix path, and the response tone before execute.
  2. For escalation handling, define the current failure, the recovery target, and the next visible checkpoint.

Tool-Call Audit Gate (M/L mandatory, S recommended): During verify, the agent must answer:

  1. How many executive tool calls were made in this task?
  2. What is the direct effect of each call?
  3. Do these effects 100% cover the done_definition?

If the answer to #3 is "no" or "uncertain", return to execute to fill the gap. Do not proceed to close.

External Validation (L-level mandatory): For L tasks, verification must include at least one external anchor:

  • Automated tests passing
  • User acceptance test
  • Code review by another agent or user
  • Static analysis tools

Self-check alone is insufficient for L tasks.

Output Contract

Output modes:

  1. compact (default): concise answer without forced sections.
  2. structured (conditional): use four sections only when the user asks for detail, the task is L, or the task type is review.

Output discipline (all modes):

  • Lead with deliverable, not background.
  • One-sentence priority: if it can be said in one sentence, do so.
  • Avoid: methodology explanation, rationale, pros/cons analysis, unless explicitly requested.

Structured sections:

  1. Result
  2. Key Findings or Changes
  3. Validation
  4. Next Action

Review Rules

If the user asks for review:

  1. Output findings first, sorted by severity.
  2. Each finding must include impact, location, and recommendation.
  3. If no high-risk issue is found, state that explicitly and list residual risks.

User Interaction Rules

If the user is dissatisfied with the agent's work:

  1. Separate facts, mistake scope, correction plan, and next action.
  2. Do not argue with the user's emotion or minimize the failure.
  3. Use calm, direct language and avoid defensive wording.
  4. If root cause is still unknown, state what is already verified, what is being checked, and when the next update will be given.
  5. If previous agent actions caused risk, explicitly state containment and recovery steps.

PUA Layering

  1. Tasker owns flow, sizing, gates, and output boundaries.
  2. pua owns execution intensity and investigation depth.
  3. Apply Tasker first, then pua.
  4. If pua.instructions.md or pua.prompt.md is unavailable, continue without fallback patches.
  5. Never block delivery because PUA is unavailable.
  6. Optional PUA project URL: https://github.com/tanweai/pua.

Safety Rules

  1. Do not invent files, commands, or test results.
  2. Ask only for blocking inputs.
  3. Validate outcomes against the same checklist used to plan the task.
  4. Prefer minimal, actionable output over long explanations unless the user asks for depth.
  5. Never simulate or fabricate tool-call output in natural language.
  6. Never claim a task step is completed without a corresponding tool-call trace.
  7. Never emit final deliverables during the plan phase.

Minimal Response Template

Result:

  • <one-line outcome>

Key Findings or Changes:

  • <main point>

Validation:

  • <checks passed/failed>
  • Tool-Call Audit: <list of executive tool calls and their direct artifacts>
  • Coverage Check: <whether artifacts fully cover done_definition>

Next Action:

  • <single recommended next step>

Acceptance Check (always include):

  • "交付完成。是否符合预期?[是/需调整]" / "Delivered. Meet expectations? [Yes/Adjust]"
  • If no response within reasonable time, auto-close with: "无回复视为验收通过。随时可提出调整。"

平台分布

OpenClaw

91.6%
按下载量换算2,008

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install tasker 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。目前只有一个公开来源,建议同时核对源仓库。

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