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Agent Skill · 研究检索

tp(TP 搜索)

tp 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。

tp 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 56,028。

总安装 465周安装 19GitHub Stars 6下载 150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
unknown

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:tp(TP 搜索)
来源仓库:https://github.com/deligoez/tp
仓库路径:skills/tp
安装命令:
npx skills add https://github.com/deligoez/tp --skill tp
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/deligoez/tp --skill tp

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。

tp 技能专为需要快速定位候选结果的场景设计,如文献调研或数据筛选任务。它能根据关键词、任务场景或来源线索高效匹配信息。核心能力包括智能关键词匹配与多维度来源过滤机制。使用时需确保 .tasks.json 文件存在或明确指定 tp 命令。

通过 npx skills add 命令从 GitHub 安装,建议提前确认权限范围及是否涉及联网操作。

来源仓库内路径:skills/tp

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/deligoez/tp --skill tp;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

tp 怎么安装?

在终端执行 npx skills add https://github.com/deligoez/tp --skill tp。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tp 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

tp 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 npx skills add https://github.com/deligoez/tp --skill tp 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。

tp 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

tp 用的人多吗?

公开数据是累计安装 465 次,近一周安装 19 次,下载 150 次,GitHub 6 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。

tp 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

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。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 Codex,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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
tp

TP 搜索 · 本页

4656执行命令未明确有提醒

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

tp — Task Plan Skill

Activates when: a .tasks.json file exists, user asks to implement a spec/plan/tasks, or user references tp commands.

Workflow A: Decompose (spec exists, no.tasks.json)

Step 0: Interview

Before writing or editing a spec, resolve all ambiguities:

  1. Locate material — read draft spec (if provided) or ask user to describe the problem.
  2. Explore codebase — read CLAUDE.md/README and affected files. Limit to files directly referenced.
  3. Identify ambiguities — list all unclear, under-specified aspects.
  4. Ask one at a time — for each ambiguity, ask one question. Derive follow-ups from answers.
  5. Prefer codebase — if answerable by reading code, explore (≤5 files) instead of asking. Architectural/product decisions always go to user.
  6. Recommend answers — provide a recommended answer for each question based on codebase context.
  7. Handle non-answers — if user says "skip"/"whatever"/empty, accept recommended answer.
  8. Termination — complete when: (a) every behavioral claim is verified or confirmed, (b) every design choice with user-visible impact (CLI output, file format, command behavior) is decided, (c) no new questions arise.

Then collect convergence parameters:

  • "How many consecutive clean review rounds? (default: 2)" — integer 1-10, re-ask once if invalid, announce "Invalid input — using default of 2" on second failure, use default on skip.
  • "How many consecutive clean audit rounds? (default: 2)" — same rules.

Announce: "I will review until N clean rounds, audit until M clean rounds." Hold values in memory until tp init.

If new ambiguities arise during spec writing, pause and return to step 3. Do not re-ask convergence params.

Step 1: Spec → Decompose

  1. tp lint <spec.md> — fix issues, review structured_elements
  2. Review loop — tp review with sub-agents until convergence (see Convergence Enforcement below)
  3. Decompose into tasks — you are the decomposer, tp validates your output
  4. Backward pass — every table row and numbered list item → task acceptance; tp validate for line coverage
  5. tp import tasks.json — validates and stores
  6. After tp init, run tp set --workflow review_clean_rounds=N audit_clean_rounds=M if non-default

Decomposition Rules

You are the decomposer — tp validates your output.

  1. Atomicity: Each task = 1 commit, 1 verb, 1-15 min estimated

- ≤3 acceptance criteria, ≤8 word title (no conjunctions), ≤2 source_sections - If >3 criteria, split by concern axis

  1. Concern axes for splitting: types/models → logic/engine → validation → CLI/wiring → tests → docs
  2. Structured elements (from tp lint): every table row, numbered list item, code block → some task's acceptance
  3. Source lines: every task MUST have source_lines as a range: "15-42" or "15-42,50-60"
  4. Dependencies: types before logic, logic before CLI, CLI before tests
  5. Preview before import: list proposed tasks and ask for confirmation

Workflow B: Execute (tasks exist)

plan=$(tp plan --minimal --json)  # ONE call for full plan
# For each task: implement → quality gate → tp commit <id> "evidence" → tp done <id> "evidence" --gate-passed --commit <sha>
# Or: tp done <id> "evidence" --gate-passed --auto-commit
# Or: batch close via tp done --batch results.ndjson

After all tasks done, run audit loop — tp audit spec.md --json, spawn sub-agents — until convergence (see Convergence Enforcement below). tp audit generates prompts; you spawn sub-agents and collect results.

Workflow C: Resume (some tasks done/wip)

Same as B. tp plan excludes done tasks, puts WIP first. Convergence enforcement applies equally — see below.

Closure Rules

Before closing a task (tp done):

  1. Re-read acceptance criteria from the plan output
  2. Verify implementation matches the FULL spec (not just acceptance summary)
  3. Write reason addressing EACH criterion with file paths as evidence
  4. Never use: "deferred", "covered by existing" (without proof), single-word reasons
  5. Use --gate-passed to relax keyword matching — evidence like "2559 tests pass" is accepted
  6. Use --covered-by <id> when work IS done but in a different task (not a deferral)
  7. tp done auto-claims open tasks — no separate tp claim needed
  8. Code snippets in spec may be illustrative — validate against actual codebase before implementing

Convergence Enforcement

NON-NEGOTIABLE: You MUST NOT proceed to decomposition until you have completed N consecutive review rounds with zero findings (any severity), where N = workflow.review_clean_rounds (default: 2). A single clean round is insufficient — consecutive clean rounds confirm the spec is stable. Do not skip rounds, summarize findings as "minor", or declare convergence prematurely.

NON-NEGOTIABLE: You MUST NOT declare implementation complete until you have completed N consecutive audit rounds with zero findings (any severity), where N = workflow.audit_clean_rounds (default: 2). This applies equally when resuming via Workflow C. Do not skip rounds or declare the audit passed based on your confidence in the implementation.

NON-NEGOTIABLE: You MUST NOT begin or continue writing the spec while unresolved questions remain. You must exhaust all questions and collect convergence parameters before starting. If you discover new ambiguities while writing the spec, pause and return to the interview phase.

Reference

For command details, field aliases, NDJSON format, and batch operations: see REFERENCE.md

平台分布

Codex

34.29%
按下载量换算51

Claude

32.89%
按下载量换算49

Cursor

17.35%
按下载量换算26

Gemini CLI

8.36%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

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 npx skills add https://github.com/deligoez/tp --skill tp 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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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