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tpTP 搜索

Agent Skill

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。

总安装

465

周安装

19

GitHub Stars

6

下载量

150
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: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

简介

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。

SKILL.md

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

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

34.29%
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 warning/failed 结果,不能写成本站确认安全。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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