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cook-hard努力做饭

Agent Skill

cook-hard 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

881

周安装

36

GitHub Stars

6

下载量

285
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:cook-hard(努力做饭)
来源仓库:https://github.com/duc01226/easyplatform
仓库路径:skills/cook-hard
安装命令:
npx skills add https://github.com/duc01226/easyplatform --skill cook-hard
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/duc01226/easyplatform --skill cook-hard

简介

cook-hard 强调深度分析与严谨推理,适合复杂问题的系统性探究。

  • 适用于需要多角度论证、交叉验证或多轮迭代才能得出可靠结论的任务。
  • 坚持批判性思维与序列化思考,拒绝低置信度操作,确保每一步都有支撑依据。
  • 即使耗时较长也优先保证输出质量,避免因求快而产生错误引导。
  • cook-hard 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.
Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.
AI Mistake Prevention — Failure modes to avoid on every task: - Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. - Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. - Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. - Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. - When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. - Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. - Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. - Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. - Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. - Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.
Understand Code First — HARD-GATE: Do NOT write, plan, or fix until you READ existing code. 1. Search 3+ similar patterns (grep/glob) — cite file:line evidence 2. Read existing files in target area — understand structure, base classes, conventions 3. Run python.claude/scripts/code_graph trace <file> --direction both --json when .code-graph/graph.db exists 4. Map dependencies via connections or callers_of — know what depends on your target 5. Write investigation to .ai/workspace/analysis/ for non-trivial tasks (3+ files) 6. Re-read analysis file before implementing — never work from memory alone 7. NEVER invent new patterns when existing ones work — match exactly or document deviation BLOCKED until: - [] Read target files - [] Grep 3+ patterns - [] Graph trace (if graph.db exists) - [] Assumptions verified with evidence
  • docs/project-reference/domain-entities-reference.md — Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models) (content auto-injected by hook — check for [Injected:...] header before reading)
  • docs/specs/ — Test specifications by module (read existing TCs; generate/update test specs via /tdd-spec after implementation)
Plan Quality — Every plan phase MUST ATTENTION include test specifications. 1. Add ## Test Specifications section with TC-{FEAT}-{NNN} IDs to every phase file 2. Map every functional requirement to ≥1 TC (or explicit TBD with rationale) 3. TC IDs follow TC-{FEATURE}-{NNN} format — reference by ID, never embed full content 4. Before any new workflow step: call TaskList and re-read the phase file 5. On context compaction: call TaskList FIRST — never create duplicate tasks 6. Verify TC satisfaction per phase before marking complete (evidence must be file:line, not TBD) Mode: TDD-first → reference existing TCs with Evidence: TBD. Implement-first → use TBD → /tdd-spec fills after.
Skill Variant: Variant of /cook — thorough implementation with maximum verification.

Quick Summary

Goal: Implement features with deep research, comprehensive planning, and maximum quality verification.

Workflow:

  1. Research — Deep investigation with multiple researcher subagents
  2. Plan — Detailed plan with /plan-hard, user approval required
  3. Implement — Execute with full code review and SRE review
  4. Verify — Run all tests, review changes, update docs

Key Rules:

  • Maximum thoroughness: research → plan → implement → review → test → docs
  • User approval required at plan stage
  • Break work into todo tasks; add final self-review task

Frontend/UI Context (if applicable)

When this task involves frontend or UI changes,
UI System Context — For ANY task touching .ts, .html, .scss, or .css files: MUST ATTENTION READ before implementing: 1. docs/project-reference/frontend-patterns-reference.md — component base classes, stores, forms 2. docs/project-reference/scss-styling-guide.md — BEM methodology, SCSS variables, mixins, responsive 3. docs/project-reference/design-system/README.md — design tokens, component inventory, icons Reference docs/project-config.json for project-specific paths.
  • Component patterns: docs/project-reference/frontend-patterns-reference.md
  • Styling/BEM guide: docs/project-reference/scss-styling-guide.md
  • Design system tokens: docs/project-reference/design-system/README.md

Ultrathink to plan and implement these tasks with maximum verification:

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

$ARGUMENTS

Mode: HARD - Extra research, detailed planning, mandatory reviews.

Workflow

1. Deep Research Phase

  • Launch 2-3 researcher subagents in parallel covering:

- Technical approach validation - Edge cases and failure modes - Security implications - Performance considerations

  • Use /scout-ext for comprehensive codebase analysis
  • Generate research reports (max 150 lines each)
  • External Memory: Write all research to .ai/workspace/analysis/{task-name}.analysis.md. Re-read ENTIRE file before planning.
Graph-Assisted Investigation — MANDATORY when .code-graph/graph.db exists. HARD-GATE: MUST ATTENTION run at least ONE graph command on key files before concluding any investigation. Pattern: Grep finds files → trace --direction both reveals full system flow → Grep verifies details | Task | Minimum Graph Action | | --- | --- | | Investigation/Scout | trace --direction both on 2-3 entry files | | Fix/Debug | callers_of on buggy function + tests_for | | Feature/Enhancement | connections on files to be modified | | Code Review | tests_for on changed functions | | Blast Radius | trace --direction downstream | CLI: python.claude/scripts/code_graph {command} --json. Use --node-mode file first (10-30x less noise), then --node-mode function for detail.
After implementing, run python.claude/scripts/code_graph connections <file> --json on modified files to verify no related files need updates.

Graph-Trace Before Implementation

When graph DB is available, BEFORE writing code, trace to understand the blast radius:

  • python.claude/scripts/code_graph trace <file-to-modify> --direction both --json — see what calls this code AND what it triggers
  • python.claude/scripts/code_graph trace <file-to-modify> --direction downstream --json — see all downstream consumers
  • This prevents breaking implicit dependencies (bus message consumers, event handlers)

2. Comprehensive Planning

  • Use planner subagent with all research reports
  • Create full plan directory with:

- plan.md - Overview with risk assessment - phase-XX-*.md - Detailed phase files - Success criteria for each phase - Rollback strategy

3. Verified Implementation

  • Implement one phase at a time
  • After each phase:

- Run type-check and compile - Run relevant tests - Self-review before proceeding

Batch Checkpoint (Large Plans)

For plans with 10+ tasks, execute in batches with human review:

  1. Execute batch — Complete next 3 tasks (or user-specified batch size)
  2. Report — Show what was implemented, verification output, any concerns
  3. Wait — Say "Ready for feedback" and STOP. Do NOT continue automatically.
  4. Apply feedback — Incorporate changes, then execute next batch
  5. Repeat until all tasks complete

4. Mandatory Testing

  • Use tester subagent for full test coverage
  • Write tests for:

- Happy path scenarios - Edge cases from research - Error handling paths

  • NO mocks or fake data allowed
  • Repeat until all tests pass

5. Mandatory Code Review

  • Use code-reviewer subagent
  • Address all critical and major findings
  • Re-run tests after fixes
  • Repeat until approved

6. Documentation Update

  • Use docs-manager to update relevant docs
  • Use project-manager to update project status
  • Record any architectural decisions

7. Final Report

  • Summary of all changes
  • Test coverage metrics
  • Security considerations addressed
  • Unresolved questions (if any)
  • Ask user to review and approve

When to Use

  • Critical production features
  • Security-sensitive changes
  • Public API modifications
  • Database schema changes
  • Cross-service integrations

Quality Gates

GateCriteria
Research2+ researcher reports
PlanningFull plan directory
TestsAll pass, no mocks
Review0 critical/major findings
DocsUpdated if needed

Next Steps (Standalone: MUST ATTENTION ask user via AskUserQuestion. Skip if inside workflow.)

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS: If this skill was called outside a workflow, you MUST ATTENTION use AskUserQuestion to present these options. Do NOT skip because the task seems "simple" or "obvious" — the user decides:
  • "Proceed with full workflow (Recommended)" — I'll detect the best workflow to continue from here (feature implemented). This ensures review, testing, and docs steps aren't skipped.
  • "/code-simplifier" — Simplify and clean up implementation
  • "/workflow-review-changes" — Review changes before commit
  • "Skip, continue manually" — user decides
If already inside a workflow, skip — the workflow handles sequencing.

Closing Reminders

  • MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
  • MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
  • MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
  • MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
  • MANDATORY IMPORTANT MUST ATTENTION validate decisions with user via AskUserQuestion — never auto-decide MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting:
  • MANDATORY IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE any modification. Run graph trace when graph.db exists.
  • MANDATORY IMPORTANT MUST ATTENTION include ## Test Specifications with TC IDs per phase. Call TaskList before creating new tasks.
  • MANDATORY IMPORTANT MUST ATTENTION read frontend-patterns-reference, scss-styling-guide, design-system/README before any UI change.
  • MANDATORY IMPORTANT MUST ATTENTION run at least ONE graph command on key files when graph.db exists. Pattern: grep → graph trace → grep verify.
  • MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
  • MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.

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

33.63%
按下载量换算96

Claude

30.03%
按下载量换算86

Cursor

17.1%
按下载量换算49

Gemini CLI

9.66%
按下载量换算28

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。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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