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Agent Skill · 研究检索

constraint-discovery(约束发现)

constraint-discovery 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。

constraint-discovery 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;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 48,150。

总安装 703周安装 29GitHub Stars 公开资料未说明下载 230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

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:constraint-discovery(约束发现)
来源仓库:https://github.com/terrylica/cc-skills
仓库路径:skills/constraint-discovery
安装命令:
npx skills add terrylica/cc-skills --skill "constraint-discovery"
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。

AgentSkills.tonpx skills
npx skills add terrylica/cc-skills --skill "constraint-discovery"

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。

约束发现工具从执行日志中自动提取隐含的业务规则与技术限制。适用于遗留系统重构或复杂工作流优化场景。constraint-discovery 属于研究检索类 Skill,可作为该场景下的辅助能力补充。识别未文档化的依赖关系与异常处理边界条件。需从 terrylica/cc-skills 仓库安装并接入宿主运行环境。

发现结果可能存在噪声,建议结合领域专家进行二次确认。

来源仓库内路径:skills/constraint-discovery

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 terrylica/cc-skills --skill "constraint-discovery";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

constraint-discovery 怎么安装?

在终端执行 npx skills add terrylica/cc-skills --skill "constraint-discovery"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

constraint-discovery 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。

constraint-discovery 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 terrylica/cc-skills --skill "constraint-discovery" 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。

constraint-discovery 是免费的吗?

来源标注的许可证是 MIT。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。

constraint-discovery 用的人多吗?

公开数据是累计安装 703 次,近一周安装 29 次,下载 230 次。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。

constraint-discovery 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 许可证明确为 MIT,商用和分发边界好判断。

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。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 要求有安全扫描结论才肯安装的团队。
  • 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
constraint-discovery

约束发现 · 本页

703—执行命令MIT未收录

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
constraint-discovery
description
Discover project constraints via parallel agents. TRIGGERS - constraint scan, degrees of freedom, project memory.
allowed-tools
Task, TaskOutput, Bash, Read, Grep, Glob

Constraint Discovery Skill

Spawn 5 parallel Explore agents to discover constraints that limit Claude's degrees of freedom.

When to Use

  • Invoked by /ralph:start Step 1.4.5 via Skill tool
  • User asks to analyze project constraints
  • User mentions "degrees of freedom" or "constraint scan"
  • Standalone constraint analysis needed

Agents

Agent 1: Project Memory & Philosophy Constraints

Task tool parameters:
  description: "Analyze project memory constraints"
  subagent_type: "Explore"
  run_in_background: true
  prompt: |
    DEEP DIVE into project memory files AND FOLLOW ALL @ LINKS to discover constraints.

    STEP 1 - READ THESE FILES FIRST:
    - CLAUDE.md (project instructions, philosophy, forbidden patterns)
    - .claude/ directory (memories, settings, agents/*.md)
    - .claude/agents/*.md (agent definitions with @ references)
    - ROADMAP.md (P0/P1 priorities, explicit scope limits)
    - docs/adr/ (Architecture Decision Records)

    STEP 2 - FOLLOW ALL @ LINKS (UNLIMITED DEPTH):
    Parse each file for @ link patterns:
    - @path/to/file.md (relative to project root)
    - @ai_context/PHILOSOPHY.md (ai_context directory)
    - @projectname/path/to/file.md (project prefix)
    - @AGENTS.md, @README.md (root files)

    For EACH @ link found:
    1. Read the linked file
    2. Parse it for more @ links
    3. Recursively follow until no new @ links found

    STEP 3 - EXTRACT CONSTRAINTS FROM ALL FILES:
    - "Do NOT modify X" instructions
    - Philosophy rules (e.g., "prefer simplicity over features")
    - Explicit forbidden patterns
    - Scope limits from ROADMAP

    Return NDJSON: {"source":"agent-memory","severity":"CRITICAL|HIGH|MEDIUM","description":"...","file":"...","linked_from":"...","recommendation":"Ralph should avoid..."}

Agent 2: Architecture & Coupling Constraints

Task tool parameters:
  description: "Analyze architectural constraints"
  subagent_type: "Explore"
  run_in_background: true
  prompt: |
    Analyze architectural patterns that constrain safe modification.

    STEP 1 - READ THESE FILES:
    - pyproject.toml, setup.py (package structure, entry points)
    - Core module __init__.py files (public API surface)
    - docs/adr/ (past architectural decisions)
    - docs/reference/interfaces.md (if exists)

    STEP 2 - FOLLOW @ LINKS (UNLIMITED DEPTH):
    Parse for @ link patterns in ADRs and docs:
    - @docs/reference/*.md, @docs/architecture/*.md
    - @ai_context/*.md (philosophy files)
    Recursively follow until no new @ links found.

    STEP 3 - EXTRACT CONSTRAINTS:
    - Circular imports, tightly coupled modules
    - Public API that cannot change without breaking users
    - Package structure assumptions
    - Cross-layer dependencies

    Return NDJSON: {"source":"agent-arch","severity":"HIGH|MEDIUM|LOW","description":"...","modules":["A","B"],"linked_from":"...","recommendation":"..."}

Agent 3: Research Session Lessons Learned

Task tool parameters:
  description: "Extract research session constraints"
  subagent_type: "Explore"
  run_in_background: true
  prompt: |
    Analyze past research sessions to find lessons learned and forbidden patterns.

    STEP 1 - READ THESE FILES:
    - outputs/research_sessions/*/research_summary.md (most recent 3)
    - outputs/research_sessions/*/research_log.md (if exists)
    - outputs/research_sessions/*/production_config.yaml
    - Any "lessons_learned" or "warnings" sections

    STEP 2 - FOLLOW @ LINKS:
    Research summaries may reference:
    - @strategies/*.yaml (strategy configs that failed)
    - @docs/guides/*.md (guides with constraints)
    Recursively follow until no new @ links found.

    STEP 3 - EXTRACT CONSTRAINTS:
    - Failed experiments (don't repeat these)
    - Hyperparameter ranges that caused issues
    - Strategies that were abandoned and why
    - Explicit warnings from past sessions
    - "Do not explore below X" thresholds

    Return NDJSON: {"source":"agent-research","severity":"HIGH|MEDIUM","description":"Past session found: ...","session":"...","linked_from":"...","recommendation":"Avoid..."}

Agent 4: Testing & Validation Constraints

Task tool parameters:
  description: "Find testing constraints"
  subagent_type: "Explore"
  run_in_background: true
  prompt: |
    Find testing gaps and validation requirements that constrain safe changes.

    STEP 1 - READ THESE FILES:
    - tests/ directory structure
    - pytest.ini, pyproject.toml [tool.pytest] section
    - CI/CD workflows (.github/workflows/)
    - docs/development/testing.md (if exists)

    STEP 2 - FOLLOW @ LINKS:
    Testing docs may reference:
    - @docs/development/*.md (dev guides)
    - @ai_context/*.md (philosophy that affects testing)
    Recursively follow until no new @ links found.

    STEP 3 - EXTRACT CONSTRAINTS:
    - Modules with zero test coverage (risky to modify)
    - Integration tests that must pass
    - Validation thresholds (e.g., min Sharpe ratio, max drawdown)
    - Pre-commit hooks and their requirements
    - "Tests must pass before X" gates

    Return NDJSON: {"source":"agent-testing","severity":"HIGH|MEDIUM|LOW","description":"...","location":"...","linked_from":"...","recommendation":"..."}

Agent 5: Degrees of Freedom Analysis

Task tool parameters:
  description: "Analyze degrees of freedom"
  subagent_type: "Explore"
  run_in_background: true
  prompt: |
    Find explicit and implicit limits on what Ralph can explore.

    STEP 1 - READ THESE FILES:
    - CLAUDE.md (explicit instructions)
    - .claude/ralph-config.json (previous session guidance)
    - .claude/agents/*.md (agent definitions)
    - Config files (*.yaml, *.toml) for hardcoded limits

    STEP 2 - FOLLOW ALL @ LINKS (UNLIMITED DEPTH):
    Parse each file for @ link patterns:
    - @ai_context/IMPLEMENTATION_PHILOSOPHY.md
    - @ai_context/MODULAR_DESIGN_PHILOSOPHY.md
    - @docs/reference/*.md
    - @DISCOVERIES.md, @ai_working/decisions/
    Recursively follow until no new @ links found.

    STEP 3 - EXTRACT FREEDOM CONSTRAINTS:
    - Hard gates (if not X, skip silently)
    - One-way state transitions
    - Configuration that cannot be overridden at runtime
    - Feature flags and their current state
    - Philosophy constraints (e.g., "ruthless simplicity")
    - Escape hatches (--skip-X flags, override mechanisms)

    Return NDJSON: {"source":"agent-freedom","severity":"CRITICAL|HIGH|MEDIUM","description":"...","gate":"...","linked_from":"...","recommendation":"..."}

Execution

MANDATORY: Spawn ALL 5 Task tools in a SINGLE message (parallel execution).

Use run_in_background: true for all agents.

Blocking Gate

After spawning, use TaskOutput with block: true and timeout: 30000 for each agent:

For EACH agent spawned:
  TaskOutput(task_id: "<agent_id>", block: true, timeout: 30000)

Wait for ALL 5 agents (or timeout after 30s each).

Aggregation

Merge agent findings into constraint scan file:

/usr/bin/env bash << 'AGENT_MERGE_SCRIPT'
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(pwd)}"
SCAN_FILE="$PROJECT_DIR/.claude/ralph-constraint-scan.jsonl"

# Claude MUST append each agent's NDJSON findings here:
# For each constraint JSON from agent output:
#   echo '{"_type":"constraint","source":"agent-env","severity":"HIGH","description":"..."}' >> "$SCAN_FILE"

echo "=== AGENT FINDINGS MERGED ==="
echo "Constraints in scan file:"
wc -l < "$SCAN_FILE" 2>/dev/null || echo "0"
AGENT_MERGE_SCRIPT

Output

Each agent returns NDJSON with:

  • source: Which agent found it (agent-memory, agent-arch, agent-research, agent-testing, agent-freedom)
  • severity: CRITICAL, HIGH, MEDIUM, or LOW
  • description: Human-readable constraint description
  • linked_from: Which file the constraint was discovered from (for @ link tracing)
  • recommendation: What Ralph should avoid or be careful about

平台分布

Claude Code

28.54%
按下载量换算66

OpenCode

21.68%
按下载量换算50

Antigravity

17.49%
按下载量换算40

Gemini CLI

11.72%
按下载量换算27

windsurf

7.37%
按下载量换算17

trae

3.8%
按下载量换算9

安全审计

暂无安全审计结果可展示。

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 terrylica/cc-skills --skill "constraint-discovery" 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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