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deep-code-intelligence深度代码智能

Agent Skill

deep-code-intelligence 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

222

周安装

9

GitHub Stars

11

下载量

70
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:deep-code-intelligence(深度代码智能)
来源仓库:https://github.com/lobbi-docs/claude
仓库路径:skills/deep-code-intelligence
安装命令:
npx skills add https://github.com/lobbi-docs/claude --skill deep-code-intelligence
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/lobbi-docs/claude --skill deep-code-intelligence

简介

用于快速定位代码库中的结构、依赖与符号使用,支持架构分析与问题诊断。

  • 结合关键词搜索与来源线索筛选候选结果,输出证据表与置信度评估。
  • 采用“先框架后证据”流程,确保推荐基于充分的事实依据而非假设。
  • 安装前建议核实仓库维护状态及是否触发文件读写或命令执行。
  • deep-code-intelligence 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Deep Code Intelligence

The workflow for problems where speed kills quality — architecture, root cause, high-stakes refactor. Evidence first, recommendation second.

Workflow

1. Frame the problem

Restate in one paragraph: what is the goal, what is the constraint, what is the hidden cost of being wrong? Write it down even if the user already said it — framing shifts under scrutiny.

2. Build an evidence table

| Claim | Source | Confidence | Counter-evidence |
|-------|--------|-----------|------------------|
| X calls Y synchronously | src/a/b.ts:42-58 | high | ... |

No recommendation without this table. If you can't fill it, you don't know enough yet — go read more before synthesizing.

3. Identify invariants and constraints

  • Invariants the code must preserve (data integrity, ordering, idempotency).
  • Constraints from the environment (runtime version, db version, budget, timeline).
  • Assumptions Claude is making (mark explicitly — these are the hypothesis branches).

4. Hypothesis tree

For debugging/diagnosis: root-cause tree with ≥3 branches. Each branch:

  • Claim
  • Evidence-for count
  • Evidence-against count
  • Verification step (the cheapest check that confirms or refutes)

For design: alternatives tree with ≥3 options. Each option:

  • Sketch
  • Cost (effort, runtime, operational)
  • Reversibility (can we undo?)
  • Who wins / who loses (not everything is net-positive)

5. Synthesize recommendation

Only now. Recommendation has:

  • Chosen path with one-sentence rationale
  • What was rejected and why
  • Risks and mitigations
  • Rollback plan if the recommendation fails in production

6. Signal certainty honestly

  • "High confidence" — evidence is multi-source, counter-evidence addressed.
  • "Medium" — some evidence, but a few assumptions.
  • "Low" — more research needed; here's the next best step.

Never upgrade certainty to sound confident. A low-confidence answer clearly labeled is more useful than a high-confidence guess.

When to invoke principal-engineer-strategist agent

Route to the agent when:

  • The decision affects multiple teams.
  • The cost of being wrong is ≥ days of work.
  • There are hidden stakeholders (security, compliance, ops) whose concerns aren't obvious.
  • The task requires deep repo context that exceeds working memory.

Agent runs the same workflow with more depth and less context pressure.

MCP delegation

NeedTool
Task resolution → starting docscc_docs_resolve_task(task)
Compare two approaches side by sidecc_docs_compare(["approach-a", "approach-b"])
Check pattern fitcc_kb_pattern_template(name)

Anti-patterns

  • Recommending before the evidence table → advice isn't grounded; often wrong in subtle ways.
  • Single-branch hypothesis tree → not actually a tree; confirmation bias.
  • Ignoring counter-evidence → the one line that breaks the claim is the one that matters.
  • "Move fast" framing on high-stakes work → that's the definition of getting it wrong.

Reference

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

36.6%
按下载量换算26

Claude

27.19%
按下载量换算19

Cursor

19.29%
按下载量换算14

Gemini CLI

9.61%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。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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