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context-hunter上下文猎人

Agent Skill

context-hunter 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

636

周安装

26

GitHub Stars

5,420

下载量

206
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:context-hunter(上下文猎人)
来源仓库:https://github.com/mrlesk/backlog.md
仓库路径:skills/context-hunter
安装命令:
npx skills add https://github.com/mrlesk/backlog.md --skill context-hunter
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/mrlesk/backlog.md --skill context-hunter

简介

context-hunter 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于复杂任务前的聚焦发现循环,可结合来源仓库与安装命令进一步核验具体用法。
  • 通过分类任务复杂度(L0-L2)决定是否需要上下文简报,支持从文件系统中精准提取目标内容。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Context Hunter

Before writing code, run a focused discovery loop. Do not load everything. Find the right files.

Complexity Gate

Classify task complexity first:

  • L0 (trivial): typos, renames, copy-only edits, obvious single-line fixes with no behavior change.
  • L1 (moderate): behavior changes in one bounded area.
  • L2 (high-risk): cross-module changes, data semantics, refactors, architecture-impacting work.

Output by level:

  • L0: no context brief, proceed directly.
  • L1: write a micro-brief.
  • L2: write a full context brief.

Re-evaluate level during discovery and implementation. If new evidence shows higher complexity than initially classified, upgrade the level and apply the stricter workflow.

Core Behavior

Act like a senior engineer who asks the next useful question:

  1. Assess completeness: Check whether the request omits expected concerns seen in analogous code.
  2. Discover selectively: Read the minimum set of relevant files.
  3. Validate assumptions: Confirm with tests/config/history.
  4. Synthesize: Capture findings before coding for L1/L2.

Discovery Workflow (Before Coding)

1) Assess Request Completeness

Ask: "What is likely missing?"

Examples:

  • Similar endpoints include auth/validation. Is that expected here?
  • This area uses soft-delete semantics. Should this operation follow that?
  • Similar flows emit telemetry/error states. Should this change do the same?
  • Existing module boundaries suggest a different placement. Is current request still correct?

2) Run Targeted Discovery

Prioritize these in order:

  1. Find analogous implementations and copy their structure.
  2. Trace data flow for similar features end-to-end.
  3. Identify reusable utilities before creating new helpers.
  4. Inspect nearby tests to infer team priorities and edge cases.
  5. Read recent commits in the same area for current direction.

Portable discovery actions:

  • Search for feature/domain terms in relevant directories.
  • Enumerate nearby files in the affected area.
  • Inspect recent change history for touched paths.
  • Run targeted validation first, then broader project checks as needed.

3) Probe for Silent Knowledge

Look for implicit rules encoded in code:

  • Soft-delete, audit, or historical retention patterns (for data-touching changes).
  • Naming conventions (userId vs user_id) and file placement norms.
  • Existing design system choices (for this repo: Nuxt + Vue + Tailwind 4.1 patterns).
  • “Dead but dangerous” APIs/functions that exist but are no longer preferred.

4) Confidence-Based Stop Rule

Stop discovery when confidence is high enough to predict likely review feedback. If you cannot anticipate reviewer concerns yet, keep looking.

5) Produce Scaled Discovery Output

For L1, write a micro-brief:

  • Closest analog and chosen pattern.
  • Main risk or ambiguity.

For L2, write a full context brief:

  • Analogous files reviewed (with paths).
  • Patterns to follow (state/data/error handling/naming).
  • Reusable utilities/components/composables identified.
  • Risks and unknowns.

For L1/L2, keep an internal discovery log:

  • Files checked.
  • Patterns inferred.
  • Decisions made from evidence.
  • Naming evidence for new identifiers (new name -> analog paths -> extracted pattern).

Clarification Policy

  • Prefer fewer questions.
  • Ask only when the answer would change implementation approach.
  • If convention is clear, proceed silently.
  • Escalate only genuine ambiguity/conflict or product-level tradeoffs.

During Implementation

Changes should look native to the codebase:

  • Reuse existing abstractions first.
  • Match existing module boundaries and naming.
  • Follow established error-handling and testing style.
  • Prefer consistency over novelty.

Naming derivation rule:

  • Do not invent names from general priors.
  • For each new identifier family (file/function/variable/class/route), derive naming from closest local analogs.
  • Use at least 2 analogous examples when available before finalizing a new naming pattern.
  • If no analog exists, introduce the new term explicitly and record it as a no-analog exception.

If requirements conflict with discovered conventions:

  • Flag the conflict explicitly.
  • Propose 1-2 alternatives aligned with existing patterns.
  • Ask for decision when tradeoffs are product/architecture-level.

Verification

After coding:

  • Run targeted validation for changed area first.
  • Run broader checks appropriate for risk (typecheck, lint/check, tests as needed).
  • Confirm no new pattern drift was introduced.

Checklist

L0:

  • Confirmed change is truly trivial and safe to execute without full discovery.

L1/L2:

  • Classified complexity (L0/L1/L2) before discovery
  • Studied analogous features in the codebase
  • Checked for reusable utilities
  • Reviewed test patterns for similar functionality
  • Assessed request completeness before implementation
  • Identified at least one silent convention/risk
  • Produced the required artifact for the chosen level
  • Kept an internal discovery log for L1/L2
  • All new names were derived from codebase analogs, or marked as intentional no-analog exceptions
  • Verified final approach matches existing patterns

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

36.91%
按下载量换算76

Claude

29.03%
按下载量换算60

Cursor

18.61%
按下载量换算38

Gemini CLI

9.75%
按下载量换算20

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。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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