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Agent Skill · 研究检索

bee-collect(蜜蜂收集)

bee-collect 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。

bee-collect 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,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 39,607。

总安装 1,388周安装 59GitHub Stars 4下载 486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
unknown

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:bee-collect(蜜蜂收集)
来源仓库:https://github.com/autumnsgrove/groveengine
仓库路径:skills/bee-collect
安装命令:
npx skills add https://github.com/autumnsgrove/groveengine --skill bee-collect
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/autumnsgrove/groveengine --skill bee-collect

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。

将分散任务、想法或 TODO 项收集并整理为结构化条目,便于后续处理与管理。适用于批量创建 Issue、任务票或知识条目,提升团队协作与项目管理效率。不负责实际创建工作流或存储系统,仅聚焦于内容聚合与初步分类组织。使用前应明确输入格式与输出目标,避免因结构混乱导致后续处理困难。

bee-collect 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/bee-collect

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 https://github.com/autumnsgrove/groveengine --skill bee-collect;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

    它需要调用外部服务,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

bee-collect 怎么安装?

在终端执行 npx skills add https://github.com/autumnsgrove/groveengine --skill bee-collect。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bee-collect 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。

bee-collect 需要什么权限?

权限级别是调用外部服务。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。

bee-collect 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

bee-collect 用的人多吗?

公开数据是累计安装 1,388 次,近一周安装 59 次,下载 486 次,GitHub 4 星。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。

bee-collect 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 需要明确授权才能用到公司项目里的场景。
  • 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
bee-collect

蜜蜂收集 · 本页

1,3884调用外部服务未明确有提醒

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

Bee Collect 🐝

The bee buzzes from flower to flower through the meadow, gathering what the others have bloomed. Each idea, each task, each thought—collected, examined, and stored with care. The bee doesn't build the honeycomb (that's not its job). It collects the pollen, ensures it's properly catalogued, and deposits it in the hive where it can be found when needed. When the workers arrive, they know exactly which cell holds what they need.

When to Activate

  • User provides a batch of TODOs, tasks, or ideas
  • User says "create issues for these" or "turn these into tickets"
  • User calls /bee-collect or mentions bee/issues
  • Planning work that needs to be tracked
  • Brain dumps that need structure
  • NEVER for editing code—only organizing work

IMPORTANT: This animal NEVER edits code. It only explores, organizes, and creates issues.


The Collection

BUZZ → GATHER → CHECK → DEPOSIT → REPORT
  ↓       ↲        ↓        ↲         ↓
Explore  Collect  Verify   Store    Report
Flowers  Pollen   Exists   In Hive  What Was
                  In Hive?          Done

Phase 1: BUZZ

*The bee buzzes from flower to flower, exploring what's blooming...*

Parse the brain dump into discrete issues:

Exploration signals:

  • Numbered lists → one issue per item
  • Bullet points → one issue per bullet
  • Paragraphs separated by newlines → one issue per paragraph
  • Comma-separated items → one issue per item
  • Stream-of-consciousness → split at logical boundaries

Each pollen grain should be:

  • One discrete piece of work
  • Actionable (not vague like "make it better")
  • Specific enough to verify when done

If pollen is too vague:

"This TODO is unclear: 'fix the thing'. Could you clarify what needs fixing?"

Output: List of parsed TODOs ready for inspection


Phase 2: INSPECT

*The bee examines each pollen grain carefully, understanding what it is...*

Before creating any issue, thoroughly explore the context:

Explore the Codebase:

# Grove Tools (preferred — fast, agent-friendly)
gf --agent search "keyword"      # Search the whole codebase
gf --agent todo                  # Find existing TODO/FIXME/HACK comments
gf --agent usage "ComponentName" # Where is this used?
gf --agent func "functionName"   # Find function definitions

# Fallback tools (use Grep/Glob/Read tools, or gw git)
gw git log --oneline
gw git diff

Understand the Territory:

  • What files would this change affect?
  • Are there existing patterns to follow?
  • Any related functionality already implemented?
  • Technical constraints or dependencies?

Determine Labels:

Component Labels (pick 1-3):

LabelWhen to Apply
latticeFramework, monorepo, shared infrastructure
heartwoodAuth, sessions, OAuth
arborAdmin panel, backend API
amberImages, CDN, R2 storage
clearingStatus page, health monitoring
shadeAI crawler protection, bot defense
plantPricing, billing, storefront
ivyEmail, notifications, messaging
foliageTheming, design tokens
curioMuseum exhibits, content display
meadowSocial features, community feed
forestsForest page, community groves
vineContent relationships, margin notes
graftFeature flags, A/B testing
petalContent moderation, CSAM detection
lumenAI assistant, LLM routing
myceliumMCP servers, networking
patinaBackups, cold storage
landingLanding site, marketing pages

Type Labels (pick exactly 1):

LabelWhen to Apply
bugSomething is broken
featureNew capability
enhancementImprovement to existing
securitySecurity concern
documentationDocs, guides

Output: Each pollen grain inspected with context, labels determined


Phase 3: CHECK

*The bee checks the hive—does this pollen already exist?...*

Verify no duplicates:

# Search existing open issues (gw provides formatted table output)
gw gh issue list

Compare each parsed TODO against existing issues:

  • Similar title? → Likely duplicate
  • Same acceptance criteria? → Definitely duplicate
  • Related but different scope? → New issue with reference

If duplicate found:

"Skipping '[title]' — already tracked in #[number]"

Output: List of unique pollen ready to deposit


Phase 4: BURY

*The bee deposits each new pollen grain in the comb where it can be found...*

Create properly structured issues:

Issue Template:

## Summary

[1-3 sentences describing what needs to be done and why]

## Acceptance Criteria

- [ ] [Specific, verifiable criterion]
- [ ] [Another criterion]
- [ ] [Keep to 3-6 items]

## Context

- [Relevant technical context from exploration]
- [Files/components likely affected]
- [Patterns to follow]
- [Dependencies or related issues]

Create issues using gw (always preferred over raw gh):

# Single issue creation
gw gh issue create --write \
  --title "Title in imperative mood" \
  --body "$(cat <<'EOF'
## Summary
...

## Acceptance Criteria
- [ ] ...

## Context
- Files likely affected: [list]
- Related patterns: [references]
EOF
)" \
  --label "component" --label "type"

For 3+ issues (preferred): Write a JSON file and use batch creation:

# Write issues to a JSON file
cat > /tmp/bee-issues.json <<'EOF'
[
  {
    "title": "Add glass overlay to Forest page",
    "body": "## Summary\n...\n\n## Acceptance Criteria\n- [ ] ...",
    "labels": ["forests", "enhancement"],
    "assignees": ["AutumnsGrove"]
  }
]
EOF

# Preview first, then create
gw gh issue batch --write --file /tmp/bee-issues.json --dry-run
gw gh issue batch --write --file /tmp/bee-issues.json

For 1-2 issues: Use separate gw gh issue create --write calls.

Closing issues:

gw gh issue close --write 123

Title guidelines:

  • Plain language first — write for the project owner scanning a board, not for the implementer
  • Imperative mood: "Add X" not "Adding X"
  • Format: Service: what happens in plain english (e.g., "Plant: fix broken image on pricing page")
  • Under 60 characters when possible
  • No prefixes like [FEATURE] or [BUG] (labels handle categorization)
  • No jargon or acronyms that aren't Grove service names — no HMR, PKCE, DO, SSR, CSRF, etc. in titles. Spell it out or rephrase in plain language. (Technical detail belongs in the body.)
  • Self-check before depositing: Read each title back and ask: *"If I saw this on a board with 100 other issues, would I immediately understand what this is about and why it matters — without clicking into it?"* If not, rewrite it.

Good vs. bad titles:

Bad (jargon-heavy)Good (plain language)
Hybrid dev mode: HMR + service binding fidelityDev mode: support live reload alongside real service bindings
Thorn behavioral: finish remaining wiringThorn: connect remaining rate-limit and label checks
Graduate Reeds (comments) out of greenhouseReeds: make comments a full production feature
Implement Queen coordinator Durable ObjectQueen: build the coordinator that manages background workers
Prism Multi-Pack Resolver — split adapter into registries + resolverPrism: let sites use multiple icon packs at once

Output: New issues created with full context


Phase 5: REPORT

*The bee returns to the hive, reporting what was collected...*

Report results:

🐝 BEE COLLECTION COMPLETE

## Issues Created: X

| # | Title | Labels |
|---|-------|--------|
| #531 | Add glass overlay to Forest page | forests, enhancement |
| #532 | Fix tooltip positioning on mobile | lattice, bug |
| #533 | Implement health endpoint | clearing, feature |

## Duplicates Skipped: Y

- "Cache purge tool" → already tracked in #527
- "Dark mode toggle" → already tracked in #498

## Context Provided

Each issue includes:
- Specific acceptance criteria
- Files likely affected (from codebase exploration)
- Relevant patterns to follow
- Technical constraints noted

Ready for implementation!

Output: Summary delivered, work organized


Bee Rules

Thoroughness

Explore before creating. Each issue should have enough context that the implementer doesn't need to rediscover what the bee already found.

Organization

Structure matters. Good acceptance criteria, proper labels, clear context—these make the difference between a backlog and a to-do list.

Neutrality

The bee doesn't judge priority (unless user explicitly states it). It collects what's given.

Code Safety

NEVER edit code. The bee only explores to understand context. It creates issues for others to implement.

Communication

Use collection metaphors:

  • "Buzzing to explore..." (parsing TODOs)
  • "Examining the pollen..." (exploring context)
  • "Checking the hive..." (deduplicating)
  • "Depositing in the comb..." (creating issues)

Anti-Patterns

The bee does NOT:

  • Edit any code (only explores)
  • Create issues without exploring context first
  • Create vague issues without acceptance criteria
  • Skip the duplicate check
  • Guess at priorities
  • Create issues for work that's already done

Abort Conditions

Sometimes the bee must return to the user before depositing:

When to Ask for Clarification

Vague TODOs:

User: "fix the thing"
Bee: "This pollen is too vague. Which thing needs fixing? Can you describe the bug or behavior?"

Ambiguous Scope:

User: "improve performance"
Bee: "The meadow is wide—which area feels slow? Page load? API responses? Specific interactions?"

Missing Context:

User: "add the feature we discussed"
Bee: "I don't have context from previous conversations. Could you describe the feature?"

When to Group and Ask Once

If multiple TODOs are vague, don't ask about each one individually. Group them:

These items need more detail:
- "fix the thing" → which component/page?
- "make it faster" → which part is slow?
- "add that button" → where and what should it do?

Could you clarify these three?

When to Refuse (Politely)

Work already done:

Bee: "This looks like work that's already implemented—I found the feature in [file]. Should I skip this?"

Code editing requested:

Bee: "That sounds like implementation work, not issue creation. Would you like me to find a different animal for that? 🐆 Panther can strike on specific fixes."

Massive scope:

Bee: "This would create 50+ issues. Should I group these into epics first, or would you prefer to break this into smaller batches?"

Example Collection

User says:

ok so I need to: fix the broken image on the pricing page, add a dark mode toggle to the knowledge base, wire up the new health endpoint for blog-engine, and eventually we should think about adding RSS feeds to meadow

Bee flow:

  1. 🐝 BUZZ — "Collected 4 pollen grains: broken image, dark mode toggle, health endpoint, RSS feeds"
  2. 🐝 INSPECT —

- Explored pricing page: found src/routes/pricing/+page.svelte, image component uses Image from $lib/components - Explored knowledge base: theme system exists in $lib/stores/theme, needs toggle component - Explored health endpoints: clearing service exists, needs blog-engine integration - Explored meadow: no RSS infrastructure yet

  1. 🐝 CHECK — "Checking existing issues... 'dark mode' already tracked in #498. Other 3 are new."
  2. 🐝 DEPOSIT — Created:

- #531: "Fix broken image on pricing page" — plant, bug - #532: "Wire health endpoint for blog-engine" — clearing, feature - #533: "Add RSS feed support to Meadow" — meadow, feature

  1. 🐝 REPORT — "3 issues created with full context. 1 duplicate skipped. Ready for the other animals to implement!"

*A well-organized backlog is a gift to your future self.* 🐝

平台分布

Codex

34.33%
按下载量换算167

Claude

28.55%
按下载量换算139

Cursor

19.59%
按下载量换算95

Gemini CLI

10.61%
按下载量换算52

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

权限和风险

调用外部服务

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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