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Agent Skill · 研究检索

librarian(librarian 搜索)

librarian 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。

librarian 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,权限为需要联网;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 43,484。

总安装 979周安装 40GitHub Stars 133下载 317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:librarian(librarian 搜索)
来源仓库:https://github.com/jdforsythe/forge
仓库路径:skills/librarian
安装命令:
npx skills add https://github.com/jdforsythe/forge --skill librarian
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/jdforsythe/forge --skill librarian

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。

用于查找、检索和筛选相关信息。librarian 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合在关键词搜索或任务场景中快速定位候选结果。使用时需结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。注意是否会触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/librarian

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/jdforsythe/forge --skill librarian;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

    它需要需要联网,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

librarian 怎么安装?

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

librarian 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。

librarian 需要什么权限?

权限级别是需要联网。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。

librarian 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。

librarian 用的人多吗?

公开数据是累计安装 979 次,近一周安装 40 次,下载 317 次,GitHub 133 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。

librarian 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
librarian

librarian 搜索 · 本页

979133需要联网未明确有提醒

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

Librarian

The curation and maintenance engine for the Forge library. Audits inventory, detects quality issues, and produces actionable review reports to keep the library lean, accurate, and useful.


Expert Vocabulary Payload

Deduplication & Similarity: deduplication, semantic similarity, tag overlap, merge candidate, near-duplicate detection, Jaccard coefficient, synonym clustering Lifecycle & Quality: quality promotion, lifecycle stage, quality tier, curation, retention policy, staleness threshold, maturation criteria Inventory & Structure: orphan detection, dependency graph, index integrity, catalog maintenance, inventory audit, library hygiene, reference traversal Usage & Telemetry: usage frequency, usage telemetry, usage decay, access recency, impact-weighted frequency, archive candidate


Anti-Pattern Watchlist

Hoarder Library

  • Detection: Library exceeds 50 items with fewer than 20% showing any usage in the past 90 days. The index grows monotonically — items are added but never removed.
  • Why it fails: Search and matching degrade as irrelevant items dilute results. Users lose trust in library quality when most items are stale or broken. Cognitive overhead increases with every unused entry.
  • Resolution: Flag all items with zero usage in the past 90 days for review. Present the list sorted by staleness (oldest unused first). Recommend archive for items with no usage data at all and no recent modification.

Premature Deletion

  • Detection: Item has low usage frequency (fewer than 3 uses total) but the uses that exist are high-impact — referenced in complex blueprints, used for critical domains, or explicitly requested by name.
  • Why it fails: Specialized items (security auditors, compliance reviewers, incident responders) are infrequently needed but irreplaceable when they are. Deleting them forces recreation from scratch at the worst possible time.
  • Resolution: Before recommending removal of any low-frequency item, check usage context. If any usage occurred within a complex team blueprint or critical domain, flag as "low-frequency but high-value" and recommend keeping. Apply a higher staleness threshold (180 days instead of 90) for items in critical domains like security, compliance, and incident response.

Duplicate Blindness

  • Detection: Two or more items share more than 70% tag overlap, or their descriptions are semantically similar (same domain, same deliverables, similar SOPs). Common pattern: items created at different times for the same purpose with slightly different names (e.g., "api-developer" and "backend-engineer").
  • Why it fails: Users get inconsistent results depending on which duplicate is matched. Quality improvements to one copy do not propagate to the other. Library size inflates without capability gain.
  • Resolution: Recommend merge. Keep the version with the higher quality tier. If tied, keep the more recently modified version. If still tied, keep the version with more usage. Present both items side-by-side so the user can make the final call.

Orphan Accumulation

  • Detection: A template's roles array references agent names that do not exist in index.json agents. This happens when agents are deleted or renamed without updating the templates that reference them.
  • Why it fails: Templates that reference nonexistent agents will fail at runtime. Users who load the template get errors or incomplete teams. Trust in the library erodes.
  • Resolution: For each orphaned reference, present two options: (1) create the missing agent, or (2) update the template to remove or replace the reference. Flag the severity — a template with one orphan out of four roles is recoverable; a template where all roles are orphaned should be archived.

Quality Stagnation

  • Detection: Items remain at "untested" quality tier for more than 30 days despite having one or more recorded uses. Items at "tested" for more than 60 days with continued active usage and no modifications.
  • Why it fails: Quality tiers lose meaning if they never advance. Users cannot distinguish battle-tested agents from first drafts. The promotion system exists to build confidence — stagnation defeats that purpose.
  • Resolution: Automatically recommend promotion based on usage thresholds. Present the evidence (usage count, time at current tier, modification history) and let the user approve the promotion.

Behavioral Instructions

Phase 1: Load Inventory

  1. Read library/index.json for the current catalog. PARSE: total item count, items by type (agents, skills, templates), items by domain, items by quality tier. IF index is empty or missing: Report "Library is empty. Nothing to review." and STOP. OUTPUT: Inventory summary object.
  2. Read library/usage-log.jsonl for usage data. PARSE: per-item usage count, last-used timestamp, action history (loaded, created, modified, archived, promoted). IF usage log is empty or missing: Note "No usage data available. Staleness and promotion checks will be limited to metadata only." OUTPUT: Usage data map keyed by item path.

Phase 2: Run Checks

  1. Deduplication check. FOR each pair of items within the same type (agent-agent, skill-skill, template-template): a. Calculate tag overlap: count of shared tags divided by count of union of tags (Jaccard coefficient). b. IF tag overlap > 0.70: Flag as merge candidate. c. IF tag overlap > 0.50 AND same domain: Flag as potential merge candidate (review manually). d. Compare descriptions for semantic similarity: same domain + similar deliverables or purpose. OUTPUT: List of merge candidate pairs with overlap scores.
  2. Staleness check. FOR each item in the index: a. Find most recent usage log entry (any action type). b. Calculate days since last activity. c. IF no usage data exists AND item quality is "untested": Flag as stale (unknown — never used). d. IF last activity > 90 days (default threshold): Flag as stale. e. IF last activity > 90 days BUT item is in a critical domain (security, compliance, incident-response): Apply extended threshold of 180 days instead. OUTPUT: List of stale items with days-since-last-activity and recommended action.
  3. Quality promotion check. FOR each item with a quality tier: a. Count total "loaded" actions in usage log. b. Check for any "modified" actions in usage log. c. IF quality is "untested" AND loaded count >= 5: Recommend promotion to "tested." d. IF quality is "tested" AND loaded count >= 10 AND no "modified" actions after initial creation: Recommend promotion to "iterated." e. IF quality is "iterated" AND loaded count >= 20 AND user has explicitly reviewed: Eligible for "curated" (requires manual approval). OUTPUT: List of promotion candidates with current tier, recommended tier, and evidence.
  4. Orphan detection. FOR each template in index: a. Read the template's roles array. b. FOR each role name: Check if an agent with that name exists in the index agents array. c. IF agent not found: Flag as orphaned reference. OUTPUT: List of orphaned references with template name, missing agent name, and severity.
  5. Core overlap check. FOR each library item: a. Compare against the core skills (mission-planner, agent-creator, skill-creator, librarian). b. IF a library item's described functionality substantially overlaps with a core skill: Flag as core overlap. OUTPUT: List of items that may duplicate core skill functionality.

Phase 3: Produce Report

  1. Compile review report. Assemble all findings into a structured markdown report (see Output Format below). Include:

- Summary statistics - Merge recommendations (with which item to keep and why) - Archive/removal recommendations (with reason and staleness data) - Quality promotion recommendations (with usage evidence) - Orphaned references (with fix options) - Core overlap warnings IF no issues found: Produce a clean report with summary statistics only.

  1. Present report and WAIT for user approval. Do NOT modify any files until the user explicitly approves. IF user approves all recommendations: Proceed to Phase 4. IF user approves selectively: Execute only the approved changes. IF user rejects: STOP. No changes made.

Phase 4: Execute Changes

  1. Execute approved changes. FOR each approved merge: a. Remove the lower-quality item from index.json. b. Optionally: copy unique tags from the removed item to the kept item. c. Delete or archive the removed item's file. FOR each approved archive: a. Remove the item from index.json. b. Move the item's file to library/archive/ (create directory if needed). FOR each approved promotion: a. Update the item's quality field in index.json. FOR each approved orphan fix: a. Update the template's roles array in index.json as directed. Update index.json updated timestamp.
  2. Log all changes. FOR each change executed: Append a usage-log.jsonl entry with:

- ts: current ISO 8601 timestamp - item: path of the affected item - type: item type (agent, skill, template) - action: "archived" for removals, "promoted" for promotions, "modified" for merges and orphan fixes - context: "librarian"


Output Format

The review report uses structured markdown:

# Library Review Report

**Date:** [ISO 8601 date]
**Total items:** [count] ([agents] agents, [skills] skills, [templates] templates)

## Summary Statistics

| Domain | Agents | Skills | Templates | Total |
|--------|--------|--------|-----------|-------|
| [domain] | [n] | [n] | [n] | [n] |
| ...    | ...    | ...    | ...       | ...   |

### Quality Distribution

| Tier | Count | Percentage |
|------|-------|------------|
| Curated | [n] | [%] |
| Iterated | [n] | [%] |
| Tested | [n] | [%] |
| Untested | [n] | [%] |

## Merge Candidates

### [Item A] + [Item B]
- **Tag overlap:** [%]
- **Recommendation:** Keep [Item A] (reason: higher quality tier / more recent / more used)
- **Action required:** Merge and remove [Item B]

## Archive Candidates

### [Item Name]
- **Last activity:** [date] ([N] days ago)
- **Total uses:** [count]
- **Reason:** [Unused for >90 days / Never used / Duplicates core skill]
- **Action required:** Archive to library/archive/

## Quality Promotions

### [Item Name]
- **Current tier:** [tier]
- **Recommended tier:** [tier]
- **Evidence:** [N] uses, [N] days at current tier, [modified/unmodified]

## Orphaned References

### Template: [template-name]
- **Missing agent:** [agent-name]
- **Options:** Create agent / Remove from template / Replace with [alternative]

## No Issues

[Only shown if all checks pass]
Library is clean. No merge candidates, stale items, promotions, or orphans detected.

Examples

Example 1: Library With Issues

Scenario: Library contains 12 agents, 3 skills, and 2 templates across software and marketing domains.

Review report output:

# Library Review Report

**Date:** 2026-03-28T12:00:00Z
**Total items:** 17 (12 agents, 3 skills, 2 templates)

## Summary Statistics

| Domain | Agents | Skills | Templates | Total |
|--------|--------|--------|-----------|-------|
| software | 8 | 2 | 1 | 11 |
| marketing | 4 | 1 | 1 | 6 |

### Quality Distribution

| Tier | Count | Percentage |
|------|-------|------------|
| Curated | 0 | 0% |
| Iterated | 1 | 8% |
| Tested | 3 | 25% |
| Untested | 8 | 67% |

## Merge Candidates

### api-developer + backend-engineer
- **Tag overlap:** 78% (shared: api, rest, backend, nodejs, database, testing; unique to api-developer: openapi; unique to backend-engineer: microservices)
- **Recommendation:** Keep backend-engineer (reason: higher quality tier — tested vs untested)
- **Action required:** Merge tags and remove api-developer

### content-writer + copywriter
- **Tag overlap:** 72% (shared: writing, content, marketing, seo, editing; unique to content-writer: blog, longform; unique to copywriter: ads, conversion)
- **Recommendation:** Keep content-writer (reason: more total uses — 8 vs 3)
- **Action required:** Merge tags and remove copywriter

## Archive Candidates

### seo-analyst
- **Last activity:** 2025-11-28 (120 days ago)
- **Total uses:** 1
- **Reason:** Unused for >90 days, low total usage
- **Action required:** Archive to library/archive/

## Quality Promotions

### product-manager
- **Current tier:** untested
- **Recommended tier:** tested
- **Evidence:** 7 uses over 45 days, no modifications

### frontend-developer
- **Current tier:** untested
- **Recommended tier:** tested
- **Evidence:** 5 uses over 30 days, no modifications

### qa-engineer
- **Current tier:** tested
- **Recommended tier:** iterated
- **Evidence:** 12 uses over 60 days, unmodified since creation

## Orphaned References

### Template: marketing-campaign
- **Missing agent:** brand-strategist
- **Options:** Create brand-strategist agent / Remove from template / Replace with content-writer

Example 2: Clean Library

Scenario: Library contains 6 agents, 1 skill, and 1 template. All items are actively used, no duplicates, no orphans.

Review report output:

# Library Review Report

**Date:** 2026-03-28T12:00:00Z
**Total items:** 8 (6 agents, 1 skill, 1 template)

## Summary Statistics

| Domain | Agents | Skills | Templates | Total |
|--------|--------|--------|-----------|-------|
| software | 4 | 1 | 1 | 6 |
| marketing | 2 | 0 | 0 | 2 |

### Quality Distribution

| Tier | Count | Percentage |
|------|-------|------------|
| Curated | 1 | 17% |
| Iterated | 2 | 33% |
| Tested | 2 | 33% |
| Untested | 1 | 17% |

## No Issues

Library is clean. No merge candidates, stale items, promotions, or orphans detected.

All items have been used within the last 90 days. Quality tiers are up to date. All template references resolve to existing agents.

Environment Branching

Claude Code Environment

  • Operates on the filesystem directly. Reads and writes library/index.json, library/usage-log.jsonl, and item files under library/.
  • Executes approved changes by modifying files in place: updating JSON, moving files to library/archive/, deleting merged duplicates.
  • Creates library/archive/ directory on first archive operation if it does not exist.

Cowork / Claude.ai Environment

  • Reads from the working folder. Parses the library index and usage log from uploaded or accessible files.
  • Cannot directly modify files. Instead, produces a report with specific recommendations.
  • For removals: recommends which installed skills to deactivate or remove via the Customize panel.
  • For promotions: provides the updated index.json content for the user to apply manually.
  • For orphan fixes: provides the corrected template definition for manual update.

Questions This Skill Answers

This skill activates when the user asks any of the following (or variations):

  • "Review the library"
  • "What's in my library?"
  • "Clean up unused agents/skills"
  • "Find duplicates in the library"
  • "How many agents do I have?"
  • "Which agents haven't been used?"
  • "Trim the library"
  • "Show me library statistics"
  • "Are there any orphaned references?"
  • "Promote tested agents"
  • "What quality tier are my agents?"
  • "Is there anything stale in the library?"

References

  • ./references/review-criteria.md — Scoring rubrics, threshold definitions, and merge strategy details
  • ./schemas/index-schema.json — Library index format specification
  • ./schemas/usage-log-schema.json — Usage log entry format specification

平台分布

Codex

35.04%
按下载量换算111

Claude

30.88%
按下载量换算98

Cursor

20.15%
按下载量换算64

Gemini CLI

10.2%
按下载量换算32

安全审计

Gen Agent Trust Hub

可疑

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。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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