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Agent Skill · 研究检索

calibre-metadata-apply(calibre metadata apply 搜索)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

calibre-metadata-apply 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,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 4,964。

总安装 28,583周安装 1,203GitHub Stars 公开资料未说明下载 10,009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-0
版本
1.0.4

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:calibre-metadata-apply(calibre metadata apply 搜索)
来源仓库:https://github.com/nextaltair/calibre-metadata-apply
安装命令:
openclaw skills install calibre-metadata-apply
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install calibre-metadata-apply

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。

对运行中的 Calibre 服务器执行元数据编辑,支持批量更新与字段修复。适用于图书馆系统维护、作者名标准化或缺失信息补全等写操作场景。通过精确匹配 ISBN 或书名进行变更,避免误改相似条目。仅限明确授权时启用,防止意外覆盖重要数据或破坏原有结构。操作前建议备份数据库,并在测试环境验证变更影响。

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 OpenClaw;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 openclaw skills install calibre-metadata-apply;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

calibre-metadata-apply 怎么安装?

在终端执行 openclaw skills install calibre-metadata-apply。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

calibre-metadata-apply 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

calibre-metadata-apply 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 openclaw skills install calibre-metadata-apply 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。

calibre-metadata-apply 是免费的吗?

来源标注的许可证是 MIT-0。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。

calibre-metadata-apply 用的人多吗?

公开数据是累计安装 28,583 次,近一周安装 1,203 次,下载 10,009 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。

calibre-metadata-apply 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,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。
  • 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 OpenClaw,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。
  • 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
calibre-metadata-apply

calibre metadata apply 搜索 · 本页

28,583—执行命令MIT-0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

name
calibre-metadata-apply
description
Primary skill for Calibre metadata edits (write operations) over a running Content server. Use ONLY when the user explicitly requests changing/editing/fixing title/authors/series/series_index/tags/publisher/pubdate/languages. Never use for read-only lookups, even if an ID is mentioned.
metadata
{"openclaw":{"requires":{"bins":["node","calibredb"],"env":["CALIBRE_PASSWORD"]},"optionalBins":["pdffonts"],"optionalEnv":["CALIBRE_USERNAME"],"primaryEnv":"CALIBRE_PASSWORD","dependsOnSkills":["subagent-spawn-command-builder"],"localWrites":["skills/calibre-metadata-apply/state/runs.json"],"modifiesRemoteData":["calibre:metadata"]}}

calibre-metadata-apply

A skill for updating metadata of existing Calibre books.

Skill selection contract (strict)

  • If the user intent is metadata edit/fix/update, this skill is mandatory.
  • If the request mentions an ID together with edit/fix/update intent (e.g. ID1011 タイトル修正, ID1011 のタイトルを直して), this skill is mandatory.
  • If the request mentions an ID but only for viewing/checking/confirming (e.g. ID1021 を確認して, ID1021 の詳細), do NOT use this skill — route to calibre-catalog-read.
  • calibre-catalog-read must not be used for those edit intents.

Use this skill when the user asks any of:

  • "ID指定でタイトル修正"
  • "メタデータ編集"
  • title/authors/series/series_index/tags/publisher/pubdate/languages updates

Do NOT use this skill for:

  • Read-only lookups (e.g. "ID 1021 を確認して", "ID 1021 の情報を見せて", "show me book 1021")
  • Checking what metadata a book currently has without intent to change it
  • Those must use calibre-catalog-read

Requirements

  • calibredb must be available on PATH in the runtime environment
  • subagent-spawn-command-builder installed (for spawn payload generation)
  • pdffonts is optional/recommended for PDF evidence checks
  • Reachable Calibre Content server URL

- http://HOST:PORT/#LIBRARY_ID - If LIBRARY_ID is unknown, use #- once to list available IDs on the server.

  • --with-library can be omitted only when one of these is configured:

- env: CALIBRE_WITH_LIBRARY or CALIBRE_LIBRARY_URL or CALIBRE_CONTENT_SERVER_URL - optional library id completion: CALIBRE_LIBRARY_ID

  • Read the "Calibre Content Server" section of TOOLS.md for the correct --with-library URL.
  • Host failover (IP change resilience):

- Optional env: CALIBRE_SERVER_HOSTS=host1,host2,... - Script auto-tries candidates, including WSL host-side nameserver from /etc/resolv.conf.

  • If authentication is enabled, prefer /home/altair/.openclaw/.env:

- CALIBRE_USERNAME=<user> - CALIBRE_PASSWORD=<password>

  • Auth scheme policy for this workflow:

- Non-SSL deployment assumes Digest authentication. - Do not pass auth mode arguments such as --auth-mode / --auth-scheme.

  • Pass --password-env CALIBRE_PASSWORD (username auto-loads from env)
  • You can still override explicitly with --username <user>.

Supported fields

Direct fields (set_metadata --field)

  • title
  • title_sort
  • authors (string with & or array)
  • author_sort
  • series
  • series_index
  • tags (string or array)
  • publisher
  • pubdate (YYYY-MM-DD)
  • languages
  • comments

Helper fields

  • comments_html (OC marker block upsert)
  • analysis (auto-generates analysis HTML for comments)
  • analysis_tags (adds tags)
  • tags_merge (default true)
  • tags_remove (remove specific tags after merge)

Required execution flow

A. Target confirmation (mandatory)

  1. Run read-only lookup to narrow candidates
  2. Show id,title,authors,series,series_index
  3. Get user confirmation for final target IDs
  4. Build JSONL using only confirmed IDs

B. Proposal synthesis (when metadata is missing)

  1. Collect evidence from file extraction + web sources
  2. Show one merged proposal table with:

- candidate, source, confidence (high|medium|low) - title_sort_candidate, author_sort_candidate

  1. Get user decision:

- approve all - approve only: <fields> - reject: <fields> - edit: <field>=<value>

  1. Apply only approved/finalized fields
  2. If confidence is low or sources conflict, keep fields empty

C. Apply

  1. Run dry-run first (mandatory)
  2. Run --apply only after explicit user approval
  3. Re-read and report final values

Analysis worker policy

  • Use subagent-spawn-command-builder to generate sessions_spawn payload for heavy candidate generation

- task is required. - Profile should include model/thinking/timeout/cleanup for this workflow.

  • Use lightweight subagent model for analysis (avoid main heavy model)
  • Keep final decisions + dry-run/apply in main

Data flow disclosure

  • Local execution:

- Build calibredb set_metadata commands from JSONL. - Read/write local state files (state/runs.json).

  • Subagent execution (optional for heavy candidate generation):

- Uses sessions_spawn via subagent-spawn-command-builder. - Text/metadata sent to subagent can reach model endpoints configured by runtime profile.

  • Remote write:

- calibredb set_metadata updates metadata on the target Calibre Content server.

Security rules:

  • Prefer env-based password (--password-env CALIBRE_PASSWORD) over inline --password.
  • If user does not want external model/subagent processing, keep flow local and skip subagent orchestration.
  • In agent/chat execution, do not call calibredb directly for edit operations.

- Always execute node skills/calibre-metadata-apply/scripts/calibredb_apply.mjs.

  • Never run calibre-server from this skill.

- This workflow always targets an already-running Calibre Content server.

Connection bootstrap (mandatory)

  • Do not ask the user for --with-library first.
  • First, execute using saved defaults (env) with no explicit --with-library.

- Scripts auto-load .env and resolve CALIBRE_WITH_LIBRARY / CALIBRE_CONTENT_SERVER_URL.

  • Ask user for URL only when command output shows unresolved connection, such as:

- missing --with-library - unable to resolve usable --with-library - repeated connection failures for all candidates

Long-run turn-split policy (library-wide)

For library-wide heavy processing, always use turn-split execution.

Unknown-document recovery flow (M3)

Batch sizing rule:

  • Keep each unknown-document batch small enough to show full row-by-row results in chat (no representative sampling).
  • If unresolved items remain, stop and wait for explicit user instruction to start the next batch.

User intervention checkpoints (fixed)

  1. Light pass (metadata-only)

- Always run this stage by default (no extra user instruction required) - Analyze existing metadata only (no file content read) - Present a table to user: - current file/title - recommended title/metadata - confidence/evidence summary - Stop and wait for user instruction before any deeper stage

  1. On user request: page-1 pass

- Read only the first page and refine proposals - Report delta from light pass

  1. If still uncertain: deep pass

- Read first 5 pages + last 5 pages - Add web evidence search - Produce finalized proposal with confidence + rationale

  1. Approval gate

- Show detailed findings and request explicit approval before apply

Pending and unsupported handling

  • Use pending-review tag for unresolved/hold items.
  • If document is unresolved in current flow, do not force metadata guesses.

- Tag with pending-review and keep for follow-up investigation.

Diff report format (for unknown batch runs)

Return full results (not samples):

  • execution summary (target/changed/pending/skipped/error)
  • full changed list with id + key before/after fields
  • full pending list with id + reason
  • full error list with id + error summary
  • confidence must be expressed as high|medium|low

Runtime artifact policy

  • Keep run-state and temporary artifacts only while a run is active.
  • On successful completion, remove per-run state/artifacts.
  • On failure, keep minimal artifacts only for retry/debug, then clean up after resolution.

Internal orchestration (recommended)

  • Use lightweight subagent for all analysis stages
  • Keep apply decisions in main session
  • Persist run state for each stage in state/runs.json

Turn 1 (start)

  1. Main defines scope
  2. Main generates spawn payload via subagent-spawn-command-builder (profile example: calibre-meta), then calls sessions_spawn
  3. Save run_id/session_key/task via scripts/run_state.mjs upsert
  4. Immediately tell the user this is a subagent job and state the execution model used for analysis
  5. Reply with "analysis started" and keep normal chat responsive

Turn 2 (completion)

  1. Receive subagent completion notice
  2. Save result JSON
  3. Complete state handling via scripts/handle_completion.mjs --run-id ... --result-json ...
  4. Return summarized proposal (apply only when needed)

Run state file:

  • state/runs.json

PDF extraction policy

  1. Try ebook-convert first
  2. If empty/failed, fallback to pdftotext
  3. If both fail, switch to web-evidence-first mode

Sort reading policy

  • Use user-configured reading_script for Japanese/non-Latin sort fields

- katakana / hiragana / latin

  • Ask once on first use, then reuse for the session
  • Default policy is full reading (no truncation)
  • Read the "Calibre Content Server" section of TOOLS.md for the configured reading_script value; pass it as a CLI argument when needed.

Usage

Dry-run:

cat changes.jsonl | node skills/calibre-metadata-apply/scripts/calibredb_apply.mjs \
  --with-library "http://127.0.0.1:8080/#MyLibrary" \
  --password-env CALIBRE_PASSWORD \
  --lang ja

Dry-run (when default library is preconfigured via env/config):

cat changes.jsonl | node skills/calibre-metadata-apply/scripts/calibredb_apply.mjs \
  --password-env CALIBRE_PASSWORD \
  --lang ja

Apply:

cat changes.jsonl | node skills/calibre-metadata-apply/scripts/calibredb_apply.mjs \
  --with-library "http://127.0.0.1:8080/#MyLibrary" \
  --password-env CALIBRE_PASSWORD \
  --apply

Do not

  • Do not run direct --apply using ambiguous title matches only
  • Do not include unconfirmed IDs in apply payload
  • Do not auto-fill low-confidence candidates without explicit confirmation
  • Do not start a local server with guessed path like ~/Calibre Library

平台分布

OpenClaw

77.33%
按下载量换算7,740

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install calibre-metadata-apply 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。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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