Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Calibre Metadata Apply
Apply metadata updates to existing Calibre books via calibredb over a Content server. Use for controlled metadata edits after target IDs are confirmed by a read-only lookup.
skill openclawclawhub Free
Apply metadata updates to existing Calibre books via calibredb over a Content server. Use for controlled metadata edits after target IDs are confirmed by a read-only lookup.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, scripts/calibredb_apply.mjs, scripts/handle_completion.mjs, scripts/run_state.mjs
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- NEXTAltair
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
calibre-metadata-apply
A skill for updating metadata of existing Calibre books.
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 authentication is enabled, prefer /home/altair/.openclaw/.env: CALIBRE_USERNAME=<user> CALIBRE_PASSWORD=<password> Pass --password-env CALIBRE_PASSWORD (username auto-loads from env) You can still override explicitly with --username <user>. Optional auth cache: --save-auth (default file: ~/.config/calibre-metadata-apply/auth.json)
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)
A. Target confirmation (mandatory)
Run read-only lookup to narrow candidates Show id,title,authors,series,series_index Get user confirmation for final target IDs Build JSONL using only confirmed IDs
B. Proposal synthesis (when metadata is missing)
Collect evidence from file extraction + web sources Show one merged proposal table with: candidate, source, confidence (high|medium|low) title_sort_candidate, author_sort_candidate Get user decision: approve all approve only: <fields> reject: <fields> edit: <field>=<value> Apply only approved/finalized fields If confidence is low or sources conflict, keep fields empty
C. Apply
Run dry-run first (mandatory) Run --apply only after explicit user approval 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) and optional auth/config files under ~/.config/calibre-metadata-apply/. 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: Do not use --save-plain-password unless explicitly instructed by the user. 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.
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)
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 On user request: page-1 pass Read only the first page and refine proposals Report delta from light pass If still uncertain: deep pass Read first 5 pages + last 5 pages Add web evidence search Produce finalized proposal with confidence + rationale 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)
Main defines scope Main generates spawn payload via subagent-spawn-command-builder (profile example: calibre-meta), then calls sessions_spawn Save run_id/session_key/task via scripts/run_state.mjs upsert Immediately tell the user this is a subagent job and state the execution model used for analysis Reply with "analysis started" and keep normal chat responsive
Turn 2 (completion)
Receive subagent completion notice Save result JSON Complete state handling via scripts/handle_completion.mjs --run-id ... --result-json ... Return summarized proposal (apply only when needed) Run state file: state/runs.json
PDF extraction policy
Try ebook-convert first If empty/failed, fallback to pdftotext 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 persist and reuse Default policy is full reading (no truncation) Config path: ~/.config/calibre-metadata-apply/config.json key: reading_script
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 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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs
- SKILL.md
- README.md
- scripts/calibredb_apply.mjs
- scripts/handle_completion.mjs
- scripts/run_state.mjs