calibre-catalog-read

name	calibre-catalog-read
description	Read Calibre catalog data via calibredb over a Content server, and run one-book analysis workflow that writes HTML analysis block back to comments while caching analysis state in SQLite. Use for list/search/id lookups and AI reading pipeline for a selected book.

calibre-catalog-read

Use this skill for:

• Read-only catalog lookup (list/search/id)
• One-book AI reading workflow (export -> analyze -> cache -> comments HTML apply)

Requirements

• calibredb available on PATH in the runtime where scripts are executed.
• ebook-convert available for text extraction.
• Reachable Calibre Content server URL in --with-library format:
  • http://HOST:PORT/#LIBRARY_ID
• Do not assume localhost/127.0.0.1; always pass explicit reachable HOST:PORT.
• If auth is enabled, pass:
  • --username <user>
  • --password-env <ENV_VAR_NAME>

Commands

List books (JSON):

node skills/calibre-catalog-read/scripts/calibredb_read.mjs list \
  --with-library "http://192.168.11.20:8080/#Calibreライブラリ" \
  --username user --password-env CALIBRE_PASSWORD \
  --limit 50

Search books (JSON):

node skills/calibre-catalog-read/scripts/calibredb_read.mjs search \
  --with-library "http://192.168.11.20:8080/#Calibreライブラリ" \
  --username user --password-env CALIBRE_PASSWORD \
  --query 'series:"中公文庫"'

Get one book by id (JSON):

node skills/calibre-catalog-read/scripts/calibredb_read.mjs id \
  --with-library "http://192.168.11.20:8080/#Calibreライブラリ" \
  --username user --password-env CALIBRE_PASSWORD \
  --book-id 3

Run one-book pipeline (analyze + comments HTML apply + cache):

python3 skills/calibre-catalog-read/scripts/run_analysis_pipeline.py \
  --with-library "http://192.168.11.20:8080/#Calibreライブラリ" \
  --username user --password-env CALIBRE_PASSWORD \
  --book-id 3 --lang ja

Cache DB

Initialize DB schema:

python3 skills/calibre-catalog-read/scripts/analysis_db.py init \
  --db /home/altair/clawd/data/calibre_analysis.sqlite

Check current hash state:

python3 skills/calibre-catalog-read/scripts/analysis_db.py status \
  --db /home/altair/clawd/data/calibre_analysis.sqlite \
  --book-id 3 --format EPUB

Main vs Subagent responsibility (strict split)

Use this split to avoid long blocking turns on chat listeners.

Main agent (fast control plane)

• Validate user intent and target book_id.
• Confirm subagent runtime knobs: model, thinking, runTimeoutSeconds.
• Start subagent and return a short progress reply quickly.
• After subagent result arrives, run DB upsert + Calibre apply.
• Report final result to user.

Subagent (heavy analysis plane)

• Read extracted source payload.
• Generate analysis JSON strictly by schema.
• Do not run metadata apply or user-facing channel actions.

Never do in main when avoidable

• Long-form content analysis generation.
• Multi-step heavy reasoning over full excerpts.

Turn policy

• One book per run.
• Prefer asynchronous flow: quick ack first, final result after analysis.
• If analysis is unavailable, either ask user or use fallback only when explicitly acceptable.

Subagent pre-flight (required)

Before first subagent run in a session, confirm once:

• model
• thinking (low/medium/high)
• runTimeoutSeconds

Do not ask on every run. Reuse the confirmed settings for subsequent books in the same session unless the user asks to change them.

Subagent support (model-agnostic)

Book-reading analysis is a heavy task. Use a subagent with a lightweight model for analysis generation, then return results to main agent for cache/apply steps.

• Prompt template: references/subagent-analysis.prompt.md
• Input schema: references/subagent-input.schema.json
• Output schema: references/subagent-analysis.schema.json
• Input preparation helper: scripts/prepare_subagent_input.py
  • Splits extracted text into multiple files to avoid read-tool single-line size issues.

Rules:

• Use subagent only for heavy analysis generation; keep main agent lightweight and non-blocking.
• Keep final DB upsert and Calibre metadata apply in main agent.
• Process one book per run.
• Confirm model/thinking/timeout once per session, then reuse; do not hardcode provider-specific model IDs in the skill.
• Configure callback/announce behavior and rate-limit fallbacks using OpenClaw default model/subagent/fallback settings (not hardcoded in this skill).
• Exclude manga/comic-centric books from this text pipeline (skip when title/tags indicate manga/comic).
• If extracted text is too short, stop and ask user for confirmation before continuing.
  • The pipeline returns reason: low_text_requires_confirmation with prompt_en text.

Language policy

• Do not hardcode user-language prose in pipeline scripts.
• Generate user-visible analysis text from subagent output, with language controlled by user-selected settings and lang input.
• Fallback local analysis in scripts is generic/minimal; preferred path is subagent output following the prompt template.

Orchestration note (important)

run_analysis_pipeline.py is a local script and does not call OpenClaw tools by itself. Subagent execution must be orchestrated by the agent layer using sessions_spawn.

Required runtime sequence:

1. Main agent prepares subagent_input.json + chunked source_files from extracted text.
   • Use:

   python3 skills/calibre-catalog-read/scripts/prepare_subagent_input.py \
     --book-id <id> --title "<title>" --lang ja \
     --text-path /tmp/book_<id>.txt --out-dir /tmp/calibre_subagent_<id>
   

2. Main agent calls sessions_spawn (model/thinking/timeout confirmed with user).
3. Subagent reads all source_files and returns analysis JSON (schema-conformant).
4. Main agent passes that file via --analysis-json to run_analysis_pipeline.py for DB/apply.

If step 2 is skipped, pipeline falls back to local minimal analysis (only for emergency/testing).

Chat execution model (required, strict)

For Discord/chat, always run as two separate turns.

Turn A: start only (must be fast)

• Select one target book.
• Call sessions_spawn.
• Record run state (runId) via run_state.py upsert.
• Reply to user with selected title + "running in background".
• Stop turn here.

Turn B: completion only (separate later turn)

Trigger: completion announce/event for that run.

• Run one command only (completion handler):
  • scripts/handle_completion.py (get -> apply -> remove, and fail on error).
• If runId is missing, handler returns stale_or_duplicate and does nothing.
• Send completion/failure reply from handler result.

Hard rule:

• Never poll/wait/apply in Turn A.
• Never keep a chat listener turn open waiting for subagent completion.

Run state management (single-file, required)

For one-book-at-a-time operation, keep a single JSON state file:

• skills/calibre-catalog-read/state/runs.json

Use runId as the primary key (subagent execution id).

Lifecycle:

1. On spawn acceptance, upsert one record:
   • runId, book_id, title, status: "running", started_at
2. Do not wait/poll inside the same chat turn.
3. On completion announce, load record by runId and run apply.
4. On successful apply, delete that record immediately.
5. On failure, set status: "failed" + error and keep record for retry/debug.

Rules:

• Keep this file small and operational (active/failed records only).
• Ignore duplicate completion events when record is already removed.
• If record is missing at completion time, report as stale/unknown run and do not apply blindly.

Use helper scripts (avoid ad-hoc env var mistakes):

# Turn A: register running task
python3 skills/calibre-catalog-read/scripts/run_state.py upsert \
  --state skills/calibre-catalog-read/state/runs.json \
  --run-id <RUN_ID> --book-id <BOOK_ID> --title "<TITLE>"

# Turn B: completion handler (preferred)
python3 skills/calibre-catalog-read/scripts/handle_completion.py \
  --state skills/calibre-catalog-read/state/runs.json \
  --run-id <RUN_ID> \
  --analysis-json /tmp/calibre_<BOOK_ID>/analysis.json \
  --with-library "http://HOST:PORT/#LIBRARY_ID" \
  --username user --password-env CALIBRE_PASSWORD --lang ja