Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/flybear16/weread-shelf/skillnpx skills add flybear16/weread-shelf --skill skillgit clone --depth 1 https://github.com/flybear16/weread-shelfWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flybear16/weread-shelf/skill)<a href="https://agentmods.dev/skills/flybear16/weread-shelf/skill"><img src="https://agentmods.dev/badge/skills/flybear16/weread-shelf/skill.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00111 | $0.00939 |
| Opus 5 | $0.00056 | $0.00469 |
| Sonnet 5 | $0.00022 | $0.00188 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
weread-shelf scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WeRead-Shelf — SQL-ify your 微信读书
The local weread-shelf CLI is the entry point. It loads weread CLI's JSON
export into a local DuckDB cache, then exposes SQL queries.
When to use
User says:
- "查我微信读书的 XX" / "我的书架" / "完读率" / "最近读了什么"
- "用 SQL 分析我的读书数据"
- "把 weread 和 XX db 关联起来" / "join weread"
- "我的读书画像 / 统计 / 报告"
Quick Start
# 0. Verify environment
weread-shelf doctor
# 1. Pull latest shelf into DuckDB
weread-shelf sync
# 2. Run preset analysis
weread-shelf analysis
# 3. Custom SQL
weread-shelf sql "SELECT category, COUNT(*) FROM shelf GROUP BY 1 ORDER BY 2 DESC"
# 4. Cross-source join (e.g. attach xiaozhi device db)
weread-shelf join xz /path/to/xiaozhi.db
weread-shelf sql "SELECT s.title, x.last_active FROM shelf s LEFT JOIN xz.devices x ON x.user_id = s.book_id"
Pre-built Queries
Available via weread-shelf <command>:
| Command | Returns |
|---|---|
analysis |
Full portrait (overall + by-category + recent + top authors) |
sql "<query>" |
Arbitrary SQL |
export <path> --format parquet|csv|json |
Dump shelf |
join <alias> <path> |
Attach external SQLite |
The internal shelf table schema:
shelf (
book_id VARCHAR PRIMARY KEY,
title VARCHAR NOT NULL,
author VARCHAR,
category VARCHAR, -- e.g. '经济理财-管理'
finish_reading INTEGER, -- 0 or 1
read_update_at TIMESTAMP,
update_at TIMESTAMP,
cover VARCHAR,
deep_link VARCHAR
)
Cross-source Join
weread-shelf join <alias> <path-to-sqlite> attaches any local SQLite db.
Common join keys:
bookId↔ user-id in app DBs (xiaozhi, sbti-tools etc.)title↔ product-name (lowercased match)category↔ tag
Rules
- Always run
syncfirst if the user added new books since last query. - Prefer preset queries over raw SQL for simple metrics — they encode
domain knowledge (e.g. category is
'-'-delimited). - For agent use, prefer
sqlwith structured JSON output:weread-shelf sql "..." --format json --output /tmp/result.json - Cache file is local at
~/.weread-shelf/cache.duckdb. Safe to delete for a fresh re-sync. - Never write to
shelffrom MCP/SQL — read-only by design. To refresh, runweread-shelf sync.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 100 lines · 111 tokens per session scan A 4788c1145ce8
weread-shelf is a skill published in the GitHub repository flybear16/weread-shelf (0 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 939 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
sdk-design
Doctrine for designing and evolving any SDK Grida ships — TypeScript, Rust, or otherwise. "SDK" here means a surface that crosses a foreign-or-foreign-treated boundary: published packages, separately-versioned consumers, FFI bindings, public-by-design modules. An SDK's job is to refuse; a strict, honest surface…
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
supabase
Supabase / PostgREST Row-Level-Security playbook — pull the anon (or leaked servicerole) key out of the frontend JS, map tables from the auto-generated OpenAPI spec, test anonymous RLS READ disclosures (PII/secret leaks), and anonymous RLS WRITE abuse (insert/update/delete — e.g. forging…
nornicdb-cypher-queries
Pick fast, predictable Cypher query shapes in NornicDB — point lookups, batch retrieval, pagination, search, traversal, batched UNWIND/MERGE writes, cleanup, multi-tenant isolation. Use when writing or reviewing Cypher whose latency or throughput matters; maps user intent to the executor's hot-path query templates.
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…