digoal/blog is a large collection of Chinese-language articles, courses, videos, and practical learning materials about databases, especially PostgreSQL and related systems, along with topics such as AI, open source, business, and finance. It is for database administrators, developers, architects, and others learning database technologies and their applications.
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 skills add digoal/blog --skill write-prdgit clone --depth 1 https://github.com/digoal/blogWrote 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/digoal/blog/write-prd)<a href="https://agentmods.dev/skills/digoal/blog/write-prd"><img src="https://agentmods.dev/badge/skills/digoal/blog/write-prd.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00237 | $0.04947 |
| Opus 5 | $0.00118 | $0.02474 |
| Sonnet 5 | $0.00047 | $0.00989 |
| Haiku 4.5 | $0.00024 | $0.00495 |
Grade A, and why
write-prd 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 4d 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.
The source is not reproduced here
Licensed GPL-2.0
The repository is licensed GPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 4d ago First seen · 501 lines · 237 tokens per session scan A 99d2767a7ef9
write-prd is a skill published in the GitHub repository digoal/blog (8,569 stars, last pushed today), licensed GPL-2.0. It adds 237 tokens to every session and 4,947 once invoked, about $0.0012 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 2026-09-03.
Other skills, from other repositories
stale-sweep
Sweep the googleapis/mcp-toolbox repo for issues and PRs with no real activity in N days (default 60), sort each by whose silence it is (the author's, ours, or nobody's), and draft the nudge or close comment. Use whenever a maintainer asks for a stale sweep, backlog cleanup, or an SLO check, e.g. "stale sweep", "find…
whodb
Query and explore databases via MCP. Use when the user asks to inspect schemas, run SQL, browse tables, analyze data quality, generate ER diagrams, or work with PostgreSQL, MySQL, MariaDB, TiDB, SQLite, MongoDB, Redis, ClickHouse, Elasticsearch, or DuckDB.
schema-designer
Help design database schemas, create tables, and plan data models. Activates when users ask to create tables, design schemas, or model data relationships.
query-builder
Convert natural language questions into SQL queries. Activates when users ask data questions in plain English like "show me users who signed up last week" or "find orders over $100".
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
graphjin-env
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.