phuryn/pm-skills is a marketplace of reusable skills, commands, and plugins that guide AI assistants through product-management work such as discovery, strategy, planning, metrics, launches, and growth. It is for product managers and teams using Claude Code, Cowork, or compatible assistants. The catalogue entries are the project's own workflows and extensions.
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.
git clone --depth 1 https://github.com/phuryn/pm-skillsWrote 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/commands/phuryn/pm-skills/write-query)<a href="https://agentmods.dev/commands/phuryn/pm-skills/write-query"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/write-query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/phuryn/pm-skills/write-query"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/write-query.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00019 | $0.00622 |
| Opus 5.5 | $0.00008 | $0.00249 |
| Sonnet 5.5 | $0.00004 | $0.00124 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
write-query 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- write-query — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/write-query -- SQL Query Generator
Describe what data you need in plain English and get an optimized SQL query. Supports multiple dialects and can read your schema from uploaded files.
Invocation
/write-query Show me daily active users for the last 30 days, broken down by plan tier
/write-query Find users who signed up last month but never completed onboarding
/write-query [upload a schema diagram] What's the conversion rate from trial to paid by cohort?
Workflow
Step 1: Understand the Question
Parse the user's natural language request to identify:
- What data is being requested (metrics, dimensions, filters)
- Time range and granularity
- Grouping and ordering preferences
- Output expectations (raw data, aggregated, ranked)
Step 2: Determine Schema
If a schema is available (uploaded diagram, DDL, or description):
- Map the request to specific tables and columns
- Identify necessary joins
If no schema is provided:
- Ask for the database type (BigQuery, PostgreSQL, MySQL, etc.)
- Infer a reasonable schema from the question and ask the user to confirm
- Use common SaaS data model conventions as defaults
Step 3: Generate Query
Apply the sql-queries skill:
- Write the SQL query in the correct dialect
- Optimize for readability and performance
- Include comments explaining key logic
- Add CTEs for complex queries to improve readability
- Handle edge cases (NULLs, timezone considerations, duplicate handling)
Step 4: Present and Iterate
## SQL Query: [What It Does]
**Dialect**: [BigQuery / PostgreSQL / MySQL / etc.]
**Tables used**: [list]
### Query
[SQL code block with comments]
### What This Returns
[Description of the output: columns, rows, expected result shape]
### Assumptions
- [Schema assumptions made]
- [Business logic assumptions]
### Notes
- [Performance considerations for large datasets]
- [Edge cases handled or flagged]
Offer:
- "Want me to modify this — add filters, change grouping, extend the time range?"
- "Should I create a companion query for a related metric?"
- "Want me to build a dashboard around this query?"
- "Need a cohort analysis version of this?"
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.
- 1mo ago First seen · 85 lines · 19 tokens per session scan A ba8fb8e96238
write-query is a command published in the GitHub repository phuryn/pm-skills (27,375 stars, last pushed 9d ago), licensed MIT. It adds 19 tokens to every session and 622 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other commands, from other repositories
brief-me
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design-ai-feature
Design an AI-powered feature end-to-end — model selection, prompt architecture, eval framework, failure modes, cost modeling, and improvement flywheel.
competitive-intel
Run a competitive intelligence analysis — landscape mapping, battlecards, 7 Powers moat comparison, positioning gaps, and monitoring plan.
discover
Run a full discovery cycle — problem framing, JTBD demand-side analysis, assumption mapping, opportunity sizing, and OST mapping — from a rough idea to validated opportunity.
setup-metrics
Set up your metrics framework — North Star selection, funnel definition, dashboard structuring, and A/B test design for your key bets.
plan-launch
Plan a full go-to-market launch — positioning, messaging hierarchy, ICP definition, launch timeline, rollback plan, and post-launch measurement.