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.
npx skills add phuryn/pm-skills --skill shipping-artifactsgit 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/skills/phuryn/pm-skills/shipping-artifacts)<a href="https://agentmods.dev/skills/phuryn/pm-skills/shipping-artifacts"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/shipping-artifacts/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/skills/phuryn/pm-skills/shipping-artifacts"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/shipping-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 49 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00120 | $0.01934 |
| Opus 5 | $0.00060 | $0.00967 |
| Sonnet 5 | $0.00024 | $0.00387 |
| Haiku 4.5 | $0.00012 | $0.00193 |
Grade A, and why
shipping-artifacts 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 9d 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:
- shipping-artifacts — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shipping Artifacts: The Docs That Make AI-Built Code Reviewable
Purpose
AI agents write code fast, but they leave no durable record of intent — what the system is supposed to do, who is allowed to do what, where the secrets live, which rules are actually verified. Without that record, no human (and no auditing agent) can tell whether the code is safe to ship. This skill defines the small set of documents that restore reviewability.
These docs live in documentation/ at the repo root and are written for two readers: a human reviewer and the next AI coding agent. They are the intended-state half of every later audit — a security or performance review is only as good as the intent it can compare the code against.
How the set is organized
The set is not a fixed list — it is a small core plus conditional docs you add only when the capability exists.
- Core docs — every reviewable app has these surfaces, so always produce them.
- Conditional docs — include one only if the app actually has that capability. If it doesn't, write a single line in
architecture.md("No scheduled work — nocron.md.") rather than inventing an empty document. Reviewability comes from an honest map, and "we don't do X" is part of the map. - Most docs are reverse-engineered from code by
/document-app. The one exception istests.md, which is derived from the other docs by/derive-tests— it is the verification map, not a description of a subsystem.
Be brutally honest about the current state without being paranoid. The job is an accurate map, not a clean bill of health. Each doc is short, table-and-bullet heavy, and skips generic theory.
Core documents
Each entry: file · one-line purpose · what it must capture · how a reviewer uses it.
architecture.md— what the system is and how it hangs together.- Must capture: product overview + key assumptions; tech stack; how auth/sessions/claims flow end to end; the trust boundaries (e.g. service-role vs. client); a short Known risks / assumptions list (each entry backed by where it shows up in the code, not a generic checklist); a "Related Documents" index of every other doc produced.
- Reviewer use: the root document — everything else is cross-referenced from here.
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.
- 9d ago First seen · 80 lines · 120 tokens per session scan A 934f6e1d0eec
shipping-artifacts is a skill published in the GitHub repository phuryn/pm-skills (26,131 stars, last pushed 2mo ago), licensed MIT. It adds 120 tokens to every session and 1,934 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 2026-08-30.
Other skills, from other repositories
pmm-resume
Resume reviewer and tailoring engine for Product Marketing Managers (IC to VP, including AI PMM roles). Takes baseline resume + job description → dissects JD → ranks bullets by impact fit → rebuilds complete resume in one pass. Trigger on: resume + JD paste, "tailor this", "which bullets for this role", "rebuild for…
beachhead-segment
Identifies and scores your highest-priority beachhead segment using four-dimension scoring (Burning Pain, Willingness to Pay, Winnability, Referral Potential) with blocking gates. Reads brain context (ICP, positioning, competitive landscape, proof points) and, when available, guardrails from prior beachhead decisions…
privacy-policy
Draft a jurisdiction-aware privacy policy for any digital product — use this skill whenever a PMM or Product Manager needs to create, update, audit, or review data protection documentation, asks about GDPR, CCPA, or UK GDPR obligations, mentions "privacy policy", "cookie policy", "data retention", "right to be…
prd
Guides Product Managers and Product Marketing Managers to co-create complete Product Requirements Documents with embedded Solution Stories. Reads brain context (positioning, ICP, Revenue Levers) to anchor PRDs in strategy. Outputs: structured Solution Story for GTM communications + full PRD for execution alignment.
pre-mortem
Identifies and pressure-tests failure modes for any strategic initiative (product launch, pricing change, GTM pivot, new market entry, feature rollout) by running a cross-functional risk exercise. Loads brain context (ICP, positioning, competitive landscape) and, when available, guardrails from prior pre-mortems…
prioritization-frameworks
Selects and applies the right prioritization framework (9 frameworks: Opportunity Score, ICE, RICE, Eisenhower, Impact vs Effort, Risk vs Reward, Kano, Weighted Decision Matrix, MoSCoW) with PMM interpretation layer and GTM launch tier output (T1–T4). Reads brain context (ICP, positioning, revenue levers) and, when…