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 ooiyeefei/ccc --skill product-managementgit clone --depth 1 https://github.com/ooiyeefei/cccWrote 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/ooiyeefei/ccc/product-management)<a href="https://agentmods.dev/skills/ooiyeefei/ccc/product-management"><img src="https://agentmods.dev/badge/skills/ooiyeefei/ccc/product-management.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 | $0.00102 | $0.01836 |
| Opus 5 | $0.00051 | $0.00918 |
| Sonnet 5 | $0.00020 | $0.00367 |
| Haiku 4.5 | $0.00010 | $0.00184 |
Grade A, and why
product-management 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 8d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Management Skill
AI-native product management for startups. Transform Claude into an expert PM that processes signals, not just feature lists.
Core Philosophy
WINNING = Pain × Timing × Execution Capability
Filter aggressively from 50 gaps to 3-5 high-conviction priorities. Expert PMs track signals with confidence scores, timestamps, and velocity.
Commands Quick Reference
| Command | Purpose |
|---|---|
/pm:analyze |
Scan codebase + interview for product inventory |
/pm:landscape |
Research competitor landscape |
/pm:gaps |
Run gap analysis with WINNING filter |
/pm:file |
Batch create GitHub Issues for approved gaps |
/pm:prd |
Generate PRD and create GitHub Issue |
/pm:sync |
Sync local cache with GitHub Issues |
Agents
This plugin provides specialized agents for autonomous tasks:
| Agent | Triggers On | Purpose |
|---|---|---|
research-agent |
"research [competitor]", "scout [name]" | Deep autonomous web research |
gap-analyst |
"find gaps", "what should we build" | Systematic gap identification with scoring |
prd-generator |
"create PRD for [feature]" | Generate PRD + create GitHub Issue |
Data Storage
All data stored in .pm/ folder at project root:
.pm/
├── config.md # Positioning, scoring weights
├── product/ # Product inventory, architecture
├── competitors/ # Competitor profiles
├── gaps/ # Gap analyses with scores
├── requests/ # Synced GitHub Issues (for dedup)
├── prds/ # Generated PRDs
└── cache/last-updated.json # Staleness tracking
See references/data-structure.md for complete file templates.
WINNING Filter Scoring
Hybrid scoring approach - Claude suggests researchable criteria, user scores domain-specific:
| Criterion | Scorer | Source |
|---|---|---|
| Pain Intensity (1-10) | Claude | Review sentiment, support data |
| Market Timing (1-10) | Claude | Search trends, competitor velocity |
| Execution Capability (1-10) | User | Architecture fit, team skills |
| Strategic Fit (1-10) | User | Positioning alignment |
| Revenue Potential (1-10) | User | Conversion/retention impact |
| Competitive Moat (1-10) | User | Defensibility once built |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 199 lines · 102 tokens per session scan A e2e4cfe6831e
product-management is a skill published in the GitHub repository ooiyeefei/ccc (486 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,836 once invoked, about $0.0005 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.
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