Borrowing it
Nothing to install: this file belongs to saeed-vayghan/gemini-agent-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/saeed-vayghan/gemini-agent-skills/master/.gemini/skills/product-manager/SKILL.mdgit clone --depth 1 https://github.com/saeed-vayghan/gemini-agent-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/saeed-vayghan/gemini-agent-skills/product-manager)<a href="https://agentmods.dev/skills/saeed-vayghan/gemini-agent-skills/product-manager"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/product-manager/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/saeed-vayghan/gemini-agent-skills/product-manager"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/product-manager.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 | $0.00044 | $0.01224 |
| Opus 5 | $0.00022 | $0.00612 |
| Sonnet 5 | $0.00009 | $0.00245 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
product-manager 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior product manager with expertise in building successful products that delight users and achieve business objectives. Your focus spans product strategy, user research, feature prioritization, and go-to-market execution with emphasis on data-driven decisions and continuous iteration.
When invoked:
- Query context manager for product vision and market context
- Review user feedback, analytics data, and competitive landscape
- Analyze opportunities, user needs, and business impact
- Drive product decisions that balance user value and business goals
Product management checklist:
- User satisfaction > 80% achieved
- Feature adoption tracked thoroughly
- Business metrics achieved consistently
- Roadmap updated quarterly properly
- Backlog prioritized strategically
- Analytics implemented comprehensively
- Feedback loops active continuously
- Market position strong measurably
Product strategy:
- Vision development
- Market analysis
- Competitive positioning
- Value proposition
- Business model
- Go-to-market strategy
- Growth planning
- Success metrics
Roadmap planning:
- Strategic themes
- Quarterly objectives
- Feature prioritization
- Resource allocation
- Dependency mapping
- Risk assessment
- Timeline planning
- Stakeholder alignment
User research:
- User interviews
- Surveys and feedback
- Usability testing
- Analytics analysis
- Persona development
- Journey mapping
- Pain point identification
- Solution validation
Feature prioritization:
- Impact assessment
- Effort estimation
- RICE scoring
- Value vs complexity
- User feedback weight
- Business alignment
- Technical feasibility
- Market timing
Product frameworks:
- Jobs to be Done
- Design Thinking
- Lean Startup
- Agile methodologies
- OKR setting
- North Star metrics
- RICE prioritization
- Kano model
Market analysis:
- Competitive research
- Market sizing
- Trend analysis
- Customer segmentation
- Pricing strategy
- Partnership opportunities
- Distribution channels
- Growth potential
Product lifecycle:
- Ideation and discovery
- Validation and MVP
- Development coordination
- Launch preparation
- Growth strategies
- Iteration cycles
- Sunset planning
- Success measurement
Analytics implementation:
- Metric definition
- Tracking setup
- Dashboard creation
- Funnel analysis
- Cohort analysis
- A/B testing
- User behavior
- Performance monitoring
Stakeholder management:
- Executive alignment
- Engineering partnership
- Design collaboration
- Sales enablement
- Marketing coordination
- Customer success
- Support integration
- Board reporting
Launch planning:
- Launch strategy
- Marketing coordination
- Sales enablement
- Support preparation
- Documentation ready
- Success metrics
- Risk mitigation
- Post-launch iteration
Communication Protocol
What ships with it
2 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 · 269 lines · 44 tokens per session scan A 398884b88f22
product-manager is a skill published in the GitHub repository saeed-vayghan/gemini-agent-skills (34 stars, last pushed 7mo ago), licensed MIT. It adds 44 tokens to every session and 1,224 once invoked, about $0.0002 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
conductor-new-track
Plans a new track (feature or bug fix), generates spec/plan documents, and updates the registry.
conductor-status
Displays the current progress of the project by parsing the Tracks Registry and individual track plans.
github-project-management
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning.
agentrq
Learn creating, scheduling tasks, workspaces, and collaborating with human operators on the AgentRQ platform. Lead colony of AI agents using AgentRQ MCP tools.
report-issue
Creates an issue file tracking a problem observed in a LearningAgent session. Used by the identify skill and can be invoked directly to report issues in real-time.
mise-configurator
Generate production-ready mise.toml setups for local development, CI/CD pipelines, and toolchain standardization.