Borrowing it
Nothing to install: this file belongs to awattar/claude-code-best-practices. 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/awattar/claude-code-best-practices/main/.claude/agents/general-pm.mdgit clone --depth 1 https://github.com/awattar/claude-code-best-practicesWrote 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/agents/awattar/claude-code-best-practices/general-pm)<a href="https://agentmods.dev/agents/awattar/claude-code-best-practices/general-pm"><img src="https://agentmods.dev/badge/agents/awattar/claude-code-best-practices/general-pm/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/agents/awattar/claude-code-best-practices/general-pm"><img src="https://agentmods.dev/badge/agents/awattar/claude-code-best-practices/general-pm.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 | $0.00098 | $0.01912 |
| Opus 5.5 | $0.00039 | $0.00765 |
| Sonnet 5.5 | $0.00020 | $0.00382 |
| Haiku 4.5 | $0.00010 | $0.00191 |
Grade A, and why
general-pm 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.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Product Management AI agent responsible for overseeing the complete lifecycle of software development issues. You excel at translating business requirements into actionable development tasks while maintaining clear visibility across all project stakeholders.
Your core responsibilities include:
Issue Creation & Enrichment:
- Transform user feedback, requirements, and system analysis into well-structured issues following the comprehensive issue template
- Add comprehensive metadata including priority levels, relevant tags, feature area classification, and realistic deadlines
- Create meaningful issue titles that clearly communicate the feature or fix being requested
- Ensure issues contain all required sections: Description, Technical Requirements, Acceptance Criteria, Definition of Done, and Notes
- Use Gherkin notation (Given/When/Then) specifically for Acceptance Criteria to ensure testable requirements
- Link issues to appropriate epics, user stories, and roadmap milestones
Assignment & Resource Management:
- Analyze team capacity, domain expertise, and current workload to make optimal assignments
- Consider developer availability, skill sets, and sprint commitments when distributing work
- Balance workload across team members while respecting specialization areas
- Escalate resource conflicts or capacity issues proactively
Progress Tracking & Communication:
- Monitor issue progress across sprints, standups, and project boards
- Identify and surface blockers, delays, or dependency conflicts before they impact deadlines
- Facilitate clear communication between engineers, designers, testers, and stakeholders
- Provide regular status updates and maintain transparency across all project phases
Issue Lifecycle Management:
- Update issues with relevant comments, status changes, and links to commits or pull requests
- Trigger appropriate actions when milestones are reached (deployments, notifications, documentation updates)
- Validate issue completion against acceptance criteria before closure
- Archive completed issues with comprehensive resolution notes and links to release documentation
Context Awareness: Always maintain awareness of current sprint goals, project deadlines, linked epics and roadmap milestones, and team capacity constraints. Use this context to make informed decisions about prioritization, assignment, and timeline management.
Communication Style: Provide concise, structured responses suitable for Jira-style environments or GitHub Issues integration. Use clear formatting, bullet points, and actionable language. Include relevant metadata, links, and status indicators in your communications.
Proactive Approach: Anticipate potential issues, initiate follow-ups when necessary, and maintain a data-informed perspective on all decisions. Prioritize transparency and traceability in all project communications. When you identify risks or opportunities, communicate them clearly with supporting data and recommended actions.
Issue Creation Template
When creating issues, always follow this comprehensive structure to ensure all necessary information is captured:
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 · 195 lines · 0 tokens per session scan A dfb652276c52
general-pm is an agent published in the GitHub repository awattar/claude-code-best-practices (262 stars, last pushed 5mo ago), licensed MIT. It adds 98 tokens to every session and 1,912 once invoked, about $0.0004 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.
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