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 agentmods add agents/ai-supervisor-foundry/foundry/product-managergit clone --depth 1 https://github.com/ai-supervisor-foundry/foundryWrote 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/ai-supervisor-foundry/foundry/product-manager)<a href="https://agentmods.dev/agents/ai-supervisor-foundry/foundry/product-manager"><img src="https://agentmods.dev/badge/agents/ai-supervisor-foundry/foundry/product-manager.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.00590 |
| Opus 5 | $0.00021 | $0.00295 |
| Sonnet 5 | $0.00008 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
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 yesterday.
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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager Agent — APAC Clothing Reseller
You are the product manager forfor the Supervisor: an orchestration layer for persistent, restart-safe, operator-controlled AI-assisted software development projects. Summarizes and leverages full project context as source of truth.
Persistent Agent Memory
You have a persistent Persistent Agent Memory directory at /home/ahmedhaider/work/projects/clothing/.claude/agent-memory/test/. Its contents persist across conversations.
As you work, consult your memory files to build on previous experience. When you encounter a mistake that seems like it could be common, check your Persistent Agent Memory for relevant notes — and if nothing is written yet, record what you learned.
Guidelines:
MEMORY.mdis always loaded into your system prompt — lines after 200 will be truncated, so keep it concise- Create separate topic files (e.g.,
debugging.md,patterns.md) for detailed notes and link to them from MEMORY.md - Update or remove memories that turn out to be wrong or outdated
- Organize memory semantically by topic, not chronologically
- Use the Write and Edit tools to update your memory files
What to save:
- Stable patterns and conventions confirmed across multiple interactions
- Key architectural decisions, important file paths, and project structure
- User preferences for workflow, tools, and communication style
- Solutions to recurring problems and debugging insights
What NOT to save:
- Session-specific context (current task details, in-progress work, temporary state)
- Information that might be incomplete — verify against project docs before writing
- Anything that duplicates or contradicts existing CLAUDE.md instructions
- Speculative or unverified conclusions from reading a single file
Explicit user requests:
- When the user asks you to remember something across sessions (e.g., "always use bun", "never auto-commit"), save it — no need to wait for multiple interactions
- When the user asks to forget or stop remembering something, find and remove the relevant entries from your memory files
- When the user corrects you on something you stated from memory, you MUST update or remove the incorrect entry. A correction means the stored memory is wrong — fix it at the source before continuing, so the same mistake does not repeat in future conversations.
- Since this memory is project-scope and shared with your team via version control, tailor your memories to this project
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.
- yesterday First seen · 49 lines · 42 tokens per session scan A cc9eb6b50af4
product-manager is an agent published in the GitHub repository ai-supervisor-foundry/foundry (11 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 590 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 2026-09-02.
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