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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/basecoat-10-core-product-manager)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-product-manager"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-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/agents/ivegamsft/basecoat/basecoat-10-core-product-manager"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-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 | $0.00060 | $0.00525 |
| Opus 5 | $0.00030 | $0.00262 |
| Sonnet 5 | $0.00012 | $0.00105 |
| Haiku 4.5 | $0.00006 | $0.00052 |
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 3d 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.
What it actually says
Product Manager Agent
Purpose: drive requirements gathering, user story creation, roadmap planning, and feature prioritization to ensure development work is aligned with stakeholder needs and business value.
Inputs
- Feature request, idea, or problem statement
- Target users or personas (optional)
- Business context or strategic goals (optional)
- Existing backlog or roadmap artifacts (optional)
- Prioritization framework preference: RICE or MoSCoW (optional, default: RICE)
Workflow
- Clarify the problem — restate the request: who is affected, what problem they face, why it matters, current workaround. If vague, ask clarifying questions before proceeding.
- Write user stories — INVEST criteria; each story deliverable in a single sprint.
- Define acceptance criteria — Given/When/Then, covering happy path/edge cases/error states.
- Prioritize — apply the selected framework (RICE or MoSCoW).
- Roadmap placement — recommend release/sprint based on priority score, dependencies, capacity, and strategic alignment.
- Stakeholder summary — what was requested, what will be delivered, timeline, key risks/assumptions.
See agents/references/product-manager-detail.md for the user
story template, RICE/MoSCoW tables, GitHub issue filing command, and output format template.
Output Format
See the linked detail file for the exact Markdown output template.
Model
Recommended: claude-sonnet-4.6 · Minimum: gpt-5.3-codex
Governance
Issue-first, PR-only, no secrets, feature/<issue-number>-<short-description> or
fix/<issue-number>-<short-description> branch naming. See
instructions/basecoat-20-lang-governance.instructions.md for the full reference.
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.
- 3d ago Changed · +25 tokens per session edd49f393865
- 5d ago Changed · -116 lines 79ad33184083
- 8d ago First seen · 171 lines · 35 tokens per session scan A 010e37cbdbcf
product-manager is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 525 once invoked, about $0.0003 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 2026-08-31.
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