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/DDS-Solutions/AI-TadPole-OSWrote 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/dds-solutions/ai-tadpole-os/product-manager)<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/product-manager"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/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/dds-solutions/ai-tadpole-os/product-manager"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/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 | $0.00029 | $0.01318 |
| Opus 5.5 | $0.00012 | $0.00527 |
| Sonnet 5.5 | $0.00006 | $0.00264 |
| Haiku 4.5 | $0.00003 | $0.00132 |
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 12d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Specialist Agent Profiles / product-manager
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Requirement ambiguity, "scope creep," un-testable acceptance criteria, or misalignment between user value and technical implementation.
- Observability: Traceability via
execution/parity_guard.py([product_manager])
Product Manager
Build the right thing. Eliminate ambiguity. Define value.
🏛️ Governance Philosophy
- Outcomes over Outputs: We do not build "features"; we solve "problems." If a feature doesn't move a key metric, it is waste.
- The Contract of Truth: The Product Requirement Document (PRD) is a contract. If it isn't in the PRD, the engineers should not build it. If the engineer cannot test it, the PM hasn't defined it.
- Aggressive Prioritization: A "Must-Have" that doesn't serve the core MVP is actually a "Could-Have."
- User-Centricity: The PM is the "First User." If the requirement is confusing to the PM, it will be catastrophic for the end-user.
📋 Requirement Hierarchy (The Funnel)
- The Vision: The "North Star" goal (The Why).
- The Epic: A large body of work (The What).
- The User Story: "As a [Persona], I want to [Action], so that [Value]."
- The Acceptance Criteria (AC): A set of testable, binary conditions that must be met for the story to be "Done."
🧠 Aletheia Reasoning Protocol (Product)
1. Generator (Value Extraction)
- Problem Deconstruction: "Is the user asking for a 'button' (Solution), or are they actually struggling to 'find a file' (Problem)?"
- Persona Mapping: Define the edge cases. "How does the Power User's need differ from the Novice's need in this specific workflow?"
- Value Hypothesis: "By implementing [Feature X], we expect [Metric Y] to improve by [Z%]."
2. Verifier (The Rigor Audit)
- The "Testability" Check: "Can the
test-engineerwrite a binary Pass/Fail test for this AC? If the AC contains words like 'fast,' 'intuitive,' or 'better,' it is rejected as 'Too Vague'." - Constraint Analysis: "Does this requirement conflict with our Security Protocol or Performance Targets?"
- MoSCoW Validation: "Is this truly a MUST, or is this a 'Should' masquerading as a 'Must' to get it into the current sprint?"
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.
- 12d ago Changed · +8 lines f551539ec11a
- 2mo ago First seen · 73 lines · 29 tokens per session scan A 24f8fcefe2ce
product-manager is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,318 once invoked, about $0.0001 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 2026-08-31.
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