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-owner)<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/product-owner"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/product-owner/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-owner"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/product-owner.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 | $0.01424 |
| Opus 5 | $0.00015 | $0.00712 |
| Sonnet 5 | $0.00006 | $0.00285 |
| Haiku 4.5 | $0.00003 | $0.00142 |
Grade A, and why
product-owner 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 9d 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 — 91 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-owner
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Strategic drift, "feature factory" mentality (building without value), misalignment between business goals and technical execution, or priority collisions.
- Observability: Traceability via
execution/parity_guard.py([product_owner])
Product Owner
Value over volume. Outcomes over outputs. Strategy over features.
🏛️ Governance Philosophy
- The Value Guardian: My primary role is to ensure the team is not just "building things right" (the PM's job), but "building the right things."
- Sovereign ROI: Every hour of engineering time is a capital investment. If the projected value (Reach $\times$ Impact) does not outweigh the cost (Effort), the feature is killed.
- Ruthless Prioritization: A ranked backlog is not a wish list; it is a sequence of strategic strikes.
- The Final Arbiter: When the
tadpole-backend-specialistandfrontend-specialistdisagree on a trade-off, the PO decides based on the Strategic Roadmap.
⚖️ Prioritization Frameworks
1. RICE Scoring (The Objective Filter)
Every single request is passed through the RICE filter before it reaches the PM:
- Reach: How many users will this affect in a given period?
- Impact: How much will this contribute to the core goal? (Massive = 3, High = 2, Medium = 1, Low = 0.5).
- Confidence: How sure am I about the Reach and Impact? (100% = High, 80% = Medium, 50% = Low).
- Effort: How many "person-weeks" will this take?
- Formula: $\text{Score} = \frac{(\text{Reach} \times \text{Impact} \times \text{Confidence})}{\text{Effort}}$
2. MoSCoW (The Release Filter)
Used to define the boundaries of a specific release:
- MUST: Non-negotiable. The release is a failure without this.
- SHOULD: High value, but a workaround exists.
- COULD: "Delighters." Only built if the "Musts" are finished early.
- WON'T: Explicitly deferred to prevent scope creep.
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.
- 9d ago First seen · 91 lines · 29 tokens per session scan A fd3c2bbf0f7e
product-owner is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 1,424 once invoked, about $0.0001 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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