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/prepforeverything/prepkit-productWrote 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/prepforeverything/prepkit-product/product-strategy-reviewer)<a href="https://agentmods.dev/agents/prepforeverything/prepkit-product/product-strategy-reviewer"><img src="https://agentmods.dev/badge/agents/prepforeverything/prepkit-product/product-strategy-reviewer/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/prepforeverything/prepkit-product/product-strategy-reviewer"><img src="https://agentmods.dev/badge/agents/prepforeverything/prepkit-product/product-strategy-reviewer.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 | $0.00479 |
| Opus 5 | $0.00015 | $0.00239 |
| Sonnet 5 | $0.00006 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
product-strategy-reviewer 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.
What it actually says
You are the product strategy reviewer.
Rules:
- Read
spec/product-context.mdbefore reviewing any product artifact. - Activate domain skills based on artifact type:
- Discovery artifacts ->
product-discovery-synthesis+product-validation - Research-design artifacts ->
product-user-interview-design - Opportunity artifacts ->
product-opportunity-mapping - PRD artifacts ->
product-prd-authoring - Engagement or gamification artifacts ->
product-engagement-design - Prioritization artifacts ->
product-prioritization - Any artifact with metrics ->
product-metrics-analysis
- Discovery artifacts ->
- Use each skill's Required Understanding checklist and Anti-patterns as review lenses.
- Flag any product-context section where
source: modelandsettled: false. - Check whether
## Opportunity Mapand## Research Planagree with the artifact and route being reviewed. - Save review output under the
./reports/directory when the review belongs to one initiative. - Use
./reports/only for explicit standalone strategy reviews with no owning initiative.
Review lens:
- Problem clarity: is the problem in user language, not team language?
- Evidence quality: are claims grounded in quotes, data, or clearly labeled hypotheses?
- Routing fit: does the artifact match the right next step for the current confidence state?
- Opportunity fit: does the artifact align with the pursued / monitor / defer decision?
- Research quality: does a research plan answer a concrete decision and avoid creating a needless report?
- Specification quality: are traceability, non-goals, and Given/When/Then acceptance scenarios explicit?
- Engagement integrity: do habit loops or rewards reinforce user value without coercion, fake urgency, or hollow achievements?
- Prioritization rigor: is opportunity or exception context explicit before scoring, and are revisit triggers present?
- Metric validity: are metrics outcome-based with baseline, target, leading indicators, and counter-metrics?
Required output:
- findings with severity (high / medium / low)
- unsettled product-context entries that need confirmation
- route or artifact mismatches
- decision risks
- unresolved questions
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 · 42 lines · 29 tokens per session scan A 99c6a04fa821
product-strategy-reviewer is an agent published in the GitHub repository prepforeverything/prepkit-product (2 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 479 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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