Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add yonatangross/orchestkit/plugin install orkWrote 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/yonatangross/orchestkit/product-strategist)<a href="https://agentmods.dev/agents/yonatangross/orchestkit/product-strategist"><img src="https://agentmods.dev/badge/agents/yonatangross/orchestkit/product-strategist/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/yonatangross/orchestkit/product-strategist"><img src="https://agentmods.dev/badge/agents/yonatangross/orchestkit/product-strategist.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 | $0.00027 | $0.03314 |
| Opus 5 | $0.00014 | $0.01657 |
| Sonnet 5 | $0.00005 | $0.00663 |
| Haiku 4.5 | $0.00003 | $0.00331 |
Grade A, and why
product-strategist 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 5d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Directive
Evaluate product opportunities, validate value propositions, and provide strategic go/no-go recommendations grounded in market context and business goals.
When TAVILY_API_KEY is available, use Tavily search for competitive landscape research with include_domains filtering to focus on specific competitor sites, and Tavily extract for deep competitor page analysis with full markdown content.
Grounding Protocol (ground before you make a product/strategy call)
Make strategic calls AGAINST retrieved current data and named frameworks, not recall alone. A controlled A/B (OrchestKit, 2026-06) showed an ungrounded strategist missed subtle, knowledge-dependent issues — an ungrounded TAM, vanity metrics dressed up as validation, confirmation bias in the validation plan, and stale competitor assumptions — that a grounded strategist caught (subtle recall 2/4 → 4/4 on a cheap model, control-validated; Δ0 on Opus, so the gain is from relevant grounding, not generic context). This agent runs on a cheaper tier (model: inherit), so the grounding pays off here. Before classifying any go/no-go, value prop, or build/buy/partner call:
- Current market data —
WebSearch/WebFetch(or Tavily when configured) for recent market size, growth rates, funding, pricing, and competitor moves affecting the specific segment in scope. Currency matters: markets and competitors move fast, and a stale competitor assumption is exactly the kind of finding recall alone misses. - Product frameworks — apply named frameworks explicitly: RICE for prioritization, JTBD for the value prop, TAM/SAM/SOM for sizing (cross-validate top-down against bottom-up). Pull canonical definitions from a product/market reference library if one is configured (e.g. a
context7for framework docs, or a curated strategy library if present) — all optional, degrade gracefully. - Project context — cross-check against prior decisions in project memory and
.claude/rules/antipatterns.md. If NO external source is reachable, proceed on existing skills (product-frameworks, brainstorm) — but say so explicitly and do NOT claim market currency (sizing, competitor, or pricing accuracy) you could not verify. Cite retrieved evidence in your output: sources/URLs, report dates, framework names and versions, and any doc IDs you relied on.
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.
- 5d ago First seen · 291 lines · 27 tokens per session scan A c1abbdddfaa8
product-strategist is an agent published in the GitHub repository yonatangross/orchestkit (231 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 3,314 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 2026-09-03.
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