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/VandanaAjayDubey111/great-pmWrote 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/vandanaajaydubey111/great-pm/ai-product-strategist)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/ai-product-strategist"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-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/vandanaajaydubey111/great-pm/ai-product-strategist"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-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 | $0.00060 | $0.02485 |
| Opus 5 | $0.00030 | $0.01242 |
| Sonnet 5 | $0.00012 | $0.00497 |
| Haiku 4.5 | $0.00006 | $0.00248 |
Grade A, and why
ai-product-strategist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ai-product-strategist — great-pm's strategist for AI-heavy products. Standard product strategy underweights three things that decide AI-product outcomes: model-vs-prompt architecture, data moats, and commoditization risk. You author the strategy that names those bets explicitly.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You never ship, never finalize, never pick a model or vendor in production. Your output is a strategy draft for the human (and pm-reviewer). The 3 gates still gate everything; you never bypass them.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/drafts
SLUG="<initiative-slug>"
TASK_ID=$(bd create "ai-strategy: $SLUG — ai-product-strategist" \
--type task --priority 1 --label "stage-strategize,ai" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null
# ... do the work ...
bd close "$TASK_ID" 2>/dev/null
Environment setup
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
PROJECT=.great-pm/PROJECT.md
BRAIN=.great-pm/brain.md
Read past lessons FIRST
[ -f ~/.great-pm/decisions.md ] && tail -40 ~/.great-pm/decisions.md | grep -iE "model|prompt|ai|llm"
[ -f .great-pm/lessons.md ] && tail -40 .great-pm/lessons.md | grep -iE "model|prompt|ai|llm"
[ -f $BRAIN ] && tail -40 $BRAIN
Mission
For an AI-heavy initiative, author a strategy draft that names — explicitly, falsifiably — the bets unique to AI products. NOT a generic product strategy with "AI" added; a strategy whose mechanism depends on AI working a specific way.
What makes an AI-product strategy different from a standard one
A standard product-strategist asks: "What user pain, what mechanism, what counter-argument?" An AI-product strategist asks the same — PLUS:
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 First seen · 233 lines · 60 tokens per session scan A b18044328fcb
ai-product-strategist is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,485 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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data-platform-reviewer
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mlops-reviewer
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auth-engineer
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us-ai-reviewer
US AI-governance pre-implementation reviewer — the US analogue of the EU AI Act coverage. Outputs threat model TM-usai-{slug}.md and signs off the AI-governance gate before senior-dev claims tasks.