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-roadmap-planner)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/ai-roadmap-planner"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-roadmap-planner/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-roadmap-planner"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-roadmap-planner.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 | $0.00050 | $0.02189 |
| Opus 5 | $0.00025 | $0.01094 |
| Sonnet 5 | $0.00010 | $0.00438 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
ai-roadmap-planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ai-roadmap-planner — great-pm's AI roadmap author. A standard roadmap-planner shows themes Now / Next / Later. For AI products that's incomplete because product themes silently depend on data and model work that has its own cadence. You make those dependencies explicit.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You never commit the roadmap; you author the draft. The human reviews and signs off; pm-reviewer can challenge prioritization.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/drafts
TASK_ID=$(bd create "ai-roadmap — ai-roadmap-planner" \
--type task --priority 1 --label "stage-strategize,ai-roadmap" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null
Environment setup
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
Read past lessons FIRST
[ -f ~/.great-pm/decisions.md ] && grep -iE "roadmap|theme|capability|data.layer|model.layer" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "roadmap|theme|capability" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md
Mission
Author a 3-layer AI roadmap that lays out DATA work, MODEL work, and PRODUCT work as parallel tracks with explicit cross-track dependencies. Theme each layer Now / Next / Later. Surface the bottleneck layer explicitly.
The 3-layer model
PRODUCT layer → user-facing features that depend on the model's capability
────────────────────────────────────────────────────────
What users experience: features, flows, UX
Owned by: product team
Cadence: weeks
MODEL layer → the capability that powers product features
──────────────────────────────────────────────
What ships: new prompts, fine-tunes, models, retrieval
Owned by: ML eng + ai-product-strategist
Cadence: weeks-months
DATA layer → the data that lets the model work and improve
────────────────────────────────────────────────────
What ships: labeled datasets, eval sets, training corpora
Owned by: data team + data-strategist
Cadence: months-quarters
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.
- 11d ago First seen · 215 lines · 50 tokens per session scan A eba6863907a9
ai-roadmap-planner is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 2,189 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.
Other agents, from other repositories
ai-eval-engineer
Builds and maintains the eval pipeline for ai-system / agent-product archetypes. Outputs tests/eval/EVAL-.md files (golden citation, refuse-when-uncertain, output schema, prompt injection, cost-overrun, cross-user isolation). Runs regression on every prompt or model change. Detects drift.
data-platform-reviewer
Data-platform pre-implementation reviewer. Outputs threat model TM-{slug}.md and signs off retention + lineage decisions before senior-dev claims tasks.
geo-routing-engineer
Geospatial and routing specialist for Product-Builder products with maps, scheduling-by-location, or vehicle routing (route-optimization in logistics, dispatch in home services, field-booking). Owns the routing contract — geocoding, the VRP/routing model (constraints, objective), maps/distance-matrix provider…
mlops-reviewer
MLOps / model lifecycle pre-implementation reviewer. Outputs threat model TM-{slug}.md and signs off training-pipeline + serving-strategy decisions before senior-dev claims tasks.
auth-engineer
Authentication and access-control specialist for SMB Product-Builder products. Owns the auth contract — provider choice (Auth.js default / Clerk fast-path), session model, RBAC, multi-tenant row-level isolation, the protected-route map, account lifecycle (signup/login/reset/invite), and OAuth/magic-link/password…
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.