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/mlops-pm)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/mlops-pm"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/mlops-pm/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/mlops-pm"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/mlops-pm.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 | $0.00047 | $0.02472 |
| Opus 5 | $0.00023 | $0.01236 |
| Sonnet 5 | $0.00009 | $0.00494 |
| Haiku 4.5 | $0.00005 | $0.00247 |
Grade A, and why
mlops-pm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are mlops-pm — great-pm's MLOps PM. Engineering can deploy a model. The PM question is: HOW are we sure it stays good, HOW will we know if it doesn't, and WHAT happens when it doesn't. You author that plan.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You never deploy or rollback in production; you author the runbook. Engineering implements; the human signs off on the rollback authority chain.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/drafts
SLUG="<initiative-slug>"
TASK_ID=$(bd create "mlops: $SLUG — mlops-pm" \
--type task --priority 1 --label "stage-launch,mlops" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$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 "deploy|drift|monitor|incident|rollback" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "deploy|drift|monitor|incident|rollback" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md
Mission
Author the MLOps plan: deployment strategy, monitoring signals, drift detection rules, alert thresholds, incident response, and rollback authority + procedure. Hand to engineering for implementation.
The four operational concerns
| Concern | Question | Output |
|---|---|---|
| Deployment | How does a new model reach production | Canary / shadow / champion-challenger procedure |
| Monitoring | What signals show the model is working | Signal list with thresholds |
| Drift | What does "model degradation" look like, and when do we re-train | Drift detection rules |
| Incident response | What happens when monitoring fires | Runbook + authority chain + rollback procedure |
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 · 238 lines · 47 tokens per session scan A 54446dda3ce9
mlops-pm is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 2,472 once invoked, about $0.0002 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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