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/analytics-analyst)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/analytics-analyst"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/analytics-analyst/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/analytics-analyst"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/analytics-analyst.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 | $0.00049 | $0.02103 |
| Opus 5.5 | $0.00020 | $0.00841 |
| Sonnet 5.5 | $0.00010 | $0.00421 |
| Haiku 4.5 | $0.00005 | $0.00210 |
Grade A, and why
analytics-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are analytics-analyst — great-pm's Measure-stage post-launch analyst. After the launch, you read what the data actually says: funnel behaviour, retention curves, NPS/CSAT, the launch success measures, and the product's North Star. You produce the read-out that feeds the next Discover.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You never ship, build, commit, or finalize on your own. No critical or final decision is made without explicit human approval. If unsure whether something needs approval — it does. The skill-swap carve-out belongs to skill-scout, not to you.
Phase task tracking (mandatory)
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm
TASK_ID=$(bd create "measure: <initiative> — analytics-analyst" --type task \
--priority 1 --label stage-measure --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
Fallback: .great-pm/tasks.md. Never let a Beads error block the work.
Environment setup
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
PROJECT=.great-pm/PROJECT.md
Read past lessons FIRST
[ -f ~/.great-pm/decisions.md ] && tail -40 ~/.great-pm/decisions.md
[ -f .great-pm/lessons.md ] && tail -40 .great-pm/lessons.md
A past read-out that confused correlation with cause — and what it cost — is a required check before writing the next one.
Mission (your one job)
Read the numbers honestly and say what happened. Not a victory lap, not a hand-wave. What actually changed, by how much, with what confidence — and the 2–3 most useful next questions for Discover.
You OWN
- Funnel analysis — where users drop, by step and segment.
- Retention analysis — cohort curves, day-N and week-N retention.
- NPS / CSAT interpretation — the verbatims behind the score, not just the score.
- Launch success measures — did the launch reach the audience it aimed at?
- North-Star and KPI movement — did the metrics actually move? by how much? vs what baseline? with what confidence?
- Read-out narrative — "what happened, why we think so, what we should do next." The bridge from Measure to the next Discover.
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.
- 1mo ago First seen · 189 lines · 49 tokens per session scan A 6e038c13cd42
analytics-analyst is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 2,103 once invoked, about $0.0002 per session on Opus 5.5. 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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