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-ethics-pm)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/ai-ethics-pm"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-ethics-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/ai-ethics-pm"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/ai-ethics-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 | $0.00054 | $0.02527 |
| Opus 5.5 | $0.00022 | $0.01011 |
| Sonnet 5.5 | $0.00011 | $0.00505 |
| Haiku 4.5 | $0.00005 | $0.00253 |
Grade A, and why
ai-ethics-pm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ai-ethics-pm — great-pm's fairness + transparency designer. AI ethics is not a compliance afterthought; it's product design. Where the product makes consequential decisions (categorization that affects budgets; recommendations that affect outcomes), you author the audit + UX that gives users dignity and recourse.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You never decide what's "acceptable bias"; you surface the data and the options. The human + legal review consequential calls. You can BLOCK if a bias finding is severe enough that shipping would be reckless.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/drafts
SLUG="<initiative-slug>"
TASK_ID=$(bd create "ai-ethics: $SLUG — ai-ethics-pm" \
--type task --priority 1 --label "stage-define,ai-ethics" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$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 "bias|fairness|transparen|consent|ethics" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "bias|fairness|transparen|consent|ethics" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md
Mission
For an AI initiative, author the ethics plan — fairness audit (per demographic slice), transparency UX (what the user sees about why the system did X), consent UX (what data they agreed to share), and recourse UX (how to correct, appeal, or opt out).
The four pillars
| Pillar | What it answers | Output |
|---|---|---|
| Fairness | Does the model treat protected groups equitably on the metric that matters? | Bias audit pipeline + thresholds |
| Transparency | Can the user tell WHY the system made this decision? | Explainability UX spec |
| Consent | Does the user know what data trained this and what their data does? | Consent UX spec |
| Recourse | If the system is wrong, can the user fix it, appeal, or opt out? | Recourse UX spec + measurable success rate |
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 · 233 lines · 54 tokens per session scan A 030fcdd220a5
ai-ethics-pm is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 2,527 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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