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/stefanoskarakasis/Product-Marketing-SkillsWrote 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/commands/stefanoskarakasis/product-marketing-skills/customer-stories)<a href="https://agentmods.dev/commands/stefanoskarakasis/product-marketing-skills/customer-stories"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/customer-stories/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/commands/stefanoskarakasis/product-marketing-skills/customer-stories"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-skills/customer-stories.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 | $0.00031 | $0.00524 |
| Opus 5.5 | $0.00012 | $0.00210 |
| Sonnet 5.5 | $0.00006 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
customer-stories 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 5d 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.
What it actually says
/pmm-growth:customer-stories -- Customer Stories
Run a customer story from kickoff to customer approval in a fixed seven-block structure. You pick the depth (short, standard, deep) and the angle; the skill finds the tension, asks who does what instead of assuming, refuses to invent evidence, and runs an 8-point gate before handing the draft over. Also writes the internal brief, the interview questions, the customer review pack, and audits a finished story.
Invocation
/pmm-growth:customer-stories We want a story on Harbor & Pine. Start the kickoff
/pmm-growth:customer-stories I have a call with Harbor & Pine on Thursday
/pmm-growth:customer-stories Write a short story from these notes: [paste]
/pmm-growth:customer-stories Prepare the review pack for Harbor & Pine
/pmm-growth:customer-stories Audit this case study our agency sent: [paste]
Workflow
Uses the customer-stories skill. Loads brain Sections 2, 3, 4 and 6 if
present, loads the story ledger and the story's stage, asks you for the names
and departments of the people involved (never assumes them), runs kickoff and
drafts the internal brief in the format you choose, picks Interview, Draft,
Review pack or Audit from what you paste, builds a packet of stats and quotes with every missing field
flagged, asks you to choose depth, proposes two or three angles with their
tension, drafts in the fixed structure with placeholders for any gap, runs
the 8-check gate on its own draft, saves the packet with its stage and next action to
/context/customer-stories.md, offers to hand confirmed claims to
proof-points, and closes with a session log to /context/skill-sessions.md.
Commands
/kickoff— the six kickoff questions and the internal brief/interview— the question list for a customer call/draft— packet, depth, angle and draft from notes/review-pack— cover note, checklist and asset list for the customer/audit— gate check and ranked fixes for a finished story
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.
- 5d ago First seen · 45 lines · 31 tokens per session scan A ff6af1e020d8
customer-stories is a command published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (7 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 524 once invoked, about $0.0001 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 2026-10-11.
Other commands, from other repositories
brief-me
Get briefed — loads your full memory, pulls live state from all connected tools, surfaces risks, staleness, upcoming milestones, and gives you a prioritized briefing so you're never starting blank.
design-ai-feature
Design an AI-powered feature end-to-end — model selection, prompt architecture, eval framework, failure modes, cost modeling, and improvement flywheel.
competitive-intel
Run a competitive intelligence analysis — landscape mapping, battlecards, 7 Powers moat comparison, positioning gaps, and monitoring plan.
discover
Run a full discovery cycle — problem framing, JTBD demand-side analysis, assumption mapping, opportunity sizing, and OST mapping — from a rough idea to validated opportunity.
plan-launch
Plan a full go-to-market launch — positioning, messaging hierarchy, ICP definition, launch timeline, rollback plan, and post-launch measurement.
retro
Run a post-ship retrospective — measure outcomes vs. predictions, extract lessons, update memory, and feed insights back into the PM system.