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.
npx skills add marfoerst/the-pragmatic-pm --skill pm-stakeholder-simulatorgit clone --depth 1 https://github.com/marfoerst/the-pragmatic-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/skills/marfoerst/the-pragmatic-pm/pm-stakeholder-simulator)<a href="https://agentmods.dev/skills/marfoerst/the-pragmatic-pm/pm-stakeholder-simulator"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-stakeholder-simulator/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/skills/marfoerst/the-pragmatic-pm/pm-stakeholder-simulator"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-stakeholder-simulator.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 | $0.00068 | $0.01401 |
| Opus 5 | $0.00034 | $0.00700 |
| Sonnet 5 | $0.00014 | $0.00280 |
| Haiku 4.5 | $0.00007 | $0.00140 |
Grade A, and why
pm-stakeholder-simulator 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM Stakeholder Simulator — Surface Objections Before the Meeting
You simulate how key stakeholders would react to a product proposal. Your job is to be the tough room before the real room — surfacing objections, concerns, and perspectives the PM might not have considered. Read domain-context.md for company and industry context. Also read personal-context.md if available to adapt the simulation to the user's organizational dynamics and seniority.
Intent Detection
Activate this skill when the user:
- Asks "how will [person/role] react?" to a proposal
- Wants a "stakeholder analysis" or "stakeholder simulation"
- Says "what objections will I face?" before a meeting
- Asks to "prepare me for the meeting" or "simulate the room"
- Needs to anticipate pushback on a product decision or change
Step 1: Gather Context
Ask the user:
- What's the proposal? (feature, strategy change, deprecation, pricing change, etc.)
- Who will be in the room? (roles/names — e.g., CEO, VP Engineering, Head of Sales, Head of CS, Compliance Officer)
- What's the ask? (approval, resources, alignment, buy-in)
- Any known sensitivities? (past conflicts, political dynamics, personal stakes)
Step 2: Simulate Each Stakeholder
For each stakeholder, generate their likely reaction:
### [Role/Name]: [Emoji reaction: 👍 Supportive / 🤔 Cautious / 👎 Resistant / ❓ Needs More Info]
**Their primary concern:** [What they care most about]
**Likely reaction:** [2-3 sentences in their voice — what they'd actually say]
**Key objection:** [The strongest pushback they'd give]
**What would win them over:** [What evidence, framing, or concession would address their concern]
Standard Stakeholder Perspectives
If the user doesn't specify stakeholders, simulate these archetypes:
| Stakeholder | Primary Lens |
|---|---|
| CEO / GM | Strategic alignment, market impact, resource trade-offs |
| VP Engineering | Technical feasibility, maintenance burden, team capacity |
| Head of Sales | Revenue impact, competitive positioning, sales enablement |
| Head of CS | Customer impact, support burden, churn risk |
| Finance | ROI, cost, timeline to value |
| Compliance / Legal | Regulatory risk, data privacy, audit implications |
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 · 127 lines · 68 tokens per session scan A 62b5cf27abdf
pm-stakeholder-simulator is a skill published in the GitHub repository marfoerst/the-pragmatic-pm (8 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 1,401 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 skills, from other repositories
forge-execute
Guided plan execution — list available plans, estimate cost, choose mode, and execute with live progress. Use when you want to run a hardened plan through the orchestrator.
source-command-cycle
Execute continuous development cycle for an epic.
source-command-story
Generate implementation stories from planning documents.
source-command-plan
Generate scale-adaptive planning documents (PRD and architecture).
Block Close
Update block documentation after completing work on a feature block. Run this after finishing work to close the feedback loop.
ai-pm-prompts
A structured library of AI prompts for Product Managers covering the full product lifecycle. Use this skill whenever a PM needs help with customer interviews, research synthesis, market analysis, competitive intelligence, capability gap analysis, ideation, idea evaluation, Kano analysis, MoSCoW prioritization, RICE…