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 stefanoskarakasis/Product-Marketing-Skills --skill stakeholder-mapsgit 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/skills/stefanoskarakasis/product-marketing-skills/stakeholder-maps)<a href="https://agentmods.dev/skills/stefanoskarakasis/product-marketing-skills/stakeholder-maps"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/stakeholder-maps/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/stefanoskarakasis/product-marketing-skills/stakeholder-maps"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/stakeholder-maps.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 | $0.00074 | $0.03377 |
| Opus 5 | $0.00037 | $0.01688 |
| Sonnet 5 | $0.00015 | $0.00675 |
| Haiku 4.5 | $0.00007 | $0.00338 |
Grade A, and why
stakeholder-maps 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 today.
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 — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stakeholder-Maps — Skill
How This Works
Stakeholder maps built in a vacuum are a waste of time. This skill builds a map anchored to political reality: who has power, who's a hidden champion, who's a silent blocker, and what to do before you're in the room.
The skill runs in 7 steps:
Step 0 — Load brain context (ICP, positioning, GTM motion) and guardrails from /context/meta-patterns.md if it exists (e.g., "product launches without Sales Manage Closely engagement fail 80% of the time" — an example pattern shape, not a claim this skill has already observed).
Step 1 — Intake: Initiative description, success definition, timeline, and current champions/blockers.
Step 2 — Inversion: Who could sink this from inside? (If you can't name them, the map is assumed, not validated.)
Step 3 — Classification: Place each stakeholder on Power × Interest grid; assign political role (Champion, Blocker, Frozen, Gatekeeper, Performer, Floating Voter).
Step 4 — Conflict mapping: Identify stakeholder conflicts; define second-order risks and resolution owners.
Step 5 — Silent blocker scan: Which functions/roles aren't in the room but should be?
Step 6 — Output: HTML widget (grid + comms plan + conflict map + confidence assessment) + markdown diagnostic + Sprint Cards.
Step 7 — Learning Close: log the session to /context/skill-sessions.md.
Trigger
- When: Building a political stakeholder map for an initiative — who can kill it, who champions it, and what to say to each person.
- Not for: Campaign messaging → use
gaccs-briefafter this skill. Risk analysis → usepre-mortembefore this skill. GTM channel design → usego-to-market-strategy. - Example prompts:
- "Who are the stakeholders for this launch?"
- "Map the politics on this initiative"
- "Who could block this internally?"
- "Build a stakeholder map"
Inputs
- Args: Initiative description, success definition, timeline, current champions/blockers. Free format — Step 1 intake fills gaps conversationally.
- Defaults: If brain context is unavailable, this skill still runs — brain sharpens which stakeholders matter and what power they hold, but is not a hard blocker the way it is for
go-to-market-strategy. - Context keys:
/foundation/brain.md— optional but recommended. Sections 2 (ICP), 3 (Positioning), 5 (GTM Motion)./context/meta-patterns.md— optional; recurring patterns the user has logged from prior stakeholder mapping sessions.- Brain contract: Reads Sections 2, 3, 5. Writes: none — this skill does not write to
/foundation/brain.md.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed · +2 lines ad5dfaa3ffa8
- 12d ago First seen · 315 lines · 74 tokens per session scan A 2643e479acb6
stakeholder-maps is a skill published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (5 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 3,377 once invoked, about $0.0004 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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