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 ai-analyst-lab/ai-analyst-plugin --skill stakeholder-communicationgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/stakeholder-communication)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/stakeholder-communication"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/stakeholder-communication/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/ai-analyst-lab/ai-analyst-plugin/stakeholder-communication"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/stakeholder-communication.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 79 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00096 | $0.03019 |
| Opus 5.5 | $0.00038 | $0.01208 |
| Sonnet 5.5 | $0.00019 | $0.00604 |
| Haiku 4.5 | $0.00010 | $0.00302 |
Grade A, and why
stakeholder-communication 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 2mo 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.
This is a copy
86% identical to stakeholder-communication — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Stakeholder Communication Matrix
Purpose
Adapt analytical findings to the audience — same insight, different framing, detail level, and format depending on who will read it. Ensures that executives get the bottom line, PMs get the implications, engineers get the specifics, and data teams get the methodology.
When to Use
Apply this skill when producing a narrative (Storytelling agent), creating a deck (Deck Creator agent), or whenever the user specifies an audience. If no audience is specified, default to Product Team format.
Instructions
Pre-flight: Load Learnings
Before executing, check .knowledge/learnings/index.md for relevant communication preferences:
- Try to read the file: Use the Read tool on
.knowledge/learnings/index.md - If it exists, scan for entries under these headings:
- "Communication" - stakeholder preferences, formatting conventions
- "General" - cross-cutting preferences that apply to all work
- "Stakeholder Preferences" - audience-specific guidance
- If it doesn't exist or is empty, skip silently and proceed with standard stakeholder matrix
- If entries exist, incorporate them as constraints (e.g., "VP prefers 1-page summaries", "always include confidence grades for data team")
- Never block execution if learnings are unavailable - this is an enhancement, not a blocker
Example learnings that would apply:
- "Executive team prefers dashboards over text reports"
- "Product team wants effort estimates in days, not story points"
- "Data team requires p-values for all statistical claims"
The Stakeholder Matrix
Different audiences care about different things. For the same finding, adapt:
| Dimension | Executive | Product Team | Engineering | Data Team |
|---|---|---|---|---|
| Lead with | Business impact ($, users, risk) | What to do about it (action) | What's broken and where (specifics) | How we found it (methodology) |
| Detail level | Bottom line + 1 supporting fact | Findings + implications + next steps | Root cause + technical details + fix scope | Methodology + data quality + caveats |
| Format | 3 slides max / 1-paragraph summary | Analysis report with charts | Investigation log with queries/code | Full report with validation section |
| Metrics language | Revenue, users, growth rate | Conversion, retention, engagement | Error rate, latency, success rate | Statistical significance, confidence intervals |
| Time horizon | This quarter / this year | This sprint / this month | This release / this deploy | This analysis / this dataset |
| Charts | 1-2 high-level (big number, trend) | 3-5 focused (funnel, segmentation) | Technical plots (timelines, error logs) | Distribution, correlation, validation |
| Caveats | Only if they change the recommendation | Noted alongside findings | Noted with technical implications | Full methodology section |
| Recommendation style | "We should X" (decisive) | "I recommend X because Y" (reasoned) | "The fix is X, effort is Y" (scoped) | "The data supports X with caveats Y" (qualified) |
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.
- 2mo ago First seen · 208 lines · 96 tokens per session scan A 097c9935a751
stakeholder-communication is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (34 stars, last pushed 2mo ago), licensed MIT. It adds 96 tokens to every session and 3,019 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to stakeholder-communication, differing in 15 lines, and is treated as a copy.
stakeholder-communication compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stakeholder-communicationai-analyst-lab/ai-analyst-plugin | Skill | 34 | 2mo ago | MIT | A | 96 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |