Stakeholder translator
Skill varunk130/ai-pm-agents-suite/agents/stakeholder-translator
Generate 5 audience-tailored communications from one product update. Use when: stakeholder update, executive summary, board narrative, customer changelog, sales enablement, translate for audience, multi-audience communication, sensitivity classification.From its SKILL.md
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SKILL.md
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Stakeholder Translator
Transform a single product update into five audience-tailored communications — each with the right tone, detail level, technical depth, and framing. Includes sensitivity classification so you know what's shareable.
Output
Save to outputs/stakeholder-[topic]-[YYYY-MM-DD].md
When to Use
- Writing a product update that needs to reach multiple audiences
- Preparing exec summaries alongside engineering updates
- Creating board narratives from sprint outcomes
- Generating customer changelogs without leaking internal details
- Arming sales with talk tracks and objection handling
What You'll Get
Five distinct communications from one input:
| Output | Audience | Framing |
|---|---|---|
| Engineering Update | Dev team | Technical decisions, code references, debt trade-offs |
| Executive Summary | Leadership | Business impact, metrics, decisions needed |
| Board Narrative | Board of Directors | Strategic positioning, speaker notes |
| Customer Changelog | End users | Benefits-focused, no internal details |
| Sales Enablement | Sales team | Objection handling, competitive positioning, talk tracks |
Plus:
- Sensitivity Classification for each section (✅ Safe / ⚠️ Caution / 🔒 Internal Only)
- Key Message Consistency Check — ensures the core narrative is coherent across all five versions
Process
Step 1: Collect the Update
I'll ask for:
"What's the product update? Paste your internal notes, sprint summary, or a rough description of what happened. Include any context about timeline, metrics impact, or decisions made."
Step 2: Classify Sensitivity
Before generating anything, I'll classify each piece of information:
- ✅ Safe — Can be shared with any audience including customers
- ⚠️ Caution — OK for internal audiences, needs careful framing for external
- 🔒 Internal Only — Engineering and leadership only, never external
Step 3: Generate All Five Outputs
Each output follows audience-specific rules:
- Engineering: Include technical specifics, architecture decisions, known debt
- Executive: Lead with business impact, include metrics, end with decisions needed
- Board: Strategic framing, market context, forward-looking narrative with speaker notes
- Customer: Benefits only, no internals, action items for users
- Sales: Competitive angles, objection handling scripts, customer-facing talk tracks
Step 4: Consistency Check
I'll verify the core message is consistent across all five versions — same facts, different framing.
Demo Scenario: AI Feature Launch
Input:
"We shipped the AI-powered search feature. It uses embeddings + hybrid search. Took 3 sprints instead of 2 because of infra issues. Early metrics show 40% improvement in search relevance. We cut the advanced filtering feature to hit the deadline. Competitor X launched something similar last week."
Sample Engineering Output:
## Engineering Update: AI Search v1 Shipped 🚀
### What Shipped
- Hybrid search (BM25 + vector embeddings) using our new embedding pipeline
- New search index architecture — migrated from Elasticsearch to custom solution
- Search relevance improved 40% (measured by nDCG@10 on test queries)
### What We Cut
- Advanced filtering (date ranges, custom facets) — descoped to hit Q1 deadline
- Will pick up in next sprint as standalone work item
### Tech Debt Created
- Embedding pipeline runs synchronously — needs async job queue before scale
- No A/B testing framework for search yet — metrics are pre/post comparison only
### Timeline
- Originally scoped for 2 sprints, took 3 due to infra migration issues
- Root cause: staging environment parity — documented in retro
Sample Customer Changelog:
## 🔍 Smarter Search is Here
Finding what you need just got a lot easier. Our new AI-powered search understands
what you mean, not just what you type.
### What's New
- **Smarter results** — Search now understands context and intent
- **40% more relevant results** in our testing
### Coming Soon
- Advanced filters for date ranges and custom categories
Try it now → [Search anything]
Tips
- More context = better output — Include metrics, decisions, trade-offs, and timeline
- Flag sensitive items — If something is strictly confidential, tell me upfront
- Specify urgency — Is this a routine update or a crisis communication?
What ships with it: 1 file
2.0 KB alongside SKILL.md
- README.md2.0 KB
Gives 0 of the 12 instructions most operations skills give in 958 tokens
Counted across 483 of the 484 authors here whose files we hold, read 2026-08-07
- Collect monitoring data throughout the simulationin 14 of 483, across 6 files
- Set the random seed for reproducibilityin 14 of 483, across 6 files
- Validate simulations against analytical solutionsin 12 of 483, across 4 files
- Clarify goals, constraints, and inputsin 11 of 483, across 2 files
- Implement contract tests for integration pointsin 11 of 483, across 2 files
- Implement strangler fig infrastructure with API gatewayin 11 of 483, across 2 files
- Audit modernized components for security vulnerabilitiesin 11 of 483, across 2 files
- Avoid Python blocking calls in processesin 10 of 483, across 3 files
- Use resource context managers for automatic cleanupin 9 of 483, across 2 files
- Maintain consistent time unitsin 9 of 483, across 2 files
- Validate outcomes against success criteriain 8 of 483, across 1 file
- Analyze the legacy codebase for technical debtin 8 of 483, across 1 file
Said here and by no other author read
- save output using the specified file naming format
- classify sensitivity of each piece of information before generating outputs
- generate five distinct audience-tailored communications from one input
- include technical specifics and architecture decisions for engineering
- lead executive summary with business impact and metrics
- provide strategic framing and speaker notes for board narrative
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.