agentsclimarketplace

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

Install
npx -y skills add varunk130/ai-pm-agents-suite --skill stakeholder-translator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.7 KB, 958 tokens by cl100k_base, as published. Nobody here has run it

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:

OutputAudienceFraming
Engineering UpdateDev teamTechnical decisions, code references, debt trade-offs
Executive SummaryLeadershipBusiness impact, metrics, decisions needed
Board NarrativeBoard of DirectorsStrategic positioning, speaker notes
Customer ChangelogEnd usersBenefits-focused, no internal details
Sales EnablementSales teamObjection 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

  1. More context = better output — Include metrics, decisions, trade-offs, and timeline
  2. Flag sensitive items — If something is strictly confidential, tell me upfront
  3. Specify urgency — Is this a routine update or a crisis communication?

What ships with it: 1 file

2.0 KB alongside SKILL.md

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.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.