Your AI co-pilots for product management — translate updates for any audience, make data-backed decisions, and build financial models in seconds, not hours.
Three purpose-built agents that handle the grunt work so you can focus on strategy: a Stakeholder Translator, a Decision Engine, and a Financial Analyst — each designed to compress hours of work into seconds.
⚠️ Disclaimer: All data in this project is entirely synthetic and mock-generated for demonstration purposes. Customer names, company names, financial figures, market data, and all agent outputs are fictional. No real customer data, proprietary information, or actual business metrics were used.
Landing page — 3 specialized agents with one-click launch
Decision Engine — multi-framework strategic analysis with scoring methodology
Financial Analyst — assumption sourcing, key metrics, and sensitivity analysis
Stakeholder Translator — sensitivity classification with 5 audience-tailored outputs
Product managers spend a disproportionate amount of time on communication translation, decision structuring, and financial justification — not on the strategic thinking itself. A single product update requires five different versions, one per audience. A prioritization decision requires manually applying three or four frameworks. A feature business case requires hours of spreadsheet modeling.
This project demonstrates how purpose-built AI agents — each with a distinct cognitive role — can compress these workflows:
One product update → 5 audience-tailored communications (engineering, exec, board, customer, sales)
One strategic question → 4-framework analysis with synthesized recommendation
One feature description → Full financial model with sensitivity analysis and ship/no-ship decision
Each agent doesn't just generate text — it applies structured reasoning: sensitivity classification, multi-framework scoring, crossover analysis, pre-mortem scenarios, and audience-specific framing tuned to the actual decision being made.
Cognitive function: Audience Adaptation
Takes a single product update and produces five tailored communications — each with the right tone, detail level, technical depth, and framing for its audience. Includes sensitivity classification (Safe / Caution / Internal Only) so PMs know what's shareable and what's not.
| 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 |
Demo scenarios: AI feature launch, missed deadline communication, competitive response strategy.
Cognitive function: Strategic Reasoning
Applies four distinct analytical frameworks to a product decision, then synthesizes them into a single prioritized recommendation with confidence scoring and a pre-mortem analysis.
| Framework | What It Evaluates |
|---|---|
| Impact × Confidence Matrix | Revenue impact weighted by execution certainty |
| Strategic Alignment | Weighted scoring against company goals |
| Second-Order Effects | Downstream consequences (positive and negative) for each option |
| Pre-Mortem Analysis | "It's December and this failed — what went wrong?" with probability estimates |
Demo scenarios: Quarterly prioritization (3 competing initiatives), ship/iterate/sunset decision for an underperforming feature.
Cognitive function: Quantitative Modeling
Builds rigorous financial models from natural language inputs. Fills gaps with SaaS benchmarks, runs sensitivity analysis across multiple variables, and produces ship/no-ship/de-risk recommendations.
| Output | Description |
|---|---|
| Assumptions Table | Every input labeled by source (PM Input, SaaS Benchmark, Estimated) with edit affordance |
| Key Metrics Dashboard | Visual metric cards with color-coded status |
| Full Model | Unit economics, revenue projections, NPV, payback period |
| Sensitivity Matrix | Multi-variable sensitivity showing break-even boundaries |
| Decision Framework | Ship / Do Not Ship / De-risk with specific conditions for each |
Demo scenarios: Feature ROI analysis, TAM/SAM/SOM market sizing, pricing change impact modeling.
Each agent is available as a standalone Claude Code skill:
# Clone and copy all skills
git clone https://github.com/varunk130/pm-copilots.git
cp -r pm-copilots/skills/* ~/.claude/skills/
# Or copy individual skills
cp -r pm-copilots/skills/stakeholder-translator ~/.claude/skills/
cp -r pm-copilots/skills/decision-engine ~/.claude/skills/
cp -r pm-copilots/skills/financial-analyst ~/.claude/skills/Restart Claude Code after copying skills. Use via slash commands or natural language prompts.
- Structured reasoning over free-form generation: Each agent applies named analytical frameworks (Impact × Confidence, Pre-Mortem, Crossover Analysis) rather than open-ended generation
- Sensitivity classification as a first-class concept: The Stakeholder Translator classifies information by sensitivity level before generating — knowing what not to say to which audience
- Multi-framework convergence: The Decision Engine applies four independent frameworks and checks whether they converge — divergence surfaces genuine uncertainty
- Explicit assumption sourcing: The Financial Analyst labels every model input with its source (user-provided, benchmark, estimated) — making it visible where confidence is high vs. interpolating
- Audience-aware generation at scale: Five distinct communications from one input, each correct for its audience
pm-copilots/
├── README.md
├── screenshots/
│ ├── ai-pm-dashboard.png
│ ├── decision-engine.png
│ ├── financial-analyst.png
│ └── stakeholder-translator.png
└── skills/
├── stakeholder-translator/
│ └── SKILL.md
├── decision-engine/
│ └── SKILL.md
└── financial-analyst/
└── SKILL.md
MIT — see LICENSE for details.
Built by Varun Kulkarni
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