--- name: stakeholder-communication-generator-for-technical-policy-decisions description: "Translates technical enforcement or AI system decisions into plain-language stakeholder communications with a decision summary, policy impact analysis, risk framing, precedent notes, and a recommended action for non-technical policy and legal reviewers. Use when a product manager needs to explain a model change, enforcement threshold adjustment, or automated decision to policy, legal, trust & safety, or executive stakeholders who don't read technical documentation." argument-hint: "[technical-decision] [audience]" allowed-tools: ["Read"] --- # Stakeholder Communication Generator for Technical Policy Decisions ## Purpose Convert a technical system change — a model update, enforcement threshold adjustment, classifier retraining, or automated decision logic change — into a structured, plain-language communication that policy and legal stakeholders can review, question, and approve without needing to understand the underlying engineering. Solves the recurring gap where technical decisions stall in cross-functional review because reviewers can't map system behavior to policy risk. ## When to Use Invoke this skill when the user asks to: - Explain a model, classifier, or algorithm change to non-technical policy or legal stakeholders - Write an enforcement change notification for a trust & safety or policy team - Summarize the policy impact of an automated decision or AI system behavior - Prepare a technical decision for cross-functional review or executive sign-off - Draft a communication that translates false positive/negative tradeoffs into policy consequences - Document a threshold, rule, or logic change for a legal or compliance audience ## Inputs Required Before starting, collect: - **The technical change**: What is changing in the system — the model, threshold, rule, or logic (in the PM's own words) - **Why it's changing**: The problem being solved or the trigger for the change - **Audience**: Who receives this — policy team, legal, trust & safety leadership, executives, or a mix - **Behavioral impact**: What users or content will experience differently (more enforcement, fewer removals, faster review, etc.) - **Known tradeoffs**: Any expected shift in false positives/negatives, appeal volume, or coverage gaps - **Metrics available**: Any before/after numbers — precision, recall, volume, error rates (optional but strongly used if present) - **Decision needed**: Whether this is informational (FYI) or requires stakeholder approval If metrics aren't available, produce qualitative impact framing and flag which metrics would strengthen the case. ## Instructions ### Step 1: Extract the Decision in One Sentence Compress the technical change into a single plain-language sentence a policy lawyer could repeat accurately. Strip all engineering jargon. - Replace terms like "classifier," "model," "threshold" with functional descriptions: what the system now catches, misses, or does differently - The sentence must answer: *what changed and what will now happen differently* - Test: could a reviewer paraphrase this correctly to their manager after one read? ### Step 2: Build the Policy Impact Analysis Map the technical behavior to policy consequences across three dimensions: **Coverage** — what the system now enforces or stops enforcing - Which policy areas or violation types are affected - Whether the change tightens or loosens enforcement **Error profile** — who gets affected by mistakes - Translate false positives into "users wrongly actioned" and false negatives into "violations that slip through" - Frame the tradeoff in human terms, not statistical terms: "this catches ~15% more violations but wrongly flags roughly 1 in 200 legitimate posts" **Affected populations** — who experiences the change - User segments, content types, or geographies disproportionately impacted - Any group with elevated appeal or fairness sensitivity ### Step 3: Frame the Risk Honestly Reviewers trust communications that name the downside. For each material risk, state: - **The risk**: what could go wrong in plain language - **Likelihood**: rough qualitative assessment (low / moderate / elevated) - **Mitigation**: what's in place or planned to contain it - **Reversibility**: whether the change can be rolled back quickly if it misfires Do not soften or bury risk — legal reviewers escalate hidden risk far harder than disclosed risk. ### Step 4: Add Precedent and Consistency Notes Situate the decision against prior decisions: - Whether this aligns with, extends, or departs from existing enforcement precedent - Any external commitments (transparency reports, regulatory filings, public policy statements) the change touches - Whether it creates a precedent future decisions will be measured against ### Step 5: State the Recommended Action and What's Needed Close with the ask: - If informational: state clearly that no action is required and by when concerns should be raised - If approval is needed: state exactly what decision is being requested, the deadline, and what happens if no response is received - List the specific questions stakeholders are best positioned to answer ## Output Format - **Structure**: Titled communication with a bolded one-line decision summary at the top, followed by labeled sections - **Length**: 500–800 words - **Format**: Structured document — short paragraphs and scannable labeled sections; error tradeoffs may use a compact table - **Sections**: Decision Summary (one line) → Why This Is Changing → Policy Impact → Risk & Mitigation → Precedent & Consistency → Recommended Action / What We Need From You - **Tone**: Calm, direct, non-defensive. Written as a product manager who respects the reviewer's expertise and doesn't hide behind technical language or optimism ## Quality Checklist Before delivering output, verify: - [ ] The one-line decision summary contains zero engineering jargon (no "model," "classifier," "threshold" without a functional gloss) - [ ] Error tradeoffs are stated in human terms (users affected, violations missed) — not raw statistical terms alone - [ ] At least one material risk is named with likelihood, mitigation, and reversibility - [ ] Affected populations are identified specifically — not "some users" - [ ] The Recommended Action states clearly whether approval is required and by when - [ ] Precedent section connects the decision to prior enforcement or external commitments ## Notes - The most common failure is under-disclosing risk to get faster approval — this backfires; disclosed risk earns trust and reversibility earns approval - When metrics are strong, lead the impact section with them; when metrics are weak or absent, lead with qualitative framing and explicitly flag the measurement gap - This communication is reusable as a template across enforcement change notifications — keep the section structure stable so stakeholders learn where to find risk and the ask every time - For legal-heavy audiences, precedent and reversibility carry more weight than performance metrics; for executive audiences, lead with the decision summary and the ask --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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