--- name: cross-functional-friction-identifier description: "Analyzes retrospective notes, survey responses, and interview inputs to surface recurring friction points and handoff breakdowns between functions, then delivers a ranked friction map with root-cause diagnosis and specific interventions. Use when diagnosing why cross-team work stalls, mapping handoff failures between departments, or turning retrospective and survey data into an actionable alignment plan." argument-hint: "[teams] [input-data]" allowed-tools: ["Read"] --- # Cross-Functional Friction Identifier ## Purpose Turn messy team feedback — retro notes, survey verbatims, interview snippets — into a ranked map of where cross-functional work breaks down, with root-cause diagnosis and specific interventions for each friction point. Solves the problem of "everyone knows things are slow at the handoffs, but no one can name exactly where or why." ## When to Use Invoke this skill when the user asks to: - Analyze retrospective or survey data to find recurring team friction - Diagnose why cross-functional projects keep stalling or slipping - Map handoff breakdowns between departments (e.g., Sales→CS, Product→Eng, Design→Eng) - Synthesize interview or feedback themes into an alignment action plan - Prepare a friction summary for a leadership offsite or planning cycle - Identify which team seams cost the most time or rework ## Inputs Required Before starting, collect: - **Functions involved**: The teams or departments whose interactions are being analyzed - **Input data**: The raw material — retro notes, survey responses, interview quotes, Slack threads, or postmortems (paste or describe) - **Time period**: What window the data covers (last quarter, last 6 months, single project) - **Known pain areas**: Any friction the user already suspects, to validate or challenge - **Recent org changes**: Reorgs, new tools, headcount shifts, or process changes in the period - **Primary outcome**: What the user wants to fix (speed, rework, morale, missed handoffs) If input data is thin, note the confidence limitation and flag which conclusions are provisional versus well-supported. ## Instructions ### Step 1: Extract and Tag Friction Signals Read all input data and pull every distinct friction signal. For each, tag: - **The seam**: Which two functions the friction sits between (or "internal" if within one team) - **The moment**: Where in the workflow it occurs — handoff, dependency wait, decision gate, feedback loop, or ownership gap - **The signal type**: Complaint, delay, rework event, escalation, or blocked work - **Frequency**: How many independent inputs mention this (count them — repetition is signal) Deduplicate near-identical signals but preserve the count. A friction mentioned by six people is not the same as one mentioned once. ### Step 2: Cluster Into Friction Points Group tagged signals into distinct friction points. A friction point is a recurring, nameable breakdown — not a one-off. Give each a plain-language name (e.g., "Design handoffs arrive without acceptance criteria" not "communication issues"). Aim for 5–9 named friction points; if you have more, the clustering is too granular. ### Step 3: Diagnose Root Cause by Category Classify each friction point's root cause into one of five categories: - **Ownership gap**: No one is accountable for the handoff or the space between teams - **Information gap**: The receiving team lacks context, spec, or data to act - **Process gap**: No defined trigger, SLA, or checklist for the handoff - **Tooling/visibility gap**: Work is invisible across the seam; no shared source of truth - **Incentive misalignment**: Teams are measured on conflicting goals that pit them against each other State the category explicitly and give one sentence of evidence from the data. Distinguish symptom from cause — "Eng deprioritizes CS requests" is a symptom; "CS and Eng have no shared prioritization ritual" is the cause. ### Step 4: Score and Rank Each Friction Point Score each friction point on three dimensions (High/Medium/Low): - **Frequency**: How often it recurs (from Step 1 counts) - **Cost**: Time lost, rework created, or deals/deadlines at risk - **Fixability**: How tractable the fix is in the next quarter Rank the friction points by a combined priority, weighting Frequency × Cost most heavily, with Fixability as the tiebreaker. Surface "high-leverage quick wins" (High cost, High fixability) separately. ### Step 5: Prescribe a Specific Intervention Per Friction Point For each friction point, write one concrete intervention matched to its root-cause category: - Ownership gaps → name a single accountable owner or DRI for the seam - Information gaps → define the required handoff artifact (spec, brief, acceptance criteria) - Process gaps → propose the ritual, trigger, or SLA to install - Tooling gaps → name the shared view or single source of truth to establish - Incentive misalignment → propose the shared metric or joint goal Each intervention must name what changes, who owns it, and how success is measured. No "improve communication." ### Step 6: Build the 30-60-90 Action Plan Sequence the interventions: - **First 30 days**: Quick wins and the single highest-priority fix - **Days 31–60**: Process and artifact changes requiring cross-team buy-in - **Days 61–90**: Structural or incentive changes needing leadership sponsorship ## Output Format - **Structure**: Ranked friction map as a table, followed by per-friction-point diagnosis and intervention, then the phased action plan - **Length**: 900–1,300 words - **Format**: Summary table + structured sections per friction point + sequenced plan - **Sections**: Executive summary (3–4 sentences) → Ranked friction map table → Per-friction-point diagnosis & intervention → Quick wins → 30-60-90 action plan → Confidence & data gaps note - **Tone**: Chief of staff analytical — neutral about people, direct about systems, evidence-anchored, non-blaming ## Quality Checklist Before delivering output, verify: - [ ] Every friction point is named in plain language, not a vague category - [ ] Each friction point cites frequency count from the input data - [ ] Every root cause is classified into one of the five categories with evidence - [ ] The ranking is driven by Frequency × Cost — not listed arbitrarily - [ ] Each intervention names what changes, who owns it, and how success is measured - [ ] Diagnosis distinguishes symptom from cause (no symptom labeled as root cause) - [ ] A confidence note flags any conclusion drawn from thin or single-source data ## Notes - The most common misdiagnosis is calling an ownership gap a "communication problem" — friction at seams is almost always structural, not interpersonal - Frequency count is the strongest signal in messy data; weight what's repeated across independent sources over what's stated most emphatically once - Keep the analysis system-focused, not person-focused — naming individuals as the problem kills adoption of the fixes - If the user has only one data source (e.g., one retro), state clearly that patterns are directional and recommend a second input type before major structural changes --- ## 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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