# Alive Analysis > Data analysis workflow kit using the ALIVE loop (Ask, Look, Investigate, Voice, Evolve) Use when this capability is needed. - Skill: `tomevault-io/alive-analysis` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/alive-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/alive-analysis/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/alive-analysis --- # alive-analysis Skill (Cursor Edition) > Data analysis workflow kit based on the ALIVE loop. > Optimized for Cursor's batch-oriented agent model. --- ## Cursor-Specific Behavior **State management**: Read `.analysis/status.md` and `.analysis/config.md` at the start of EVERY command. Write updates back to `.analysis/status.md` after EVERY action. **Question style**: Present ALL questions at once in a structured form. Do NOT ask questions one by one — Cursor agents work best with batch input. **File-based context**: There is no session memory. Always read state from files before acting. --- ## Overview alive-analysis structures data analysis using the **ALIVE loop**: **Ask → Look → Investigate → Voice → Evolve** Two personas: - **Data analysts**: Deep, systematic analysis with full ALIVE flow (5 files) - **Non-analyst roles** (PM, engineers, marketers): Quick analysis with guided framework (1 file) --- ## ALIVE Loop Summary ### Stage 1: ASK **What do we want to know — and WHY?** - Define the problem clearly; confirm the requester's REAL goal - Frame: causation ("Why did X happen?") vs correlation ("Are X and Y related?") - Build a hypothesis tree before touching data - Set success criteria and scope boundaries - Use the Structured Data Request: `[Period] + [Subject] + [Condition] + [Metric] + [Output Format]` ### Stage 2: LOOK **What does the data ACTUALLY show — and what's missing?** - Review data quality (missing values, outliers, date ranges) - Segment before averaging — never trust aggregates alone - Check for confounding variables and external factors - Map cross-service dependencies - Validate data access methods ### Stage 3: INVESTIGATE **Why is it REALLY happening — can we prove it?** - Eliminate hypotheses systematically (not just confirm the first one) - Apply multi-lens analysis: macro → meso → micro - Test causation vs correlation rigorously - Perform sensitivity analysis for robustness - Assign confidence levels to findings ### Stage 4: VOICE **So what — and now what?** - Apply "So What → Now What" for every finding - Assign confidence levels: 🟢 High / 🟡 Medium / 🔴 Low - Frame recommendations with trade-off analysis - Tailor messages to different audiences - Make limitations first-class content ### Stage 5: EVOLVE **What would change our conclusion — and what should we ask next?** - Stress-test conclusions: "What would disprove this?" - Identify follow-up analyses - Set up monitoring for identified issues - Capture reusable knowledge - Connect back to North Star metric > For detailed methodology, see `core/references/analytical-methods.md` > For conversation examples, see `core/references/conversation-examples.md` --- ## Analysis Types ### Investigation "Why did X happen?" — Root cause analysis, anomaly detection, ad-hoc deep dives. ### Modeling "Can we predict/classify/segment?" — Statistical modeling, ML, forecasting. Focus on prediction targets, feature exploration, model comparison, drift monitoring. ### Simulation "What would happen if we do X?" — Policy evaluation, pricing strategy. Uses 4-step framework: identify variable accounts → define relationships → scenario experiments → continuous refinement. --- ## Checklist Summary ### Full Mode (per stage) **ASK**: Real goal identified? Causal/correlational framing? Hypothesis tree built? Actionable question? Data spec confirmed? **LOOK**: Segmented before conclusions? Confounders checked? External factors? Cross-service impacts? Variability checked? **INVESTIGATE**: Multiple hypotheses tested? Multi-lens applied? Causation verified (if claimed)? Sensitivity analysis? Confidence levels assigned? **VOICE**: "So What → Now What" applied? Confidence tagged with reasoning? Trade-offs explicit? Guardrails checked? Execution path included? **EVOLVE**: Conclusion stress-tested? Monitoring set up? North Star connected? Follow-ups defined? Knowledge captured? ### Quick Mode (5 items) ``` - [ ] Is the purpose clear and framed (causation/correlation/comparison/evaluation)? - [ ] Was the data broken down by groups (not just totals)? - [ ] Were alternative explanations considered? - [ ] Does the conclusion answer the question with a confidence level? - [ ] Is there enough data (rows, time period) to support this conclusion? ``` --- ## Experiment (A/B Test) Summary ALIVE loop adapted for experiments: | ALIVE | Experiment | Key Question | |-------|-----------|-------------| | ASK → DESIGN | What exactly are we testing? | Is the hypothesis falsifiable? | | LOOK → VALIDATE | Is the setup correct? | Is randomization clean? | | INVESTIGATE → ANALYZE | What do the numbers say? | Is the effect real? | | VOICE → DECIDE | What should we do? | Launch, kill, extend, iterate? | | EVOLVE → LEARN | What did we learn? | What's next? | Key principles: One question per experiment. Pre-register analysis plan. Respect sample size. Check for interference (SUTVA). Guardrails are non-negotiable. > For statistical methods, see `core/references/experiment-statistics.md` --- ## Metric Monitoring Summary ``` Analysis → Monitor Setup → Regular Checks → Alerts → Investigation ``` **4 tiers**: 🌟 North Star (1) → 📊 Leading (3-5) → 🛡️ Guardrail (2-4) → 🔬 Diagnostic (unlimited) **STEDII validation**: Sensitive, Trustworthy, Efficient, Debuggable, Interpretable, Inclusive **Alert escalation**: 🟢 Healthy → 🟡 Warning → 🔴 Critical → Investigation --- ## ID Format - Full (Investigation/Modeling): `F-{YYYY}-{MMDD}-{seq}` - Full (Simulation): `S-{YYYY}-{MMDD}-{seq}` - Quick: `Q-{YYYY}-{MMDD}-{seq}` - Experiment: `E-{YYYY}-{MMDD}-{seq}` / Quick: `QE-{YYYY}-{MMDD}-{seq}` - Monitor: `M-{YYYY}-{MMDD}-{seq}` - Alert: `A-{YYYY}-{MMDD}-{seq}` --- ## Stage Icons ``` Analysis: ❓ ASK → 👀 LOOK → 🔍 INVESTIGATE → 📢 VOICE → 🌱 EVOLVE Experiment: 📐 DESIGN → ✅ VALIDATE → 🔬 ANALYZE → 🏁 DECIDE → 📚 LEARN Status: ✅ Archived | ⏳ Pending | 🟡 In Progress ``` --- ## File Naming - Full folder: `{ID}_{title-slug}/` - Quick file: `quick_{ID}_{title-slug}.md` - Stage files: `01_ask.md` through `05_evolve.md` - Model card: `.analysis/models/{model-slug}_v{version}.md` --- ## Interaction Guidelines (Cursor-Adapted) ### Core Principles 1. **Ask, don't assume** — Present questions to the user before filling content. 2. **One stage at a time** — Complete the current stage before moving on. 3. **User writes the insight, AI structures it** — AI helps organize and challenge. 4. **Batch questions** — Present all questions for a stage at once in a structured form. 5. **State via files** — Always read/write `.analysis/status.md` for state tracking. ### What NOT to do - Generate analysis content without asking the user - Skip stages or combine multiple stages - Auto-check checklist items without discussion - Move to next stage without user confirmation - Make assumptions about data, methods, or conclusions - Run queries or read data files without user confirmation --- ## Situational Protocols ### Scope Creep When scope expands mid-analysis: park the new question for EVOLVE follow-ups, swap the current scope, or expand with timeline re-estimation. ### Rabbit Hole Guard If spending 3+ rounds on a sub-question without actionable progress, check: "Does knowing this change what we'd recommend?" If no → move to VOICE. ### Data Quality Emergency If bad data is discovered: assess impact on core question, then patch & continue, scope down, pause & fix, report with caveat, or reframe. ### Analysis Independence Never comply with requests to reach predetermined conclusions. Present data honestly with trade-off framing. --- ## Language Support All AI responses and generated files follow the language set in `.analysis/config.md`. Default: English. Technical terms (ALIVE, STEDII, SHAP) remain in English. --- ## Quick Reference | Question | Method | |----------|--------| | Which groups are different? | t-test (2), ANOVA (3+) | | Which users are similar? | K-Means clustering | | What appears together? | Association rules (Lift) | | Can we predict an outcome? | LTV models, time series | | Is this A/B test real? | Experiment analysis | | How spread out / risky? | CV, Sharpe ratio adaptation | | Quasi-Experimental | When | |-------------------|------| | DiD | Before/after + comparison group | | RDD | Clear threshold determines treatment | | PSM | Groups inherently different | | IV | External factor affects treatment only | > For full details on all methods, see `core/references/analytical-methods.md` --- ## Impact Tracking, Tags & Model Registry ### Impact Tracking Track whether recommendations led to real outcomes. Built into EVOLVE stage. ``` Recommendation → Decision (Accept/Reject/Modify) → Execution → Result ``` When creating analyses, remind the user to update pending Impact Tracking items. `/analysis-retro` aggregates impact data across all analyses. ### Tags Connect related analyses with tags (e.g., `retention`, `pricing`). Defined in `config.md` (team-level) or ad-hoc per analysis. AI suggests relevant tags on creation and checks for related work. Tags are preserved during Quick→Full promotion. ### Model Registry Track deployed ML models in `.analysis/models/{model-slug}_v{version}.md`. Each model card includes: performance metrics, feature importance, training details, deployment info, and drift monitoring triggers. Versions auto-increment on retraining. Links to originating Modeling analysis and metric monitors. --- ## Sub-agent Dispatch Specialist agents auto-run or surface recommendations at each ALIVE stage. **Commands:** - `/analysis-agent` — Show specialist recommendations for current stage - `/analysis-agent {number}` — Run a recommended agent directly (e.g., `/analysis-agent 1`) - `/analysis-agent "{alias}"` — Run by alias (e.g., `/analysis-agent "통계"`, `/analysis-agent "sql"`) **Behavior:** - Required gates (⚡) auto-run without confirmation: `scope-guard`, `data-quality-sentinel`, `ethics-guard`, `reproducibility-keeper` - Optional specialists shown as numbered list (max 3); run after single confirmation - Configure in `.analysis/agents.yml` (copy from `core/config/agents.yml`) > See `core/agents/registry.yml` for full agent catalog (31 agents). See `platforms/cursor/rules/alive-agents.mdc` for dispatch protocol. --- ## Education Mode Structured learning for the ALIVE methodology through guided scenarios. **Commands:** - `/analysis-learn` — Start a learning session - `/analysis-learn-next` — Get feedback and advance to next stage - `/analysis-learn-hint` — Request progressive hints (3 levels per stage) - `/analysis-learn-review` — Complete with scored review **Difficulty levels:** - **Beginner** (Quick format): Rich annotations, built-in hints, single-cause scenarios, 20-30 min - **Intermediate** (Full format): Brief reminders, hints via command only, complex scenarios, 45-60 min **Scenarios:** - b1: "Why did signups drop?" (Beginner, Investigation) - b2: "Which onboarding flow is better?" (Beginner, Comparison) - b3: "How much does turnover cost us?" (Beginner, Quantification) - i1: "Why did DAU drop 15%?" (Intermediate, Investigation) - i2: "Should we lower delivery fees?" (Intermediate, Simulation) - i3: "Did the new checkout flow improve conversion?" (Intermediate, Experiment) - i4: "Can we predict which users will churn?" (Intermediate, Modeling) **Feedback protocol:** Score each stage using rubric → highlight strengths → identify gaps with explanations → check `## Most Common Mistakes` in rubric.md and present matched patterns as `> **Common Mistake Detected**: {name} — {explanation}` → reveal next-stage data. Adjust tone by learner's role. **Hints:** 3 progressive levels per stage (Direction → Specific → Near-answer). Tracked in progress.md. **Progress:** `.analysis/education/progress.md` — scores, Skill Radar, recommended next. **Graduation:** Beginner → Intermediate (2+ scenarios, 70%+). Education → Production (Intermediate 75%+). **ID format:** `L-{YYYY}-{MMDD}-{seq}` **Integration:** Learning sessions show as 📚 in status, excluded from search/retro by default. --- ## Insight Search & Retrospective ### `/analysis-search` Deep full-text search across all analyses (active + archived). Shows matching context with surrounding lines, cross-references similar conclusions, and surfaces unresolved follow-ups. Filters: `--keyword`, `--tag`, `--date`, `--type`, `--confidence`, `--active`/`--archived`/`--both` ### `/analysis-retro` Generates a retrospective report from archived analyses for a given period. Outputs to `analyses/.retro/retro_{period}.md`. Sections: Summary, Analysis Activity, Impact Tracking, Patterns, Unresolved Follow-ups, Recommendations, Appendix. Options: `--last-month` (default), `--last-quarter`, `--range {from to}`, `--all` --- ## Team Dashboard ### `/analysis-dashboard` Exports analyses to JSON and opens the ALIVE Dashboard (node graph visualization). 1. `bash dashboard/export.sh > dashboard-export.json` 2. Open `dashboard/alive-dashboard.html` in browser 3. Load → paste JSON Add `meta.yml` to each analysis folder for analyst, tags, followups, keyFinding fields. --- ## Analysis Memory System (v1.4) ### Analysis Decision Records — `/analysis-dr` Append-only log of methodology decisions. Prevents AI from re-proposing alternatives already evaluated. | Command | What it does | |---------|-------------| | `/analysis-dr new` | Write new DR — batch input or AI-proposed draft | | `/analysis-dr scan` | Find DR candidates from existing analyses | | `/analysis-dr list` | Show all decisions (reads `analyses/decisions/INDEX.md`) | | `/analysis-dr show 003` | Show DR-003 in detail | DR types: Metric, Methodology, Scope, Model, Principle. Files: `analyses/decisions/DR-NNN.md` | Guide: `core/decisions/GUIDE.md` ### Analysis Wiki — `/analysis-wiki` AI-maintained knowledge base compiled from archived analyses (LLM Wiki concept). | Command | What it does | |---------|-------------| | `/analysis-wiki update` | Scan archives → build/update wiki pages | | `/analysis-wiki check` | Find contradictions, stale pages, orphan claims | | `/analysis-wiki show dau` | Display the DAU wiki page | Wiki pages: `analyses/wiki/metrics/`, `patterns/`, `hypotheses/` Guide: `core/wiki/GUIDE.md` --- > Source: [with-geun/alive-analysis](https://github.com/with-geun/alive-analysis) — distributed by [TomeVault](https://tomevault.io).