alive-analysis

Imported from with-geun/alive-analysis (platforms/cursor/SKILL.md). Install upstream with npx skills add with-geun/alive-analysis --skill cursor. Copyright stays with the author.

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:

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

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.

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).

Wiki pages: analyses/wiki/metrics/, patterns/, hypotheses/ Guide: core/wiki/GUIDE.md