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

Ask, don't assume — Present questions to the user before filling content.
One stage at a time — Complete the current stage before moving on.
User writes the insight, AI structures it — AI helps organize and challenge.
Batch questions — Present all questions for a stage at once in a structured form.
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.

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).

bash dashboard/export.sh > dashboard-export.json
Open dashboard/alive-dashboard.html in browser
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

name	alive-analysis
description	Data analysis workflow kit using the ALIVE loop (Ask, Look, Investigate, Voice, Evolve)

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

Ask, don't assume — Present questions to the user before filling content.
One stage at a time — Complete the current stage before moving on.
User writes the insight, AI structures it — AI helps organize and challenge.
Batch questions — Present all questions for a stage at once in a structured form.
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.

Model Registry

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)

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).

bash dashboard/export.sh > dashboard-export.json
Open dashboard/alive-dashboard.html in browser
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

alive-analysis

Más de este repositorio

Más de este repositorio

alive-analysis Skill (Cursor Edition)

Cursor-Specific Behavior

Overview

ALIVE Loop Summary

Stage 1: ASK

Stage 2: LOOK

Stage 3: INVESTIGATE

Stage 4: VOICE

Stage 5: EVOLVE

Analysis Types

Investigation

Modeling

Simulation

Checklist Summary

Full Mode (per stage)

Quick Mode (5 items)

Experiment (A/B Test) Summary

Metric Monitoring Summary

ID Format

Stage Icons

File Naming

Interaction Guidelines (Cursor-Adapted)

Core Principles

What NOT to do

Situational Protocols

Scope Creep

Rabbit Hole Guard

Data Quality Emergency

Analysis Independence

Language Support

Quick Reference

Impact Tracking, Tags & Model Registry

Impact Tracking

Tags

Model Registry

Sub-agent Dispatch

Education Mode

Insight Search & Retrospective

/analysis-search

/analysis-retro

Team Dashboard

/analysis-dashboard

Analysis Memory System (v1.4)

Analysis Decision Records — /analysis-dr

Analysis Wiki — /analysis-wiki

alive-analysis Skill (Cursor Edition)

Cursor-Specific Behavior

Overview

ALIVE Loop Summary

Stage 1: ASK

Stage 2: LOOK

Stage 3: INVESTIGATE

Stage 4: VOICE

Stage 5: EVOLVE

Analysis Types

Investigation

Modeling

Simulation

Checklist Summary

Full Mode (per stage)

Quick Mode (5 items)

Experiment (A/B Test) Summary

Metric Monitoring Summary

ID Format

Stage Icons

File Naming

Interaction Guidelines (Cursor-Adapted)

Core Principles

What NOT to do

Situational Protocols

Scope Creep

Rabbit Hole Guard

Data Quality Emergency

Analysis Independence

Language Support

Quick Reference

Impact Tracking, Tags & Model Registry

`/analysis-search`

`/analysis-retro`

`/analysis-dashboard`

Analysis Decision Records — `/analysis-dr`

Analysis Wiki — `/analysis-wiki`

`/analysis-search`

`/analysis-retro`

`/analysis-dashboard`

Analysis Decision Records — `/analysis-dr`

Analysis Wiki — `/analysis-wiki`