Alive Analysis

alive-analysis

Structured analysis workflow for AI coding agents — every analysis traceable, repeatable, and team-shareable.

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Why this exists

You ask Claude to investigate a metric drop. It gives you an answer. You act on it.

Three months later: *"Why did we change that?"* — gone. No reasoning, no data checks, no audit trail.

Or you open a new chat and explain the whole context again from scratch, repeating work that already happened.

alive-analysis solves this by turning every analysis into a structured, version-controlled workflow. Claude guides you through a five-stage loop. Each stage produces a markdown file. Everything stays in your repo, searchable by you and by Claude.

The result: a growing knowledge base of how your team thinks, not just what it decided.

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How it works

You describe a question  →  /analysis-new

↓ Claude walks you through 5 stages: ASK → LOOK → INVESTIGATE → VOICE → EVOLVE ↓ Each stage = one markdown file in analyses/ ↓ Files are Git-tracked, full-text searchable, readable by Claude in future conversations ↓ Six months later: /analysis-search "checkout drop" → Claude surfaces context, findings, and follow-ups

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Installation

# Clone the repo

git clone https://github.com/with-geun/alive-analysis.git /tmp/alive-analysis

Install — pick your platform

bash /tmp/alive-analysis/install.sh # Claude Code bash /tmp/alive-analysis/install.sh --cursor # Cursor bash /tmp/alive-analysis/install.sh --both # Both

One-time setup — run in your AI agent chat (not the terminal)

/analysis-init # Configure language, team, data stack, key metrics /analysis-new # Start your first analysis

That's it. Claude handles the rest.

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The ALIVE Loop

Every analysis follows the same five-stage structure. Each stage has a clear purpose, a checklist, and a quality gate before moving forward.

You never skip a stage without a deliberate choice. The loop enforces analytical discipline — even when you're in a hurry.

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Three Modes

Full Mode — for decisions that matter

Five separate files, one per stage. ~40-item checklists. All specialist agents available. Use this when the decision is high-stakes, when you'll need to explain your reasoning, or when you want the full audit trail.

analyses/active/F-2026-0303-001_checkout-drop/

├── 01_ask.md ├── 02_look.md ├── 03_investigate.md ├── 04_voice.md └── 05_evolve.md

Quick Mode — for fast turnaround

Everything in one file. Compressed checklist. Same ALIVE structure, just lighter. Use this for exploratory questions, morning standups, or anything you need in under an hour. If it grows in scope, /analysis-promote converts it to Full automatically.

analyses/active/quick_Q-2026-0308-001_dau-check.md

Learn Mode — for building the skill

Guided scenarios with rubric-based scoring, progressive hints, and common-mistake detection. Seven real-world scenarios across two levels. Use this onboard new analysts, practice unfamiliar analysis types, or train interns.

analyses/active/L-2026-0220-002_signup-drop-scenario/

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All Commands

Core workflow

Finding and reviewing past work

Specialist agents

Experiments and monitoring

Education mode

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31 Specialist Agents

At each ALIVE stage, a routing engine reads your analysis context and recommends the right specialists from a pool of 31 agents. You choose which ones to run.

Required gates (auto-run, no confirmation needed)

These four run automatically when their trigger condition is met:

Optional specialists by stage

ASK — problem-framer, hypothesis-gen, metric-translator, sampling-designer

LOOK — data-scout, tracking-auditor, lineage-mapper, sql-writer

INVESTIGATE — eda-agent, stats-agent, experiment-designer, causal-agent, root-cause-analyst, ml-agent, forecast-agent, anomaly-detector, chart-recommender, dashboard-designer, peer-reviewer

VOICE — narrative-agent, exec-summarizer, decision-memo-writer

EVOLVE — metric-definer, semantic-layer-engineer, governance-steward

The routing engine scores each agent against your current context (analysis type, domain, data signals) and presents the top 3 with a plain-language explanation: *"This analysis involves a before/after metric change with no randomization — the causal-agent can assess whether DID or RDD is appropriate here."*

Agents can be disabled per-project in .analysis/agents.yml.

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Experiment Support (A/B Testing)

When you run /analysis-new and choose Experiment type, the ALIVE loop adapts:

DESIGN → VALIDATE → ANALYZE → DECIDE → LEARN

Key enforcements:

• Pre-registration lock: Analysis plan is written and locked before results are revealed — prevents post-hoc rationalization
• SRM detection: Sample Ratio Mismatch flagged automatically during VALIDATE
• Guardrail metrics: Counter-metrics defined alongside primary metric to catch unintended side effects
• Multiple comparison correction: Alerts when running many variants without adjustment
• Statistical validity: The stats-agent verifies power, sample size, and test selection before you call significance

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Metric Monitoring

/monitor-setup

Configure thresholds for your key metrics. When an alert fires, escalate it directly into an analysis:

/analysis-new --from-alert {alert-id}

The ALIVE context pre-fills with the alert data — metric name, value, time window, relevant segments. You start at ASK with context already loaded.

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Analysis Search and Retrospective

Search across all past analyses

/analysis-search "checkout funnel"

Full-text search across titles, hypotheses, findings, and follow-ups. Returns matching analyses with surrounding context, cross-references to related analyses, and suggestions for follow-up work.

Automatic retrospective

/analysis-retro --last-quarter

Aggregates all analyses in a period into a structured report:

• Activity summary (analyses by type, analyst, stage completion)
• Impact tracking (recommendations → decisions → outcomes)
• Recurring patterns and unresolved follow-ups
• Team-level metrics (acceptance rate, decision speed, analysis accuracy over time)

Output: analyses/.retro/retro_{period}.md

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Analysis Memory System (v1.4)

Two features that give your team persistent analytical memory — so knowledge compounds instead of disappearing into chat history.

Decision Records — /analysis-dr

An append-only log of methodology decisions, adapted from the software engineering concept of Architecture Decision Records (ADR).

The problem it solves: Claude keeps suggesting "what if we try Bayesian A/B testing?" even though your team decided that two months ago. Or a new analyst uses a different DAU definition than everyone else. Decision Records prevent this by giving AI a permanent record of what's already been decided — and why.

Five decision types:

Append-only: Decisions are never edited or deleted. If reversed, the old DR is marked Superseded by DR-NNN — so future analysts understand why the team thought differently at that time.

/analysis-dr new        # Write a new decision record

/analysis-dr scan # Find DR candidates from existing analyses /analysis-dr list # Show all recorded decisions /analysis-dr show 003 # Show DR-003 in detail

DRs integrate with the agent system — 6 methodology-suggesting agents (hypothesis-gen, stats-agent, metric-definer, metric-translator, experiment-designer, causal-agent) check decision records before suggesting alternatives. No more debating the same methodology question twice.

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Analysis Wiki — /analysis-wiki

A structured, AI-maintained knowledge base compiled fro