cohort-review

Pack Availability Guard

When recommending a skill from another pack, verify the target pack is installed via .agents/project.json enabled_packs. If it is not enabled, recommend npx skillpacks install <pack> from the project shell. After install, tell Codex users to start a fresh Codex CLI session if the $ skill list remains stale. Only the currently running skill and skills verified available in the active session or project-local install state are directly recommendable. For unavailable pack skills, recommend npx skillpacks install <pack-or-skill>; for unavailable base skills, recommend npx skillpacks init before the skill.

Cohort Review — Post-Launch Metrics & Funnel Analysis

Invoke as $cohort-review.

Takes real usage/revenue data and analyzes against targets from $metrics. Performs cohort analysis, funnel analysis, and channel performance review. Identifies where the funnel leaks, which cohorts retain, and whether activation targets are being hit. Append-style output for tracking performance over time.

Prerequisites

• Hard: research/metrics.md (or research/{slug}/metrics.md) must exist. If not, tell the user to run $metrics first and stop.
• Soft: Read these if they exist for richer analysis:
  • research/icp.md — segment analysis against ICP profiles
  • research/gtm.md — channel strategy to compare against actual channel performance
  • research/monetization.md — revenue targets and unit economics to validate
  • research/assumption-tracker.md — assumptions to validate or invalidate with real data

Process

0. Product-Path Scope Resolution

Resolve research scope by product path before using code or app structure as a hint:

1. If $ARGUMENTS names a non-archived research/{slug}/ directory or a product-path ID whose scope_path points there, use that path. Treat {slug} as the product/app name, not the ICP, audience, or segment label.
2. If $ARGUMENTS names only research/_archive/{slug}/ or a manifest entry with status: archived or legacy status: abandoned, stop and warn that the path is archived; do not write or update scoped outputs there.
3. Read research/.progress.yaml when present. Normalize legacy active_path to active_paths on read and write back active_paths on manifest updates. Treat legacy abandoned as archived; exclude archived, abandoned, deferred, revisit_candidate, promoted, and any scope_path under research/_archive/ from active target selection.
4. If active product paths exist in the manifest, use those paths. If multiple active paths exist, ask which one to target unless this skill explicitly supports cross-path output.
5. If no active manifest target exists, list non-archived product directories under research/, excluding research/_archive/ and dot directories. Auto-select only when exactly one exists; ask when multiple exist.
6. If no product directories exist, use flat research/ single-product mode.
7. Detect monorepo/app/package structure only as a secondary hint. Suggest creating a missing research/{slug}/ product path when code clearly exposes an app, but do not require code or monorepo detection before using research/{slug}/.

When product path {slug} is active, read and write research under research/{slug}/, specs under specs/{slug}/, and treat top-level research/*.md files as flat-mode documents or cross-path summaries.

1. Load Context & Data

Load research context:

• Read research/metrics.md — extract all metric definitions, targets, and North Star metric
• Read other research docs as listed in prerequisites

Ingest performance data: Check $ARGUMENTS:

• File path provided: Read the file (CSV, JSON, markdown table, or any structured format)
• Text provided: Parse the data directly
• Empty: Ask the user to provide data — accept any format: analytics exports, dashboard screenshots described in text, spreadsheet pastes, database query results. If the session is already in Plan mode and there are 2-3 concrete input-source choices, prefer request_user_input.

Clarify with the user. If the session is already in Plan mode and there are 2-3 concrete choices, prefer request_user_input; otherwise ask in plain text:

• What date range does this data cover?
• What's the data source? (analytics tool, database, manual tracking)
• Any known data quality issues?

2. Funnel Analysis

Map the data against the funnel stages defined in research/metrics.md:

1. Top of funnel — visitors, signups, leads (from Growth metrics)
2. Activation — users who reach aha moment (from Activation metrics)
3. Engagement — active usage patterns (from Engagement metrics)
4. Retention — users still active at D7/D30/D90 (from Retention metrics)
5. Revenue — paying users, ARPU, expansion (from Business metrics)

For each stage:

• Actual vs. Target: Compare real numbers against targets from metrics.md
• Conversion rate: What % moves to the next stage?
• Drop-off analysis: Where exactly do users fall off? What's the biggest leak?
• Trend: Is it improving, declining, or flat compared to previous cohort reviews?

3. Cohort Analysis

If the data supports cohort segmentation:

• Time-based cohorts: Group users by signup week/month. Compare retention curves across cohorts — are newer cohorts retaining better or worse?
• Source-based cohorts: If channel data is available, compare retention by acquisition channel
• Segment-based cohorts: If ICP segment data is available, compare performance across ICP segments
• Behavioral cohorts: Group by activation behavior — users who did X vs. didn't

Identify:

• Which cohorts are strongest/weakest?
• Is there a trend in cohort quality over time?
• Are certain channels or segments significantly better?

4. Channel Performance Review

If channel/attribution data is available:

Channel	Volume	CAC	Activation Rate	D30 Retention	LTV (est.)	ROI
[channel]	[count]	[$]	[%]	[%]	[$]	[ratio]

Compare against:

• GTM channel strategy recommendations (from research/gtm.md)
• Budget allocation — is spend aligned with performance?
• Channel-specific benchmarks from research

Flag:

• Channels performing above/below expectations
• Channels with high volume but poor retention (leaky bucket)
• Channels with low volume but high quality (scale opportunity)

5. Key Findings & Present

Synthesize findings into:

1. Health summary — overall: on track, at risk, or off track against metrics targets
2. Top 3 wins — what's working better than expected
3. Top 3 concerns — what's underperforming, where the funnel leaks
4. Biggest lever — the single change that would most improve metrics
5. Assumption updates — which assumptions from $assumption-tracker are validated or invalidated by this data

Present to the user. If the session is already in Plan mode, prefer request_user_input; otherwise ask in plain text:

• "Here's what the data shows. Which conclusions need adjustment based on data quality limits, missing fields, or known operational context?"
• "Any data quality issues that would change these conclusions?"

Incorporate feedback before proceeding.

6. Populate Next Steps

Include 3–5 applicable items with "Pick one:" framing:

• IF activation below target: $experiment — Design an experiment to improve the activation funnel
• IF channel performing poorly: $gtm — Revisit channel strategy with real performance data
• IF pricing/revenue below target: $monetization — Update monetization strategy with real unit economics
• IF assumptions invalidated: $assumption-tracker — Update register with real-data validations
• IF metrics targets need adjustment: $metrics — Revisit targets based on baseline reality
• IF overall on track: $research-roadmap — Check what's next in the project lifecycle

7. Write Output

Present final analysis to user. Ask:

• "Ready to write this analysis? Anything to adjust?"

Only after confirmation, write the output file.

Output

research/cohort-review-[YYYY-MM-DD].md (or research/{slug}/cohort-review-[YYYY-MM-DD].md)

# Cohort Review: [Date Range]

> Date: [current date]
> Data source: [analytics tool / database / manual]
> Period: [start date] — [end date]
> Based on: research/metrics.md

## Health Summary

**Overall**: [On Track / At Risk / Off Track]
[2-3 sentences: the headline — are we hitting our numbers? What's the biggest story?]

### North Star Metric
**Target**: [from metrics.md]
**Actual**: [real number]
**Status**: [On Track / Below / Above] — [trend: improving / declining / flat]

## Funnel Analysis

| Stage | Metric | Target | Actual | Delta | Trend |
|-------|--------|--------|--------|-------|-------|
| Acquisition | [metric] | [target] | [actual] | [+/-] | [↑↓→] |
| Activation | [metric] | [target] | [actual] | [+/-] | [↑↓→] |
| Engagement | [metric] | [target] | [actual] | [+/-] | [↑↓→] |
| Retention | [metric] | [target] | [actual] | [+/-] | [↑↓→] |
| Revenue | [metric] | [target] | [actual] | [+/-] | [↑↓→] |

### Biggest Leak
**Stage**: [where the biggest drop-off occurs]
**Conversion**: [actual %] vs. target [target %]
**Analysis**: [why this is happening — evidence from data]
**Recommendation**: [what to do about it]

## Cohort Analysis

### Retention Curves
| Cohort | D1 | D7 | D14 | D30 | D60 | D90 |
|--------|----|----|-----|-----|-----|-----|
| [cohort] | [%] | [%] | [%] | [%] | [%] | [%] |

### Cohort Quality Trend
[Are newer cohorts better or worse? What's driving the trend?]

### Segment Performance
| Segment | Activation | D30 Retention | Revenue/User | Notes |
|---------|------------|---------------|-------------|-------|
| [segment] | [%] | [%] | [$] | [observation] |

## Channel Performance

| Channel | Volume | CAC | Activation | D30 Retention | Est. LTV | ROI |
|---------|--------|-----|-----------|---------------|----------|-----|
| [channel] | [n] | [$] | [%] | [%] | [$] | [x] |

### Channel Insights
- **Best performer**: [channel] — [why]
- **Worst performer**: [channel] — [why]
- **Scale opportunity**: [channel] — [evidence]
- **Cut/reduce**: [channel] — [evidence]

## Key Findings

### Wins
1. [What's working — with data]
2. [What's working — with data]
3. [What's working — with data]

### Concerns
1. [What's underperforming — with data and impact]
2. [What's underperforming — with data and impact]
3. [What's underperforming — with data and impact]

### Biggest Lever
[The single change that would most improve overall metrics — with reasoning]

## Assumption Validations

| Assumption | Previous Status | New Status | Evidence |
|-----------|----------------|------------|----------|
| [assumption from tracker] | [old status] | Validated/Invalidated | [data point] |

## Next Steps

Pick one:
- [conditional items from step 6 — only include items whose conditions are met]

Create the research/ directory if it doesn't exist.

Task Classification

When this skill produces follow-up work, file it by execution semantics:

• Immediately actionable implementation or documentation work goes in tasks/todo.md.
• Human-only external actions tied to automated steps go in tasks/manual-todo.md with _(blocks: Step N.X)_ or _(after: Step N.X)_; repo edits, SDK wiring, generated assets, local commands, tests, audits, and authenticated CLI/API work stay in tasks/todo.md.
• One-time condition-gated records, baselines, or future measurements go in tasks/record-todo.md with source, condition, non-blocking reason, evidence, and promotion rule.
• Cadence-based reviews, playtests, adoption checks, investor updates, retros, or docs-health checks go in tasks/recurring-todo.md with cadence, owner/agent, next due, evidence path, and escalation conditions.
• Do not put non-blocking records or recurring obligations in tasks/todo.md unless they have been explicitly promoted into current execution work.

Constraints

• Data-driven. Every claim must reference specific numbers from the provided data. Do not fabricate data or fill in placeholders.
• Compare to targets. Always reference metric targets from research/metrics.md. The value of this skill is target vs. actual, not just reporting numbers.
• Handle incomplete data gracefully. If data doesn't cover all funnel stages or cohort dimensions, analyze what's available and note gaps. Don't skip the analysis.
• Append-style. Each cohort review is a separate dated file. Previous reviews are not modified.
• Present before writing. Never write output files until the analysis has been presented and validated.
• Be honest about data quality. If the data is too sparse for statistical significance, say so. Don't over-interpret small samples.
• Recommend actions. Every finding should connect to a concrete next step.

Alignment Page

Follow the shared alignment-page convention via the packaged convention resolver; output path is alignment/cohort-review-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.