Customer Discovery

Pack Availability Guard

Before telling the user to run a skill from another project-local pack, check .agents/project.json.enabled_packs. If the target pack is not enabled, recommend npx skillpacks install <pack> from the project shell, instead of the target skill. After install, tell Claude users to run /reload-skills, then /clear or restart if the skill remains invisible. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.

Customer Discovery — Orchestrator

This is an orchestrator skill using the parent router delegation pattern. It detects context, bootstraps ICP candidates, recommends applicable customer-discovery frameworks, and synthesizes their outputs into unified ICP research. Individual frameworks live as child skills under frameworks/.

Available frameworks:

Report-First Approval Gate

Default to scope-first approval: before synthesized research, inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions in a review alignment page plus a concise conversation summary.

Do not perform synthesized research, rank candidates, make recommendations, or write working packets or canonical deliverables until final compiled YAML approves the research scope. Minimal pre-approval discovery may identify available files, source categories, and open questions; label it as scope evidence, not findings.

After approved research-scope YAML, perform the research and write only the non-canonical working packet defined in the staged workflow. Then update the review alignment page with findings and stop again for feedback-only YAML or final compiled YAML artifact approval before creating or updating canonical research, spec, or task files.

Do not include downstream or cross-skill command recommendations while a scope, framework findings, or synthesis approval is pending. The approval request itself is the next action. Parent-loop continuation is not downstream routing: after framework completion, hand control back to /customer-discovery, never an execution-loop command or a path-shaped child framework invocation. Only emit downstream next-skill routing after the synthesized icp.md artifact has been approved and written.

Staged Research Workflow

Use this staged workflow for synthesized research or report outputs that would create or update canonical research, spec, or task files.

1. Stage 1 - Scope discovery and approval. Inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions. Build the review HTML alignment page before synthesized research. The page must render the proposed scope, available source categories, known context, assumptions/confidence, proposed working-packet and canonical output paths, and research-scope approval gates. Stop for final compiled YAML approval of the research scope. Do not perform synthesized research, rank candidates, make recommendations, or write working packets, canonical research, spec, or task files in Stage 1.
2. Stage 2 - Research and artifact review. Only after approved research-scope YAML with no unresolved needs-clarification, unresolved down feedback, or other unresolved negative feedback, perform the synthesized research, run required source/code checks, and write only a non-canonical working packet: flat mode uses research/_working/preliminary-<skill>-research.md; product-path mode uses research/{slug}/_working/preliminary-<skill>-research.md. Replace <skill> with this skill's name value. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet. Update the review HTML alignment page so it renders the complete working-packet substance as structured HTML review UI: purpose-built sections, tables, matrices, gates, cards, and tier-appropriate charts or diagrams that preserve every packet section, finding, caveat, and decision detail without summary loss. Raw Markdown packet text may appear only as a supplemental source view after the rendered review UI; do not make a Full Preliminary Packet or Full Working Packet raw Markdown dump, giant <pre><code> block, link-only view, or source-only view the primary review surface. Include the evidence matrix, assumptions/confidence register, source coverage gaps, proposed canonical file changes, and artifact approval gates. Stop for either feedback-only YAML or final compiled YAML. Feedback-only YAML revises the working packet and page, then remains in Stage 2.
3. Stage 3 - Finalize approved artifacts. Consume final compiled YAML for artifact approval only when it has no unresolved needs-clarification, unresolved down feedback, or other unresolved negative feedback. Apply approved edits first, archive the working packet to docs/history/archive/YYYY-MM-DD/HHMMSS/<original-working-path>, remove the active working packet, write the approved canonical artifacts to the unchanged output paths below, and convert the alignment page to confirmed with the approval record preserved. As part of confirming, reconcile each displayed gate decision against the final compiled YAML and the written canonical artifact, render any other/freeform choice as the read-only decision and drop superseded options, and run the post-confirmation self-check from the alignment-page contract before handoff.

Canonical output paths remain unchanged. Search logs and other supporting evidence remain allowed only where this skill's output contract already requires them.

How this maps onto the Research Session Loop. This orchestrator runs as a self-advancing loop (see Execution Model below and docs/research-session-loop-convention.md): each approval stop above is a session boundary — the agent stops, the user reviews and compiles YAML, then clears context and re-invokes /customer-discovery to continue. The multi-select framework page is the single scope-and-candidate approval gate (Stage 1) for the whole selected set; synthesis has its own review page. When the loop runs a framework subskill inline (state C), that multi-select approval already satisfies the framework's Stage-1 scope gate, so the framework enters at its research stage (Stage 2) and produces exactly one findings review page — one approval gate per framework. Standalone (non-loop) invocation of a framework subskill still runs its full two-stage workflow.

Evidence And Feedback Handling

Treat user feedback as input to evaluate, not as automatic ground truth.

• For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.
• For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.
• When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.
• When uncertain, say what is known, what is inferred, and what would change the conclusion.

Prerequisites

• Hard: None — this is the first research skill in the AFPS chain.
• Soft: Read these if they exist:
  • research/idea-brief.md (or research/{slug}/idea-brief.md) — concept, hypotheses, ICP readiness notes
  • Specs, README/CLAUDE, and relevant source files for product context
  • research/customer-feedback.md — real customer evidence (triggers product-exists mode)

Execution Model — Research Session Loop

This is a self-advancing Pattern A research orchestrator (see docs/research-session-loop-convention.md). Each invocation starts cold, resolves its state from pasted YAML + filesystem, runs exactly one heavy phase, emits the next gate, and stops. The user advances the loop by clearing context and re-invoking /customer-discovery. The user never invokes a framework subskill directly — the orchestrator follows each selected framework's subskill inline.

When a framework is pending, the only user-facing continuation route is re-invoking /customer-discovery with the same product/research path argument when present, for example /customer-discovery research/afps-tracker. Never tell the user to run a path-shaped child framework command; the parent resolves the pending framework from the run manifest and filesystem.

State lives in two places only:

• Run manifest — research/_working/customer-discovery-run.yaml (flat) or research/{slug}/_working/customer-discovery-run.yaml (product-path). Records the selected framework set and each framework's intermediate path. Written when the multi-select YAML is approved. Shape:

  orchestrator: customer-discovery
  slug: skills-showcase            # omit in flat mode
  selected_frameworks:
    - slug: w3-hypothesis
      intermediate: research/skills-showcase/customer-discovery-w3-hypothesis.md
    - slug: jtbd-needs
      intermediate: research/skills-showcase/customer-discovery-jtbd-needs.md
  

• Canonical-intermediate existence — a selected framework is done when research/customer-discovery-{framework}.md (or research/{slug}/customer-discovery-{framework}.md) exists, pending otherwise. pending = selected − existing-intermediates. The manifest stores selection only, not per-framework status.

research/.progress.yaml stays coarse — its pipeline_stage is a pointer, not per-framework status.

State resolution (resolve the first match; YAML first, then most-progressed A→F)

On each invocation, after Product-Path Scope Resolution (step 0), resolve state:

Light vs heavy. Recording the approved selection into the run manifest (state 0→C head), writing an already-reviewed framework intermediate, and archiving a consumed source are light — they fold into the head of the next heavy session and do not get their own round-trip. The heavy phase (interview, one framework's research, synthesis) is the only thing isolated per session.

Shortcuts. /customer-discovery discovery and /customer-discovery validate short-circuit states E→C: they write a fixed framework set straight into the run manifest after the user approves the shortcut plan, then enter state C (steps 7–8).

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.

0b. Product-path manifest: Read research/.progress.yaml when present. Normalize active_path (singular legacy) to active_paths (plural list) when reading; treat legacy abandoned as archived and exclude archived/deferred/revisit/promoted paths plus research/_archive/ scopes from active target selection.

1. Mode Detection

Runs in state E (the build-selection session, after reading the interview handoff). Detect pre-product mode (default) or product-exists mode:

Pre-product mode activates when:

• No production codebase with live users detected, AND
• No research/customer-feedback.md with post-launch customer evidence, AND
• $ARGUMENTS does not contain "product" or "validate"

Available frameworks in pre-product mode:

• w3-hypothesis (default) — Schwartzfarb WHO/WHAT/WHY hypothesis generation + disproval
• jtbd-needs (default) — Ulwick/Christensen needs-based segmentation via jobs and outcomes
• four-forces (default) — Moesta Push/Pull/Anxiety/Habit switching moment analysis
• five-rings (optional) — Revella decision psychology and buyer journey. Pre-check when idea brief indicates B2B model.
• seven-dimensions (optional) — Lincoln Murphy ICP scoring with post-sale dimensions
• pmf-engine (unavailable) — requires real user data; grayed out with explanation

Product-exists mode activates when:

• Production code or deployment exists, OR
• research/customer-feedback.md exists with real customer data, OR
• $ARGUMENTS contains "product" or "validate"

Available frameworks in product-exists mode:

• pmf-engine (default) — Vohra/Supan empirical ICP from existing user data
• seven-dimensions (default) — Lincoln Murphy structured scoring with post-sale dimensions
• four-forces (optional) — switching analysis grounded in real churn/adoption data
• five-rings (optional) — buying insight refresh with real buyer evidence
• jtbd-needs (optional) — job validation against actual usage
• w3-hypothesis (unavailable) — superseded by empirical data; grayed out with explanation

2. Load Context

• Read $ARGUMENTS: if file path, read it; if text, treat as concept; if empty, check for idea brief
• Read research/{slug}/idea-brief.md or research/idea-brief.md — treat as starting hypotheses, not settled truth
• Read CLAUDE.md, README for product context
• Read codebase (if it exists): package config, key source files, routes, data models
• Read existing research/icp.md if it exists (prior run — ask user if they want to refine or start fresh)
• Read any existing research/customer-discovery-*.md intermediate artifacts (from prior framework runs)

Provisional product-path evidence. When a referenced product path is not present in research/.progress.yaml (either absent entirely or not in active_paths or product_paths[]), do not treat it as a canonical active path. Before using it as source context, require an explicit provisional-path evidence reference: a review-only-approved alignment page that fully renders the proposed path's concept, brief, and manifest entry. If no such evidence exists, ask the user whether to proceed with the path as unverified context or to run /idea-scope-brief first.

3. Marketplace Side Preflight

Before identifying ICP candidates, resolve market-structure side coverage:

• Read any Market Structure Handoff or marketplace/platform/B2B2C/multi-sided notes from the idea brief, especially ## ICP Readiness. Treat side names and value exchange as hypotheses from the idea-scope brief, not validated ICPs.
• If no idea brief exists, infer likely sides from $ARGUMENTS, README, specs, codebase context, or product description when the concept suggests a marketplace/platform/B2B2C/multi-sided model.
• During broad market research, validate or refute the marketplace/platform/B2B2C/multi-sided classification with evidence; log the evidence for or against each apparent side and value exchange.
• Before candidate generation, write a side-coverage note in the working packet that names material sides, buyer/customer/user distinctions, and any uncertain or unsupported side assumptions.
• Candidate generation must cover each material side or explicitly explain why a side is excluded, deferred, or not a customer side.

4. Candidate Bootstrapping

This step runs in state E only (and is skipped when research/icp.md already exists and user chooses to refine rather than start fresh).

Broad market research using WebSearch with 8–12 diverse query strategies:

1. Direct persona searches — "who buys [category]", "[category] buyer persona"
2. Pain point searches — "[domain] biggest challenges", "[workflow] frustrations"
3. Market segment searches — "[category] market segments", "[category] by company size"
4. Trend searches — "[category] trends 2025 2026", "future of [domain]"
5. Competitor user searches — "[competitor] customers", "[competitor] case studies"
6. Forum/community searches — "[domain] reddit complaints", "[domain] community pain points"
7. Job posting searches — "[related role] job description responsibilities"
8. Industry report searches — "[category] market report", "[category] TAM"
9. Switching trigger searches — "why switch from [incumbent]", "[category] migration"
10. Adjacent market searches — "[related category] users", "[upstream/downstream] tools"
11. Geographic/regulatory searches — "[category] by region", "[domain] regulations by country"
12. Business model searches — "[category] business model", "[product] PLG vs sales-led"
13. WTP signal searches — "[category] pricing complaints", "[category] ROI", "[competitor] pricing reviews"
14. Named account searches (B2B) — "[competitor] customer list", "companies using [incumbent]"

Classify the business model into one or more of: B2B SaaS (PLG), B2B SaaS (SLG), B2C, B2C subscription, marketplace/platform, B2B2C, D2C, open-source/open-core, API/developer-first.

Identify 2–5 ICP candidates. For each, note:

• Who they are (role, company type, size)
• What pain evidence exists
• How accessible they are
• How much value we could deliver
• How strong the WTP signal is

Write candidates to research/_working/preliminary-customer-discovery-research.md (the working packet that subskills will read).

Checkpoint — Present candidates to the user. Show ICP candidates with pain evidence, accessibility signals, and value reasoning. Ask:

• "Do any of these surprise you? Is there a segment I'm missing?"
• "Any of these clearly wrong for your situation?"

Incorporate feedback before proceeding to framework selection.

5a. State E — Build Framework Multi-Select Page

Build the framework multi-select review alignment page with:

1. Mode explanation: which mode was detected and why (evidence for detection)
2. ICP candidates summary: validated candidates from step 4 (or from existing research/icp.md)
3. Available evidence summary: what research exists and what's missing
4. Multi-select framework section: checkboxes for each available framework with:
   • Framework name, creator, and one-line description
   • Why it's recommended or optional for this context
   • Which ICP sections this framework primarily feeds (see synthesis mapping)
   • Pre-checked defaults based on detected mode (see mode detection above)
5. Loop explanation: the selected set is the scope-and-candidate approval gate; each selected framework will then be run inline (one findings page per framework) and the run advances by re-invoking /customer-discovery
6. Approval gate: framework selection confirmation

This multi-select approval is the Stage-1 scope approval for the whole selected set. Stop for compiled YAML. Do not write the run manifest or run any framework in this session — that is state C.

5b. State C — Run Next Pending Framework (inline)

This session consumes the approved multi-select YAML (state 0→C) or advances after a prior framework's approval. At the head of the session, do the light bookkeeping first:

1. Write/append the run manifest if it does not yet exist: research/_working/customer-discovery-run.yaml (flat) or research/{slug}/_working/customer-discovery-run.yaml (product-path), recording selected_frameworks with each framework's slug and canonical intermediate path. Include only frameworks the user selected.
2. Archive the preliminary interview handoff at this selection-commit point (move it under docs/history/archive/YYYY-MM-DD/HHMMSS/<original-working-path>). A rejected multi-select page must still be able to rebuild from the handoff, so do not archive it earlier.
3. If a prior framework's reviewed content was just approved by the pasted YAML, write its canonical intermediate research/customer-discovery-{fw}.md (or research/{slug}/customer-discovery-{fw}.md) from the already-reviewed working packet, and archive that framework's working packet and superseded review page.

Then run the one heavy phase for this session:

4. Determine the next pending framework = the first framework in selected_frameworks whose canonical intermediate does not yet exist.
5. Load and follow that framework subskill's SKILL.md inline, entering at its research stage (Stage 2): the multi-select approval already satisfied the framework's Stage-1 scope gate, so perform the framework's research, write its working packet, and build a single findings review page for that framework. Stop for that framework's compiled YAML.

Advance the loop by self-re-invocation. The confirmed-page handoff and the terminal message name /customer-discovery and tell the user to clear context and re-invoke; report progress as "k of N frameworks complete":

✔ Framework 1 (W3 Hypothesis) findings ready for review.
Next: review the page and compile YAML, then clear context and run  /customer-discovery
(the next run writes this framework's intermediate and picks up the next pending framework automatically.)

Do not emit cross-skill routing here — that happens only after synthesis (step 9).

6. State B — Synthesis (auto-detected; also /customer-discovery --synthesize)

Enter synthesis when the run manifest exists, all selected framework intermediates exist, and no canonical research/icp.md yet exists. An explicit /customer-discovery --synthesize also forces this state.

Read all intermediate framework outputs:

• research/customer-discovery-w3-hypothesis.md
• research/customer-discovery-jtbd-needs.md
• research/customer-discovery-four-forces.md
• research/customer-discovery-five-rings.md
• research/customer-discovery-seven-dimensions.md
• research/customer-discovery-pmf-engine.md

At least one must exist. If none exist, tell user to run framework selection first.

Synthesis mapping — how framework outputs merge into the canonical 9+1 section format:

Cross-ICP handling: Each framework intermediate analyzes all ICP candidates. The synthesis merges per-candidate evidence across frameworks into the canonical sections, selects a primary ICP using seven-dimensions scoring (or value × accessibility matrix when seven-dimensions was not run), and populates "Additional ICPs" and "Cross-ICP Analysis" sections.

Score & select primary ICP using a Value × Accessibility scoring matrix:

Value score (how much we can help): pain severity/frequency, WTP quality, segment size, alignment with what's built.

Accessibility score (how easy to reach and convert): channel reachability, sales cycle length, DMU complexity, champion availability, budget alignment.

Cross-ICP analysis: shared pains, conflicts/trade-offs, product line recommendations, build sequence, discovery & evaluation comparison.

Convert secondary ICPs and product-line recommendations into research/.progress.yaml product_paths[] entries when they imply a different product surface. Keep primary ICP in active_paths. Mark non-selected paths status: deferred or status: revisit_candidate.

Evidence matrix: Each claim in the synthesized output maps back to which framework(s) produced it and at what confidence level.

Build alignment page for synthesis approval with:

• Full proposed research/icp.md content
• Evidence matrix combining all framework sources
• Confidence/assumption register
• Cross-ICP analysis with scoring matrix
• Proposed file changes gate
• Approval gate

After approval: write research/icp.md and research/icp-search-log.md. Then, on this canonical write, archive the run manifest (customer-discovery-run.yaml) and the synthesis working packet under docs/history/archive/YYYY-MM-DD/HHMMSS/<original-working-path>, update research/.progress.yaml pipeline_stage to customer-discovery, and emit the downstream next-step routing (step 9). This is the one place cross-orchestrator routing is allowed.

7. State C via Discovery Shortcut (/customer-discovery discovery)

Pre-product shortcut — short-circuits states E→C with a fixed framework set. Build a review alignment page for the shortcut plan with:

1. Shortcut explanation: discovery sprint selected — three core pre-product frameworks (w3-hypothesis, jtbd-needs, four-forces)
2. Evidence readiness: available context and any caveats
3. Proposed framework set: the fixed set that will be recorded in the run manifest
4. Approval gate: require final compiled YAML approval before writing the run manifest

After user approval via final compiled YAML, write the fixed framework set straight into the run manifest (research/_working/customer-discovery-run.yaml or the product-path equivalent), then enter state C and run framework 1 inline per step 5b. The loop advances by re-invoking /customer-discovery.

8. State C via Validation Shortcut (/customer-discovery validate)

Product-exists shortcut — short-circuits states E→C with a fixed framework set. Build a review alignment page for the shortcut plan with:

1. Shortcut explanation: validation sprint selected — empirical ICP from existing users (pmf-engine, seven-dimensions)
2. Evidence readiness: available product/customer evidence and any caveats
3. Proposed framework set: the fixed set that will be recorded in the run manifest
4. Approval gate: require final compiled YAML approval before writing the run manifest

After user approval via final compiled YAML, write the fixed framework set straight into the run manifest, then enter state C and run framework 1 inline per step 5b. The loop advances by re-invoking /customer-discovery.

9. Next Steps (after synthesis only)

Before writing, check which files exist to populate the ## Next Steps section contextually. Include 3–5 applicable items with a "Recommended + Other options" framing — the first matching condition becomes the Recommended item, remaining items become Other options:

• ALWAYS: /competitive-analysis — Research competitors and market gaps for this ICP
• IF no specs/ directory or it's empty: /competitive-analysis — Map the competitive landscape for this ICP's market
• IF specs/ exist but no research/journey-map.md: check .agents/project.json.enabled_packs for customer-lifecycle — if not enabled, recommend npx skillpacks install customer-lifecycle from the project shell; if enabled, recommend /journey-map
• IF codebase exists: check .agents/project.json.enabled_packs for business-ops — if not enabled, recommend npx skillpacks install business-ops from the project shell; if enabled, recommend /mvp-gap
• IF research/competitive-analysis.md exists: check .agents/project.json.enabled_packs for product-design — if not enabled, recommend npx skillpacks install product-design from the project shell; if enabled, recommend /brainstorm

Impact-aware adjustments:

• IF downstream impact is Major: prepend /reconcile-research — [N] conflicts found in downstream docs as the first item
• IF downstream impact is Minor: annotate relevant skill suggestions with "(stale — [brief description])"

10. Downstream Impact Check

After writing, check for downstream research documents that may be affected. Only check documents that exist on disk.

Downstream documents to check (use {slug}/ prefix when product-path scope is active):

• research/competitive-analysis.md
• research/journey-map.md
• research/metrics.md
• research/gtm.md
• research/monetization.md
• research/enterprise-icp.md
• research/customer-feedback.md

For each existing downstream document:

1. Read it — focus on > Based on: header, ## Summary, and sections that reference concepts this skill just defined or changed
2. Identify specific conflicts: claims, assumptions, or references that contradict what was just decided
3. Note each conflict: downstream file, section, the stale claim (quote it), and what it should now say

Classify the impact:

• None: No downstream documents exist, or no conflicts found. Skip display entirely.
• Minor (1–2 small conflicts): Display conflicts to user inline.
• Major (3+ conflicts OR a foundational assumption changed): Display conflicts and strongly recommend /reconcile-research.

Output

State E output: framework multi-select review page + research/_working/preliminary-customer-discovery-research.md

Validated ICP candidates in the working packet, plus the multi-select framework page. The run manifest research/_working/customer-discovery-run.yaml is written at the head of the first state-C session (when the multi-select YAML is approved), not in state E.

State C output: per-framework findings review page + research/customer-discovery-{framework}.md

One findings page per selected framework; the canonical intermediate is written on that framework's approval.

State B output (synthesis): research/icp.md (or research/{slug}/icp.md)

# ICP: [Primary ICP Name]

> Primary ICP selected from [N] candidates. See Additional ICPs and Cross-ICP Analysis below.
> Search log: research/icp-search-log.md
> Frameworks applied: [list of frameworks used]
> Date: [current date]

## Customer Profile
[Buyer persona, budget authority, discovery channels]

### Geographic Focus
[Include only if the product has regulatory, language, or market-specific constraints.
 Initial target geography/region, why that region first, expansion sequence.]

### Named Accounts
[Include for B2B ICPs. 5–10 real companies that fit this profile.
 For each: company name, approximate size, industry, and why they fit.]

## User Profile(s)
[Daily user persona(s), technical sophistication, goals, frustrations]

## Trigger Events
[What causes them to start looking NOW — ranked by frequency and urgency.
 Job changes, growth milestones, compliance deadlines, tool sunsets, contract renewals, etc.]

## Current State Journey
[Step-by-step workflow without our product]

## Pain Map
[Where the current state breaks down — severity, frequency]

## Current Alternatives (User Perspective)
[What users say they currently use or have tried — tool/process names in user language, not a competitive breakdown]

## Market Sizing
[TAM / SAM / SOM with methodology and confidence level.
 Bottom-up: number of target companies × estimated deal size.
 Top-down: industry reports, competitor revenue signals.]

## Stated Value Drivers
[What customers say matters — their language for the value they need, the "aha moment" as they describe it]

### Willingness-to-Pay Signals
[Evidence only: current paid alternatives, current spend or time-cost proxy, budget owner/context, switching-cost tolerance,
economic urgency, procurement or subscription constraints, and pricing sensitivity cues.
Do not recommend pricing or packaging; route raw signals to monetization.]

## Customer ↔ User Dynamics
[Post-purchase buyer-user relationship: provisioning, onboarding, admin vs end-user dynamics.
 For B2B, the detailed buying process and DMU live in Discovery & Evaluation Behavior below;
 this section focuses on the post-purchase relationship.]

## Discovery & Evaluation Behavior

### How They Find Solutions
[Where this persona searches — communities, review sites, peer recommendations, Google, events]
[Who they ask — colleagues, online communities, consultants, analysts]

### How They Evaluate
[What they compare — features, pricing, reviews, case studies, free trials]
[Decision-making process — solo, team, committee; timeline]

### How They Choose
[What tips the decision — peer recommendation, trial experience, brand trust, integration fit]
[Deal-breakers and must-haves from the user's perspective]

## Additional ICPs

### [ICP 2 Name]
#### Customer Profile
[Include Geographic Focus, Named Accounts, and Business Model & Go-to-Market Motion sub-sections where applicable]
...
#### User Profile(s)
...
#### Trigger Events
...
#### Current State Journey
...
#### Pain Map
...
#### Current Alternatives (User Perspective)
...
#### Market Sizing
...
#### Stated Value Drivers
...
#### Willingness-to-Pay Signals
...
#### Customer ↔ User Dynamics
...
#### Discovery & Evaluation Behavior
[Condensed: how they find, evaluate, and choose solutions — include only sub-sections relevant to this ICP]

### [ICP 3 Name]
...

## Cross-ICP Analysis

### Prioritization Matrix
| ICP | Value Score | Accessibility Score | Combined | Rationale |
|-----|------------|-------------------|----------|-----------|
| ... | | | | |

Scoring rationale must explicitly distinguish pain intensity from WTP quality.

### Shared Pain Points
[Pains that appear across multiple ICPs]

### Conflicts & Trade-offs
[Where serving one ICP would hurt another]

### Product Line Recommendations
[How different ICPs could map to tiers, plans, or product variants]

### Recommended Build Sequence
[Which ICP to target first → second → third, with reasoning]

### Discovery & Evaluation Comparison
[How discovery and evaluation behavior differs across ICPs]

### Evidence Matrix
| Claim | Framework Source(s) | Evidence | Confidence |
|-------|-------------------|----------|------------|
| [claim] | W3 / JTBD / Four Forces / Five Rings / Seven Dimensions / PMF Engine | [source] | Strong / Moderate / Hypothesized |

<!-- Include this section only when downstream impact is Minor or Major. Omit entirely for None. -->
## Downstream Impact

> Checked: [list of downstream docs checked]
> Impact: Minor | Major

### Conflicts Found

1. **research/[file].md** — [Section Name]
   - **Stale**: "[exact quote from downstream doc]"
   - **Now**: [what this skill's output says instead]

[For Major only:]
> **Recommended action**: Run `/reconcile-research` to audit and fix all affected downstream documents.

## Next Steps

**Recommended:** [first matching item from step 9]

**Other options:**
- [remaining conditional items from step 9 — only include items whose conditions are met]

## Signals for Downstream Research

> Raw signals captured during research. These are unvalidated observations —
> use the linked skill to verify, validate, and explore alternatives.

### → /competitive-analysis
- [signal]: competitor names mentioned by users
- [signal]: tools users compare or say they evaluated
- [signal]: stated alternatives and what users say about them

### → /positioning
- [signal]: value language users use to describe what matters
- [signal]: differentiation claims users respond to
- [signal]: how users describe the problem in their own words

### → /monetization
- [signal]: budget ownership/context and willingness-to-pay evidence
- [signal]: current paid alternatives, spend proxies, or time-cost proxies
- [signal]: switching-cost tolerance and cost-of-inaction language
- [signal]: pricing sensitivity cues from user conversations

### → /gtm
- [signal]: where users say they found or heard about solutions
- [signal]: community or channel mentions during research
- [signal]: peer recommendation patterns observed

research/icp-search-log.md

Raw research log containing:

• Every WebSearch query executed and why (from orchestrator bootstrapping and each framework subskill)
• Key findings from each search (with source attribution)
• Evidence that supported or contradicted each ICP candidate
• Marketplace Side Preflight validation/refutation plus side coverage/exclusion rationale
• The sco

…(truncated)