Scout — Domain Intelligence & Setup Recommendation

Before you build, understand the territory. This skill researches what Claude already knows, what's missing, what exists in the marketplace, and recommends exactly what to build.

When to use: Before /create for any serious product. When exploring a new domain. When the creator says "what should I build for X?" or "scout X".

When NOT to use: For marketplace browsing without building intent (use /explore). For building products (use /create→/fill). For mapping YOUR expertise (use /map).

CLI contract: This skill declares Bash(myclaude *) permission but delegates all marketplace queries to /explore. No direct CLI invocations here. See references/cli-contract.md for unified error handling when /explore is invoked as a sub-step.

Activation Protocol

0. Shared preamble: Load references/quality/activation-preamble.md — context assembly, persona adaptation, deterministic routing rules.
1. Creator profile guard: Read creator.yaml from project root. If missing → respond: "Profile not found. Run /onboard first (~3 min)." and stop. 1b. Load UX vocabulary: Load references/ux-vocabulary.md — translate all internal terms (MCS, DNA, scaffold, forge) before any creator-facing output. Vocabulary guard applies to all output in this skill. 1c. Load proactives: Load references/engine-proactive.md — wire #1 (pipeline guidance: after scout, suggest /create), #15 (research injection triggers), #16 (scout intelligence reuse).
2. Maintain creator persona: Adapt language, depth, and examples to profile.type and technical_level throughout. Load references/quality/engine-voice-core.md at the start of every /scout invocation — every user-facing line honors the ✦ signature, three tones, and six anti-patterns. Load the full references/quality/engine-voice.md only when rendering the final report delivery (a peak moment — the Creator just watched 2-5 minutes of research consolidate into a concrete recommendation).
3. Parse arguments: Read $ARGUMENTS for domain query. If no arguments → ask via AskUserQuestion: "What domain do you want me to research?"
4. Intent check: Ask via AskUserQuestion (single select): "What's your goal for this domain?"
   • "Build products for myself" → personal setup, skip pricing
   • "Build products for the marketplace" → include pricing + market scan emphasis
   • "Just explore — not sure yet" → lighter report, skip recommendation details
5. Sanitize slug: lowercase, alphanumeric + hyphens, 3-40 chars.
6. Brownfield check: Glob workspace/scout-{slug}.md. If exists → ask: "A scout report for '{domain}' already exists ({date}). Refresh it or reuse?" 7b. Load entity ontology for type recommendation: Read references/entity-ontology.md §SCOUT_INTELLIGENCE, §INTELLIGENCE_GRADIENT, §COMPOSITION, §HEURISTICS, §ISOMORPHIC. Use these to inform the setup recommendation:
   • Map domain signals to recommended product types per §SCOUT_INTELLIGENCE (single task→skill, advisory→minds, distinct specialties→squad, etc.)
   • Use §INTELLIGENCE_GRADIENT to explain WHY a type is recommended: "This domain needs judgment in routing, so squad is better than workflow."
   • Use §COMPOSITION to suggest how recommended products compose: "The skill handles execution while the mind advises."
   • Use §HEURISTICS to guard against over-recommendation: if a single skill suffices, don't recommend a squad
   • Use §ISOMORPHIC for creator-friendly explanations: "You need an advisor (minds) for thinking and a skill for doing."
   • Use §COMPOSITION_PRINCIPLES to suggest how recommended products compose: convergence-by-independence for squads, symbiosis for mind+skill pairs
   • Use §TRANSVERSAL_AXES to calibrate Nature and Depth per recommended product
   • For MCS-3 targets, consider §INTELLIGENCE_PIPELINE hermeneutic spiral as pre-forge method
7. Gate check: Read config.yaml → gates.confirm_create (default: true). If true → "I'll research '{domain}'. This involves generating a baseline, analyzing gaps, searching the marketplace, and running web research. Takes 2-5 minutes. Continue?"

Execution — 6-Step Intelligence Protocol

Load and execute the full protocol from .claude/agents/scout-agent.md with:

• domain: the parsed domain query
• slug: the sanitized slug
• creator: the loaded creator.yaml profile
• language: creator's language (from creator.yaml or detected from input)
• intent: personal / marketplace / explore (from step 4)

Critical gate between Steps 2→3: After gap analysis completes, PAUSE and show the creator:

Gap Analysis Complete — {domain}

  {critical_count} critical gaps | {significant_count} significant | {minor_count} minor

  Top gaps:
  1. {gap_1_summary} (critical)
  2. {gap_2_summary} (significant)
  3. {gap_3_summary} (significant)

  Next: marketplace scan + web research on these gaps (~{N} searches).
  Continue, adjust focus, or stop here?

Only proceed to Steps 3-6 after creator confirms.

Post-Execution

After the protocol completes:

1. Verify workspace/scout-{slug}.md was written (Glob check)

2. Compute value estimates for each recommended product: For each product in the setup recommendation (Section 5), estimate a preliminary value score using the Intelligence Layer formula (config.yaml → intelligence.pricing):

   • depth: Infer from MCS target in the recommendation (MCS-2 → 2, MCS-3 → 3, cognitive mind → +1)
   • uniqueness: Estimate from gap severity coverage (all critical gaps → 3, mixed → 2, minor only → 1)
   • coverage: Count gaps the product addresses from Section 2
   • market: Use Section 3 market position (blue_ocean → 2, moderate → 1, saturated → 0)

   Map the estimated VALUE_SCORE to a price range using config.yaml → intelligence.pricing.price_map.

   Append to each recommended product in the report output:

   Value estimate: {value_score}/12 → ${range[0]}-${range[1]} ({strategy})
   

   Epistemic caveat (always shown): "Value estimates are preliminary — based on gap coverage and market position. Actual value_score is computed by /validate Stage 8 after content is filled."

3. Portfolio connection check (back-reference from /validate): Read STATE.yaml → workspace.products[] AND STATE.yaml → mcs_results. For each recommended product:

   • Check if any existing product is in the same domain or has overlapping capability.
   • If existing product has mcs_results with overall_score: "Your {slug} covers {domain} at {score}%. New product should focus on gaps {slug} doesn't cover — zero overlap = maximum delta."
   • If existing product has baseline_delta: "Your {slug} addressed {delta}% of known gaps. Scout for complementary coverage." If found: "This connects to your existing {existing_slug} ({existing_type}). Building here extends your {domain} coverage." If the recommended products would bring the domain product count to >= bundle_suggestion_threshold (from config.yaml → intelligence.portfolio): "With these additions, you'd have {N} products in {domain} — consider bundling them for complete coverage."

4. Vocabulary guard (mandatory before rendering): Before emitting any creator-facing text in step 4, translate internal terms per ux-vocabulary.md. Specifically: "gap severity" → "how much is missing", "baseline delta" → "what Claude already knows vs. what's needed", "market saturation" → "how crowded this space is", "blue_ocean" → "wide-open opportunity". For non-developer creators (profile.type != "developer" and != "hybrid"), these plain-language substitutions are required. For developer/hybrid creators, technical terms may appear but must include a brief inline gloss on first use.

5. Show the setup recommendation summary using engine voice:

   Scout complete. {N} gaps found, {M} researched, {P} products recommended.