Signal Scan

Systematically scan a domain for product opportunities by collecting, normalizing, and scoring signals across 7 canonical types. Produces a structured JSON scan and a human-readable markdown summary with ranked opportunities and concrete ship candidates.

Step 0: Load Conventions

Before doing ANYTHING, read the shared conventions file:

Read ${SKILLS_DIR}/_conventions.md

This file defines: canonical vault path, folder-to-type mapping, frontmatter contract, valid statuses, tag hierarchy, cross-reference syntax, and the PipelineEnvelope schema. All output from this skill MUST conform to those conventions. If there is a conflict between this SKILL.md and _conventions.md, the conventions file wins.

When to Use

• Scanning a new domain for product opportunities
• Running market analysis before building something
• Identifying what to build and sell next
• Validating a product idea against real market signals
• Comparing opportunity strength across multiple domains

Trigger Phrases

• “Scan [domain] for product signals”
• “What should I build in [domain]?”
• “Run a signal scan on [domain]”
• “Market analysis for [domain]”
• “/signal-scan [domain]“

Prerequisites

Before starting, establish three things with the user:

1. Domain — What space to scan (e.g., “DevOps education”, “cybersecurity careers”, “AI writing tools”)
2. Operator edge — What makes the user uniquely qualified (skills, audience, credentials, experience)
3. Constraints — Time-to-ship target, price range preferences, format preferences, distribution channels available

If the user provides only a domain, ask about edge and constraints before proceeding. If they provide all three, proceed directly.

Workflow

Domain + Edge + Constraints (from user)
    |
Phase 1: Domain Definition
    |
Phase 2: Signal Collection (web search -- real data required)
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Phase 3: Signal Normalization (structured format per type)
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Phase 4: Opportunity Scoring (weighted scoring matrix)
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Phase 5: Offer Generation (concrete ship candidates)
    |
Phase 6: Meta-Signal Synthesis (overarching thesis)
    |
Output: JSON scan + Markdown summary -> hunter/docs/

Phase 1: Domain Definition

Define the scan parameters explicitly before collecting any data.

Produce a scan header:

• Domain name and boundaries (what is in scope, what is adjacent but excluded)
• Operator edge profile (1-3 sentences on unique qualification)
• Constraints (ship time target, price range, format preferences)
• Relevant subreddits, communities, and platforms to search
• Key competitors to analyze

Present this to the user for confirmation before proceeding. Scope adjustments here save hours of wasted collection.

Phase 2: Signal Collection

This phase requires extensive web search. Use real data. Do not hallucinate signals.

For each of the 7 signal types (see references/signal-taxonomy.md for full definitions), systematically search the following sources:

Search Targets

Collection Rules

• Collect at least 3-5 raw signals per type (or explicitly note why a type has fewer)
• Every signal must have real evidence: an actual quote, a specific number, a URL, or a concrete observation
• “People generally feel…” is not evidence. “User u/devops_mike on r/devops wrote ‘I spent 3 hours trying to…’ (47 upvotes)” is evidence
• Search multiple subreddits and platforms per signal type — do not rely on a single source
• Note the date/recency of evidence when possible

Phase 3: Signal Normalization

Convert every raw signal into the structured format defined by its type. Each signal type has a specific schema (see references/signal-taxonomy.md for field definitions).

Normalization rules:

• Intensity/score ratings (1-10) must be justified by the evidence, not assigned by vibes
• Provide the justification reasoning alongside the score
• Enum fields (recurrence, volume, trend, effort_level, valence, type, impact, strength) must use the exact values from the taxonomy
• Evidence fields must contain specific quotes, numbers, or observations — not summaries
• Source fields must identify the specific platform, subreddit, or URL

Phase 4: Opportunity Scoring

Cluster related signals into opportunity areas. Score each opportunity using 6 dimensions:

Overall score = weighted average: (pain*2 + spend*2 + edge + time + gap + audience) / 8

Scoring Discipline

Each score must be justified. For every dimension, write one sentence explaining the rating. Example:

pain_intensity: 8 — “Three separate Reddit threads with 100+ upvotes describe spending 4+ hours on this task, with emotional language (‘nightmare’, ‘insane’, ‘pulling my hair out’)”

Do not score any dimension without citing specific signals from the collection phase.

Phase 5: Offer Generation

For each opportunity scoring 6 or above, generate concrete ship candidates.

Per candidate, define:

• Product: Specific name and description
• Format: PDF, Notion template, video, video series, course, tool, SaaS, consultation, community, or other
• Price point: Derived from SPEND data (what people already pay for similar things)
• Ship time: Realistic estimate (1 day, 3 days, 1 week, etc.)
• Distribution: Where and how to sell it (the specific channel and strategy)
• Why it works: Connect back to specific signals — which pain points it solves, which demand it meets, which spend patterns it matches

Ship Candidate Rules

• Generate at least 3 candidates per top opportunity
• At least one candidate per opportunity must be shippable in 1-3 days
• Price points must be grounded in SPEND data, not aspirational
• Distribution recommendations must be specific (“post in r/devops with a value-first thread linking to Gumroad” not “social media marketing”)
• Landing page copy hooks should use actual PAIN language from the collection phase

Phase 6: Meta-Signal Synthesis

Step back from individual signals and identify the overarching pattern. Answer:

• What structural failure or market shift creates these opportunities?
• Why do these opportunities exist now and not two years ago?
• What connects the individual pain points, demand signals, and competitive gaps into a coherent thesis?
• If you could only tell someone one sentence about this domain, what would it be?

The meta-signal is not a summary of individual signals. It is the insight that ties them together into a thesis. It should explain why the market looks the way it does, not just what the market looks like.

Output

The scan produces two files, saved to the Obsidian vault:

Vault path: ${VAULT}/Admin/Product-Discovery/Signal-Scans/

1. JSON Scan: {vault}/Admin/Product-Discovery/Signal-Scans/signal-scan-[domain-slug]-[date].json

Structured data following the schema in references/output-schema.json. Must validate against that schema.

2. Markdown Summary: {vault}/Admin/Product-Discovery/Signal-Scans/{domain-slug}-{YYYY-MM-DD}.md

Human-readable report with the following sections:

# Signal Scan: [Domain]

**Date**: [date]
**Operator Edge**: [edge summary]

## Executive Summary
[2-3 sentences: meta-signal + top opportunity + recommended first ship]

## Signal Highlights
[Top 3-5 most compelling signals across all types, with evidence]

## Opportunity Ranking

| Rank | Opportunity | Score | Pain | Spend | Edge | Speed | Gap | Audience |
|------|------------|-------|------|-------|------|-------|-----|----------|
| 1 | [name] | [score] | [n] | [n] | [n] | [n] | [n] | [n] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |

## Top Opportunities (Detail)

### 1. [Opportunity Name] (Score: X/10)
**Thesis**: [why this exists]

**Key signals**:
- [signal + evidence]
- [signal + evidence]

**Ship candidates**:
| Product | Format | Price | Ship Time | Distribution |
|---------|--------|-------|-----------|-------------|
| [name] | [format] | [price] | [time] | [channel] |

### 2. [Next opportunity...]

## Recommended First Ship
**Product**: [name]
**From opportunity**: [which one]
**Rationale**: [why first -- speed, validation, revenue, audience-building]

## Signal Inventory
[Count per type, with notes on any gaps]

## Raw Signal Log
[Full signal details organized by type, for reference]

Resources

references/

• output-schema.json — JSON Schema for the structured scan output. Load when producing the JSON file to validate against.
• signal-taxonomy.md — Detailed definitions for all 7 signal types including fields, sources, measurement criteria, and examples. Load at the start of every scan.

Quality Checklist

Run this checklist before delivering the scan:

• All 7 signal types have at least 2 entries each (or explicit note explaining the gap)
• Every signal has real evidence (quotes, numbers, URLs) — no hypotheticals or “people generally feel…”
• Opportunity scores have justified breakdowns with per-dimension reasoning, not just vibes
• At least 3 ship candidates per opportunity scoring 6+
• At least one ship candidate per top opportunity is shippable in 1-3 days
• Meta-signal synthesizes across signal types, not just summarizes them
• JSON output validates against references/output-schema.json
• Markdown summary includes the ranked opportunity table
• Price points are grounded in SPEND evidence
• Distribution recommendations are specific and actionable
• Landing page copy hooks use actual PAIN language from the scan
• Both JSON and markdown files are saved to vault Admin/Product-Discovery/Signal-Scans/
• Pipeline kanban updated: add card to “Signal Scanned” column (see _conventions.md Pipeline Kanban Contract)