Paths: File paths (shared/ , references/ , ../ln-* ) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If shared/ is missing, fetch files via WebFetch from https://raw.githubusercontent.com/levnikolaevich/claude-code-skills/master/skills/{path} .

Opportunity Discoverer

Type: L3 Worker Category: 2XX Planning

Traffic-First approach to finding next growth direction for existing product.

Core Philosophy

Anti-pattern: Idea → Surveys → Product → "where's traffic?" Correct: Traffic → Niche → MVP → Launch under existing demand

The 90% Developer Bug

Most fail because they:

• Invent idea with no analogs

• Ask 5 people "would you pay?" (they say yes for a hot dog)

• Build product with round sum

• Launch with "now let's set up traffic"

• Discover: no traffic exists, never did

No marketer will build funnel for what cold traffic doesn't buy.

Traffic-First Principles

Principle Anti-pattern

1 Traffic exists BEFORE product Building then searching for traffic

2 No surveys — measure real search demand Asking "would you buy?"

3 Existing demand — launch under what people search Creating new category

4 One channel, one idea — no spreading Testing 5 channels at once

5 KILL early — fail fast, don't waste time Scoring all ideas equally

Supporting Methodology

Marc Andreessen (pmarca):

"Validate market at practical level — go get paying customers to demonstrate market exists."

Sam Altman (YC):

"Who desperately needs the product? Best answer is going after large part of small market." "Test idea by launching or trying to sell — get letter of intent before code."

Purpose & Scope

• Discover growth direction BEFORE Epic creation

• Filter ideas through evidence-first KILL funnel

• Output: one recommended idea + one traffic channel

• Position: before ln-210 (Epic Coordinator)

Runtime Contract

MANDATORY READ: Load shared/references/planning_worker_runtime_contract.md , shared/references/coordinator_summary_contract.md

Runtime family: planning-worker-runtime

Identifier:

• discovery work item identifier

Phases:

• PHASE_0_CONFIG

• PHASE_1_INPUT_PROCESSING

• PHASE_2_KILL_FUNNEL

• PHASE_3_RANK_SURVIVORS

• PHASE_4_WRITE_DISCOVERY_REPORT

• PHASE_5_WRITE_SUMMARY

• PHASE_6_SELF_CHECK

Summary contract:

• summary_kind=opportunity-discovery-worker

• standalone mode may return the summary without artifact persistence

• managed mode writes the same JSON to summaryArtifactPath

• default managed artifact path pattern: .hex-skills/runtime-artifacts/runs/{parent_run_id}/opportunity-discovery-worker/ln-201--{identifier}.json

When to Use

Use this skill when:

• Product exists, seeking next growth direction

• Have 3-10 potential ideas/niches

• Want to validate opportunity before committing

• Need to choose ONE channel to focus on

Do NOT use when:

• No product context (greenfield startup)

• Already have validated direction (skip to ln-210)

• Prioritizing existing Stories (use ln-230)

Input Parameters

Parameter Required Description Default

ideas No Comma-separated list

context No Product description for generation

strict No Strict KILL thresholds true

Input modes:

• ideas="idea1, idea2, idea3" — evaluate list

• context="SaaS for X" — generate ideas from product

• Both — generate + add user ideas

KILL Funnel Pipeline

Ideas do not go through 4 separate research-heavy passes anymore. Each idea gets one bundled evidence pass first.

Idea → [Evidence bundle: traffic + demand + competition + revenue] ↓ [Hard kill matrix] ↓ [Interest gate] ↓ [MVP gate] ↓ SURVIVOR

Evidence Bundle (single research pass)

Question: Is there enough external evidence to justify deeper evaluation?

Research bundle:

WebSearch: "[idea] how people find solutions" WebSearch: "[idea] search volume {current_year}" WebSearch: "[idea] competitors {current_year}" WebSearch: "[idea] pricing SaaS"

Extract four signals in one pass:

• Traffic channel: Where do people actively look for this solution?

• Demand: Search volume, trend direction, or strong community pain signal

• Competition: Competitor count and ocean type

• Revenue: Plausible price band and willingness to pay pattern

Traffic channel examples:

Channel Signal Best for

Search/SEO People Google "[problem] solution" Info products, tools

YouTube Tutorial searches exist Education, how-to

Marketplaces Category exists (ProductHunt, AppStore) Apps, plugins

Communities Active subreddits, forums Niche products

Paid Ads Competitors running ads Proven demand

Outbound Clear ICP, reachable B2B high-ticket

Demand thresholds:

Volume Verdict

10K/month Strong demand

1K-10K/month Viable niche

<1K/month Weak unless compensated by very strong niche signal

Competition thresholds:

Competitors Index Ocean Verdict

0 1 Blue Opportunity if demand is real

1-2 2 Emerging Best entry point

3-5 3 Growing Differentiation needed

6-10 4 Mature Hard but possible

10 5 Red Often kill-worthy

Revenue thresholds:

ARPU Market type Viability

$100/user/mo Enterprise High margin

$50-100 Professional Good

$20-50 Prosumer Viable

$5-20 Consumer Volume needed

<$5 Ad-supported Usually not worth it

Hard Kill Matrix

Kill immediately when any hard-stop condition is true:

• no identifiable traffic channel

• demand clearly below viable threshold with no compensating niche signal

• competition index = 5 and no clear wedge

• expected revenue below $20/user for a small-team business

Record the kill reason and stop analysis for that idea.

Personal Interest

Question: Will you enjoy building this?

Method: AskUserQuestion — rate 1-5

Rate your interest in building [idea]: 1 = Meh, would do for money only 2 = Low interest 3 = Neutral 4 = Interested 5 = Excited, would build for free

Why this matters:

• Low interest = burnout in 3 months

• High interest = sustained motivation through hard times

• You'll spend 2+ years on this

When to ask: Only for ideas that survive the external evidence bundle.

KILL if: Score 1-2 — you'll quit before PMF.

Output: Score 1-5

MVP-ability

Question: Can you launch in 4 weeks?

Assessment:

Factor Question Red flag

Tech Existing skills or need to learn? New stack

Dependencies External APIs, partners needed? Waiting on others

Content Significant content creation? Months of writing

Regulations Legal/compliance requirements? Licenses, approvals

Team Solo or need to hire? Can't start alone

Time estimates:

Weeks Complexity Verdict

1-2 Solo, existing skills Best

2-4 Minor learning curve Good

4-8 Some new tech Acceptable

8 Significant infrastructure KILL

When to assess: Only for ideas that survive external evidence + interest gate.

KILL if: >8 weeks to MVP — too slow to validate.

Output: Weeks estimate + blockers

Workflow

Phase 1: Input Processing (2 min)

Parse input:

• If ideas : split comma-separated list

• If context : generate 5-7 ideas via WebSearch

• If both: combine

Validate count:

• Minimum: 3 ideas

• Maximum: 10 ideas

Create output directory:

mkdir -p docs/reference/research/

Output: Idea queue (3-10 items) and checkpoint for PHASE_1_INPUT_PROCESSING

Phase 2: KILL Funnel (per idea)

Process each idea through one bundled evidence pass, then the personal filters only for survivors:

FOR each idea: Build evidence bundle: traffic + demand + competition + revenue

Apply hard kill matrix
    IF failed → KILL, log reason, NEXT idea

Ask Interest
    IF score 1-2 → KILL, log reason, NEXT idea

Assess MVP-ability
    IF >8 weeks → KILL, log reason, NEXT idea

→ SURVIVOR: add to survivors list

Token efficiency:

• Process ONE idea at a time

• One research bundle per idea instead of four separate research phases

• KILL early = no interest prompt, no MVP assessment

• Clear context after each idea

Phase 3: Rank Survivors (2 min)

If survivors exist:

Calculate composite score:

Score = Demand_score + (6 - Competition_index) + Revenue_score + Interest + MVP_score

Sort by score descending

Select TOP recommendation

If no survivors:

• Report: "All ideas killed. Rethink direction."

• Show KILL log for learning

Phase 4: Output (2 min)

Generate: docs/reference/research/[YYYY-MM-DD]-discovery.md

Also emit structured runtime summary:

• schema_version

• summary_kind=opportunity-discovery-worker

• run_id

• identifier

• producer_skill=ln-201

• produced_at

• payload with input_mode , ideas_analyzed , generated_ideas , survivors_count , killed_count , top_recommendation , report_path , warnings

Structure:

Opportunity Discovery: [Date]

Summary

• Ideas analyzed: X
• Survivors: Y
• Killed: Z

TOP RECOMMENDATION

Idea: [Name] Channel: [Primary channel] Why: [2-3 sentence rationale]

Key metrics:

• Demand: [volume]/month
• Competition: [Index] [Ocean type]
• Revenue: $[X]/user
• MVP: [X] weeks

Survivors Table

KILL Log

Next Steps

1. Create Epic with ln-210 for top recommendation
2. Focus on [channel] as primary acquisition
3. Target MVP in [X] weeks

Time-Box

Ideas Estimated time

3 15-20 min

5 25-35 min

10 50-70 min

Note: KILL funnel is faster than full scoring — bad ideas die early.

Integration

Position in workflow:

Product exists ↓ ln-201 (Opportunity Discovery) ← THIS SKILL ↓ ln-210 (Epic Coordinator) ↓ ln-220 (Story Coordinator)

Dependencies:

• WebSearch (all filters except Interest)

• AskUserQuestion (Interest filter)

• Write, Bash (output)

Critical Rules

• Traffic first — no traffic channel = no analysis

• Bundle evidence once — do not run separate research-heavy phases if one pass can answer traffic, demand, competition, and revenue

• KILL immediately — don't score dead ideas

• One recommendation — avoid paralysis

• No surveys — real search data only

• Interest matters — ask only for externally viable ideas

• MVP speed — slow launch = slow learning

Example Usage

With ideas:

ln-201-opportunity-discoverer ideas="AI writing tool, code review bot, translation API"

With context:

ln-201-opportunity-discoverer context="B2B developer tools SaaS"

Example output:

Opportunity Discovery: 2026-01-29

TOP RECOMMENDATION

Idea: Code review bot Channel: SEO (developers search "code review tool") Why: Growing demand (15K/mo), emerging market (3 competitors), $50/user pricing proven, can MVP in 3 weeks with existing skills.

KILL Log

Definition of Done

• Ideas brainstormed from product context and market signals

• Evidence bundle collected for each idea before kill decisions

• Hard kill matrix applied before interest and MVP checks

• Survivors scored and ranked

• Discovery document generated at docs/reference/research/[YYYY-MM-DD]-discovery.md

• TOP RECOMMENDATION identified with channel + rationale

• KILL Log documents all eliminated ideas with reasons

• Structured opportunity-discovery-worker summary returned

• Summary artifact written when summaryArtifactPath is provided

Reference Files

File Purpose

filter_criteria.md KILL thresholds for all filters

channel_analysis.md Traffic channel identification

discovery_template.md Output markdown template

• MANDATORY READ: Load shared/references/research_tool_fallback.md

Version: 2.0.0 Last Updated: 2026-01-29