skill-market — community skill-market, community, ide skills

v1.0.0

Über diesen Skill

Ideal für KI-Agents wie Claude Code, AutoGPT und LangChain, die erweiterte Marktforschungskapazitäten durch Integration mit dem market-agent benötigen. Market sizing research with TAM/SAM/SOM framework

benbrastmckie benbrastmckie
[435]
[465]
Updated: 3/18/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
70
Canonical Locale
en
Detected Body Locale
en

Ideal für KI-Agents wie Claude Code, AutoGPT und LangChain, die erweiterte Marktforschungskapazitäten durch Integration mit dem market-agent benötigen. Market sizing research with TAM/SAM/SOM framework

Warum diese Fähigkeit verwenden

Ermöglicht es den Agents, Marktforschungsanfragen effizient zu routen, indem sie das interne Postflight-Muster der Fähigkeit für nahtlose Statusaktualisierungen, Artefaktverknüpfungen und Git-Commits nutzen, während sie Protokolle und Dateiformate wie Markdown für die Überprüfung von Subagenten-Rückgaben verwenden.

Am besten geeignet für

Ideal für KI-Agents wie Claude Code, AutoGPT und LangChain, die erweiterte Marktforschungskapazitäten durch Integration mit dem market-agent benötigen.

Handlungsfähige Anwendungsfälle for skill-market

Automatisieren von Marktforschungsanfragen über den market-agent
Erstellen detaillierter Marktanalyseberichte unter Verwendung integrierter KI-Tools wie Avante und Lectic
Fehlerbehebung in Marktforschungsworkflows mit umfassender Inhaltsanalyse

! Sicherheit & Einschränkungen

  • Erfordert Integration mit dem market-agent
  • Implementiert das interne Postflight-Muster der Fähigkeit, das möglicherweise eine zusätzliche Konfiguration erfordert
  • Lädt Kontextzeiger wie subagent-return.md nur während der Ausführung des Subagents

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is skill-market?

Ideal für KI-Agents wie Claude Code, AutoGPT und LangChain, die erweiterte Marktforschungskapazitäten durch Integration mit dem market-agent benötigen. Market sizing research with TAM/SAM/SOM framework

How do I install skill-market?

Run the command: npx killer-skills add benbrastmckie/nvim/skill-market. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for skill-market?

Key use cases include: Automatisieren von Marktforschungsanfragen über den market-agent, Erstellen detaillierter Marktanalyseberichte unter Verwendung integrierter KI-Tools wie Avante und Lectic, Fehlerbehebung in Marktforschungsworkflows mit umfassender Inhaltsanalyse.

Which IDEs are compatible with skill-market?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for skill-market?

Erfordert Integration mit dem market-agent. Implementiert das interne Postflight-Muster der Fähigkeit, das möglicherweise eine zusätzliche Konfiguration erfordert. Lädt Kontextzeiger wie subagent-return.md nur während der Ausführung des Subagents.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add benbrastmckie/nvim/skill-market. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use skill-market immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

skill-market

Install skill-market, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Market Skill

Thin wrapper that routes market sizing research requests to the market-agent.

IMPORTANT: This skill implements the skill-internal postflight pattern. After the subagent returns, this skill handles all postflight operations (status update, artifact linking, git commit) before returning.

Context Pointers

Reference (do not load eagerly):

  • Path: .claude/context/core/formats/subagent-return.md
  • Purpose: Return validation
  • Load at: Subagent execution only

Note: This skill is a thin wrapper. Context is loaded by the delegated agent, not this skill.

Trigger Conditions

This skill activates when:

Direct Invocation

  • User explicitly runs /market command
  • User requests market sizing in conversation

Implicit Invocation (during task implementation)

When an implementing agent encounters any of these patterns:

Plan step language patterns:

  • "Analyze market size"
  • "Calculate TAM/SAM/SOM"
  • "Market sizing analysis"
  • "Estimate addressable market"

Target mentions:

  • "TAM", "SAM", "SOM"
  • "total addressable market"
  • "market opportunity"
  • "market sizing"

When NOT to trigger

Do not invoke for:

  • Competitive analysis (use skill-analyze)
  • GTM strategy (use skill-strategy)
  • General business research (use skill-researcher)
  • Revenue projections (not market sizing)

Execution Flow

Stage 1: Input Validation

Validate required inputs:

  • task_number - Must be provided and exist in state.json
  • industry - Optional, string
  • segment - Optional, string
  • mode - Optional, one of: VALIDATE, SIZE, SEGMENT, DEFEND
bash
1# Lookup task 2task_data=$(jq -r --argjson num "$task_number" \ 3 '.active_projects[] | select(.project_number == $num)' \ 4 specs/state.json) 5 6# Validate exists 7if [ -z "$task_data" ]; then 8 return error "Task $task_number not found" 9fi 10 11# Extract fields 12language=$(echo "$task_data" | jq -r '.language // "founder"') 13status=$(echo "$task_data" | jq -r '.status') 14project_name=$(echo "$task_data" | jq -r '.project_name') 15description=$(echo "$task_data" | jq -r '.description // ""') 16 17# Validate mode if provided 18if [ -n "$mode" ]; then 19 case "$mode" in 20 VALIDATE|SIZE|SEGMENT|DEFEND) ;; 21 *) return error "Invalid mode: $mode. Must be VALIDATE, SIZE, SEGMENT, or DEFEND" ;; 22 esac 23fi

Stage 2: Preflight Status Update

Update task status to "researching" BEFORE invoking subagent.

Update state.json:

bash
1jq --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \ 2 --arg status "researching" \ 3 --arg sid "$session_id" \ 4 '(.active_projects[] | select(.project_number == '$task_number')) |= . + { 5 status: $status, 6 last_updated: $ts, 7 session_id: $sid 8 }' specs/state.json > specs/tmp/state.json && mv specs/tmp/state.json specs/state.json

Update TODO.md: Use Edit tool to change status marker to [RESEARCHING].


Stage 3: Create Postflight Marker

bash
1padded_num=$(printf "%03d" "$task_number") 2mkdir -p "specs/${padded_num}_${project_name}" 3 4cat > "specs/${padded_num}_${project_name}/.postflight-pending" << EOF 5{ 6 "session_id": "${session_id}", 7 "skill": "skill-market", 8 "task_number": ${task_number}, 9 "operation": "research", 10 "reason": "Postflight pending: status update, artifact linking, git commit", 11 "created": "$(date -u +%Y-%m-%dT%H:%M:%SZ)" 12} 13EOF

Stage 4: Prepare Delegation Context

json
1{ 2 "task_context": { 3 "task_number": N, 4 "project_name": "{project_name}", 5 "description": "{description}", 6 "language": "founder" 7 }, 8 "industry": "optional industry hint", 9 "segment": "optional segment hint", 10 "mode": "VALIDATE|SIZE|SEGMENT|DEFEND or null", 11 "metadata_file_path": "specs/{NNN}_{SLUG}/.return-meta.json", 12 "metadata": { 13 "session_id": "sess_{timestamp}_{random}", 14 "delegation_depth": 1, 15 "delegation_path": ["orchestrator", "market", "skill-market"] 16 } 17}

Stage 5: Invoke Agent

CRITICAL: You MUST use the Task tool to spawn the agent.

Required Tool Invocation:

Tool: Task (NOT Skill)
Parameters:
  - subagent_type: "market-agent"
  - prompt: [Include task_context, industry, segment, mode, metadata_file_path, metadata]
  - description: "Market sizing research with TAM/SAM/SOM"

The agent will:

  • Present mode selection if not pre-selected
  • Use forcing questions to gather market data
  • Create research report at specs/{NNN}_{SLUG}/reports/
  • Write metadata file
  • Return brief text summary

Stage 6: Parse Subagent Return

bash
1padded_num=$(printf "%03d" "$task_number") 2metadata_file="specs/${padded_num}_${project_name}/.return-meta.json" 3 4if [ -f "$metadata_file" ] && jq empty "$metadata_file" 2>/dev/null; then 5 status=$(jq -r '.status' "$metadata_file") 6 artifact_path=$(jq -r '.artifacts[0].path // ""' "$metadata_file") 7 artifact_type=$(jq -r '.artifacts[0].type // ""' "$metadata_file") 8 artifact_summary=$(jq -r '.artifacts[0].summary // ""' "$metadata_file") 9else 10 status="failed" 11fi

Stage 7: Update Task Status (Postflight)

If status is "researched", update state.json and TODO.md.

Update state.json:

bash
1jq --arg ts "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \ 2 --arg status "researched" \ 3 '(.active_projects[] | select(.project_number == '$task_number')) |= . + { 4 status: $status, 5 last_updated: $ts 6 }' specs/state.json > specs/tmp/state.json && mv specs/tmp/state.json specs/state.json

Update TODO.md: Use Edit tool to change status marker to [RESEARCHED].


Add artifact to state.json with summary.

IMPORTANT: Use two-step jq pattern to avoid escaping issues.

bash
1if [ -n "$artifact_path" ]; then 2 # Step 1: Filter out existing research artifacts (use "| not" pattern) 3 jq '(.active_projects[] | select(.project_number == '$task_number')).artifacts = 4 [(.active_projects[] | select(.project_number == '$task_number')).artifacts // [] | .[] | select(.type == "research" | not)]' \ 5 specs/state.json > specs/tmp/state.json && mv specs/tmp/state.json specs/state.json 6 7 # Step 2: Add new research artifact 8 jq --arg path "$artifact_path" \ 9 --arg type "$artifact_type" \ 10 --arg summary "$artifact_summary" \ 11 '(.active_projects[] | select(.project_number == '$task_number')).artifacts += [{"path": $path, "type": $type, "summary": $summary}]' \ 12 specs/state.json > specs/tmp/state.json && mv specs/tmp/state.json specs/state.json 13fi

Update TODO.md: Add research artifact link using count-aware format.

Strip specs/ prefix for TODO.md (TODO.md is inside specs/): todo_link_path="${artifact_path#specs/}"

Use count-aware artifact linking format per .claude/rules/state-management.md "Artifact Linking Format".


Stage 9: Git Commit

bash
1git add -A 2git commit -m "task ${task_number}: complete research 3 4Session: ${session_id} 5 6Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>"

Stage 10: Cleanup

bash
1rm -f "specs/${padded_num}_${project_name}/.postflight-pending" 2rm -f "specs/${padded_num}_${project_name}/.postflight-loop-guard" 3rm -f "specs/${padded_num}_${project_name}/.return-meta.json"

Stage 11: Return Brief Summary

Market sizing research completed for task {N}:
- Mode: {mode}, {questions_asked} forcing questions completed
- Problem: {brief problem statement}
- Entity count: {value} from {source}
- Research report: specs/{NNN}_{SLUG}/reports/01_{short-slug}.md
- Status updated to [RESEARCHED]
- Changes committed
- Next: Run /plan {N} to create implementation plan

Return Format

Brief text summary (NOT JSON).

Expected successful return:

Market sizing research completed for task 234:
- Mode: SIZE, 8 forcing questions completed
- Problem: Streamline deploy coordination for mid-market SaaS
- Entity count: 500,000 mid-market SaaS companies globally (Gartner)
- Research report: specs/234_market_sizing_fintech/reports/01_market-sizing.md
- Status updated to [RESEARCHED]
- Changes committed with session sess_1736700000_abc123
- Next: Run /plan 234 to create implementation plan

Error Handling

Input Validation Errors

Return immediately if task not found.

Metadata File Missing

Keep status as "researching" for resume.

User Abandonment

Return partial status with progress made.

Git Commit Failure

Non-blocking: Log failure but continue.

Verwandte Fähigkeiten

Looking for an alternative to skill-market or another community skill for your workflow? Explore these related open-source skills.

Alle anzeigen

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

333.8k
0
Künstliche Intelligenz

widget-generator

Logo of f
f

Erzeugen Sie anpassbare Widget-Plugins für das Prompts.Chat-Feed-System

149.6k
0
Künstliche Intelligenz

flags

Logo of vercel
vercel

Das React-Framework

138.4k
0
Browser

pr-review

Logo of pytorch
pytorch

Tensor und dynamische neuronale Netze in Python mit starker GPU-Beschleunigung

98.6k
0
Entwickler