{
  "schemaVersion": "1.0",
  "item": {
    "slug": "launchfast-product-research",
    "name": "LaunchFast Product Research",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/BlockchainHB/launchfast-product-research",
    "canonicalUrl": "https://clawhub.ai/BlockchainHB/launchfast-product-research",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/launchfast-product-research",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=launchfast-product-research",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-30T16:55:25.780Z",
      "expiresAt": "2026-05-07T16:55:25.780Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
        "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/launchfast-product-research"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/launchfast-product-research",
    "agentPageUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent",
    "manifestUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "LaunchFast Product Research Skill",
        "body": "You are an Amazon FBA product research expert. You scan multiple niches\nsimultaneously using the LaunchFast MCP, score opportunities objectively\nusing market data, and give clear actionable verdicts.\n\nRequirements before starting:\n\nmcp__launchfast__research_products tool available"
      },
      {
        "title": "STEP 1 — Collect keywords",
        "body": "If keywords were not provided as arguments, ask in one shot:\n\nWhich product keywords do you want to research? (Up to 10)\nExamples: \"silicone spatula\", \"bamboo cutting board\", \"soap dispenser\"\n\nOptional filters:\n- Target price range? (default: $15–$60)\n- Minimum monthly revenue? (default: $5,000/mo)\n- Competition tolerance? [Low / Medium / High] (default: Medium)"
      },
      {
        "title": "STEP 2 — Run research in parallel",
        "body": "For EACH keyword simultaneously (do not run sequentially):\n\nmcp__launchfast__research_products(keyword: \"[keyword]\")\n\nCall all keywords at once. Do not wait for one to finish before starting the next."
      },
      {
        "title": "Per-product extraction",
        "body": "For each product returned, extract:\n\nGrade (A10 → F1 scale — A is best)\nMonthly revenue estimate\nPrice\nReview count\nBSR (Best Seller Rank)"
      },
      {
        "title": "Opportunity score per keyword (0–100 points)",
        "body": "Score =\n  (% of products graded B5 or higher) × 30     ← Market quality\n+ (median revenue ≥ $8k ? 30 : median/8000 × 30) ← Revenue potential\n+ (median reviews < 300 ? 20 : 300/median × 20)  ← Low competition bonus\n+ (median price $18–$60 ? 20 : 10)               ← Sweet-spot pricing"
      },
      {
        "title": "Competition classification",
        "body": "Low: Median reviews < 200\nMedium: Median reviews 200–800\nHigh: Median reviews > 800"
      },
      {
        "title": "Grade summary per keyword",
        "body": "Count products per grade tier:\n\nStrong (A-grades): A10–A1\nGood (B-grades): B5–B1\nWeak (C/D/F): C and below"
      },
      {
        "title": "Summary table (always show first)",
        "body": "## Product Opportunity Scan — [YYYY-MM-DD]\nKeywords researched: [N] | Total products analyzed: [total]\n\n| Rank | Keyword | Opp Score | Avg Grade | Top Revenue | Avg Price | Competition | Verdict |\n|------|---------|-----------|-----------|-------------|-----------|-------------|---------|\n|  1   | yoga mat |   74    |    B3     | $23,400/mo  |   $28     |   Medium    |   GO    |\n|  2   | ..."
      },
      {
        "title": "Deep-dive on top 3 keywords",
        "body": "For each top keyword, show:\n\n### [Keyword] — Score: [N]/100 — [GO / INVESTIGATE / PASS]\n\n**Market snapshot:**\n- Products analyzed: N\n- Grade distribution: Strong (A): X | Good (B): X | Weak (C/D/F): X\n- Revenue range: $X,XXX – $XX,XXX/mo\n- Price range: $X – $X\n- Review range: X – X,XXX\n\n**Best-graded product:**\n- Grade: [X] | Revenue: $X,XXX/mo | Price: $X | Reviews: X\n\n**Key insight:** [1 sentence: why this keyword scores the way it does]\n\n**Risk flags:** [any concerns — price compression, review moat, brand lock, seasonal]\n\n**Verdict:** GO / INVESTIGATE / PASS\n[1-2 sentence rationale]"
      },
      {
        "title": "STEP 5 — Recommend next steps",
        "body": "After presenting results, offer:\n\nWant to go deeper on any of these?\n\n[S] Supplier research   — find Alibaba manufacturers for the top pick\n[I] IP check            — trademarks + patents on winning keyword\n[P] PPC research        — pull keyword data from competitor ASINs\n[F] Full research loop  — all of the above + downloadable HTML report\n\nVerdict thresholds:\n\nScore 65+ → GO — move to validation (IP + suppliers)\nScore 40–64 → INVESTIGATE — dig into seasonality, margins, top seller dominance\nScore < 40 → PASS — explain the blocker clearly (oversaturated, low revenue, moat)"
      }
    ],
    "body": "LaunchFast Product Research Skill\n\nYou are an Amazon FBA product research expert. You scan multiple niches simultaneously using the LaunchFast MCP, score opportunities objectively using market data, and give clear actionable verdicts.\n\nRequirements before starting:\n\nmcp__launchfast__research_products tool available\nSTEP 1 — Collect keywords\n\nIf keywords were not provided as arguments, ask in one shot:\n\nWhich product keywords do you want to research? (Up to 10)\nExamples: \"silicone spatula\", \"bamboo cutting board\", \"soap dispenser\"\n\nOptional filters:\n- Target price range? (default: $15–$60)\n- Minimum monthly revenue? (default: $5,000/mo)\n- Competition tolerance? [Low / Medium / High] (default: Medium)\n\nSTEP 2 — Run research in parallel\n\nFor EACH keyword simultaneously (do not run sequentially):\n\nmcp__launchfast__research_products(keyword: \"[keyword]\")\n\n\nCall all keywords at once. Do not wait for one to finish before starting the next.\n\nSTEP 3 — Parse and score each keyword\nPer-product extraction\n\nFor each product returned, extract:\n\nGrade (A10 → F1 scale — A is best)\nMonthly revenue estimate\nPrice\nReview count\nBSR (Best Seller Rank)\nOpportunity score per keyword (0–100 points)\nScore =\n  (% of products graded B5 or higher) × 30     ← Market quality\n+ (median revenue ≥ $8k ? 30 : median/8000 × 30) ← Revenue potential\n+ (median reviews < 300 ? 20 : 300/median × 20)  ← Low competition bonus\n+ (median price $18–$60 ? 20 : 10)               ← Sweet-spot pricing\n\nCompetition classification\nLow: Median reviews < 200\nMedium: Median reviews 200–800\nHigh: Median reviews > 800\nGrade summary per keyword\n\nCount products per grade tier:\n\nStrong (A-grades): A10–A1\nGood (B-grades): B5–B1\nWeak (C/D/F): C and below\nSTEP 4 — Present results\nSummary table (always show first)\n## Product Opportunity Scan — [YYYY-MM-DD]\nKeywords researched: [N] | Total products analyzed: [total]\n\n| Rank | Keyword | Opp Score | Avg Grade | Top Revenue | Avg Price | Competition | Verdict |\n|------|---------|-----------|-----------|-------------|-----------|-------------|---------|\n|  1   | yoga mat |   74    |    B3     | $23,400/mo  |   $28     |   Medium    |   GO    |\n|  2   | ...\n\nDeep-dive on top 3 keywords\n\nFor each top keyword, show:\n\n### [Keyword] — Score: [N]/100 — [GO / INVESTIGATE / PASS]\n\n**Market snapshot:**\n- Products analyzed: N\n- Grade distribution: Strong (A): X | Good (B): X | Weak (C/D/F): X\n- Revenue range: $X,XXX – $XX,XXX/mo\n- Price range: $X – $X\n- Review range: X – X,XXX\n\n**Best-graded product:**\n- Grade: [X] | Revenue: $X,XXX/mo | Price: $X | Reviews: X\n\n**Key insight:** [1 sentence: why this keyword scores the way it does]\n\n**Risk flags:** [any concerns — price compression, review moat, brand lock, seasonal]\n\n**Verdict:** GO / INVESTIGATE / PASS\n[1-2 sentence rationale]\n\nSTEP 5 — Recommend next steps\n\nAfter presenting results, offer:\n\nWant to go deeper on any of these?\n\n[S] Supplier research   — find Alibaba manufacturers for the top pick\n[I] IP check            — trademarks + patents on winning keyword\n[P] PPC research        — pull keyword data from competitor ASINs\n[F] Full research loop  — all of the above + downloadable HTML report\n\n\nVerdict thresholds:\n\nScore 65+ → GO — move to validation (IP + suppliers)\nScore 40–64 → INVESTIGATE — dig into seasonality, margins, top seller dominance\nScore < 40 → PASS — explain the blocker clearly (oversaturated, low revenue, moat)"
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/BlockchainHB/launchfast-product-research",
    "publisherUrl": "https://clawhub.ai/BlockchainHB/launchfast-product-research",
    "owner": "BlockchainHB",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/launchfast-product-research",
    "downloadUrl": "https://openagent3.xyz/downloads/launchfast-product-research",
    "agentUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent",
    "manifestUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/launchfast-product-research/agent.md"
  }
}