kol-pricing

name	kol-pricing
description	Use this skill when pricing, ranking, or researching X/Twitter KOLs for a creator marketing campaign, especially when the user provides handles, asks for batch KOL analysis, wants outreach recommendations, wants Markdown plus Tailwind HTML campaign reports, or wants an agent-native version of the KOL Pricing framework. Require product context before analysis, prefer UnifAPI MCP tools for public X data, then run the deterministic pricing workflow before drafting outreach.
license	MIT
metadata	{"author":"UnifAPI","version":"1.0.0","homepage":"https://unifapi.com/skills/kol-pricing","source":"https://github.com/unifapi-agent/skills","original_source":"https://github.com/Antoniaiaiaiaia/kol-pricing","original_author":"Antonia (@antoniayly)"}

KOL Pricing

Overview

Analyze X/Twitter KOLs as an agent workflow instead of a local click-through app. The agent fetches public profile and tweet data through UnifAPI MCP tools when available, applies the KOL Pricing framework deterministically, then produces campaign-ready pricing, ROI, warnings, and outreach briefs.

Core principle: keep the original framework's tiering and pricing logic as the source of truth. Use the calling agent's model for synthesis and DM copy; do not require a separate LLM provider key for the core analysis.

Attribution: this skill is adapted from Antoniaiaiaiaia/kol-pricing, originally by Antonia (@antoniayly). See references/original-license.md.

References

• references/pricing-logic.md - tier matrix, boosts, penalties, ROI formula, top-pick rules, warnings, and DM boundary.
• references/original-license.md - original MIT license notice and attribution.
• ../unifapi/references/twitter-x.md - current UnifAPI X/Twitter route map.

Workflow

1. Resolve product context first.

   • Treat the promoted product as required input before fetching KOL data or pricing a campaign.
   • If the current conversation does not include product information, stop and ask for it. Accept a product/docs URL, pasted text, or a local/attached text/PDF/document file. A concise manual summary is also fine.
   • Ask for only the missing essentials: product name, URL if available, value proposition, target customer, desired action, and estimated LTV if known. Do not proceed from handles alone.
   • When the user provides a URL or file, extract product context from that source first, then ask a follow-up only for details still missing.

2. Gather campaign constraints.

   • Target KOL tiers, excluded tiers, follower floor, engagement floor, extra keywords.
   • Handles to analyze, or a search query if discovery is needed.
   • Do not assume the original app's local config files exist in this skills repository.

3. Fetch public X/Twitter data.

   • Prefer the available UnifAPI MCP tools. Look for operations corresponding to:
     • GET /x/users/by/username/{username} for profile lookup by handle.
     • GET /x/users/{id}/tweets for recent authored posts after resolving the handle to data.id.
     • GET /x/tweets/search/recent and GET /x/autocomplete for candidate discovery.
   • For each handle, fetch one profile and recent authored tweets. Ten tweets is enough for the default pricing workflow unless the user asks for deeper evidence.
   • Read follower and engagement metrics from public_metrics, not old flat fields.
   • Keep the returned billing metadata when available so final reports can mention actual record cost.
   • Do not call api.x.com directly unless the user explicitly asks for an official X implementation.

4. Create a snapshot JSON for deterministic analysis.

   • Use this shape:

{
  "product": {
    "name": "YourProduct",
    "tagline": "What you do, in one line.",
    "pitch": "Short product pitch.",
    "desired_action": "sign up",
    "ltv_usd": 120,
    "twitter_handle": "@yourhandle",
    "url": "https://example.com"
  },
  "ideal_kols": {
    "preferred_tiers": ["T", "B"],
    "excluded_tiers": [],
    "extra_keywords": ["sdk", "agent"],
    "min_followers": 1000,
    "engagement_floor_pct": 0.5
  },
  "handles": [
    {
      "handle": "example",
      "profile": { "...": "UnifAPI X user object from response.data" },
      "tweets": [{ "...": "UnifAPI X tweet object from response.data[]" }]
    }
  ]
}

• The analyzer also accepts whole UnifAPI response envelopes as profile_response and tweets_response, which is useful when preserving request_id, pagination, and billing beside the normalized report.

5. Run the offline pricing script when a reproducible artifact is useful. Generate Markdown, JSON, and Tailwind HTML artifacts from the same snapshot.

node skills/kol-pricing/scripts/analyze-snapshot.mjs \
  --input /tmp/kol-pricing-input.json \
  --out /tmp/kol-pricing-report.md \
  --json /tmp/kol-pricing-report.json \
  --html /tmp/kol-pricing-report.html

6. Draft outreach with the calling agent, not an external LLM key.
   • Use dm_brief from the JSON report.
   • Reference exactly one recent tweet when possible.
   • Keep the tone practitioner, direct, and low-hype.
   • If the recommendation is skip, draft a zero-cash affiliate/gift-access option only if the user still wants outreach.

Output

Always produce a text report in chat or Markdown and, when writing artifacts, also produce an HTML report styled with Tailwind. The HTML must use the same analysis results as the text report, avoid placeholder/mock data, and mirror the original app's result modules: profile header, warnings panel, tier verdict, collaboration matrix, top-pick ROI card, contract requirements, outreach brief, and audit trail. For batch reports, prepend a records-style ranked table and top actions before the per-KOL modules.

For single-handle analysis, return:

• Verdict: tier, top pick, cash range, ROI, risk level.
• Evidence: matched keywords, engagement, profile fit, recent tweet signals.
• Recommendation: contract terms and outreach brief.
• Cost: UnifAPI records consumed or the best estimate if billing metadata is unavailable.
• HTML report path when an artifact was generated.

For batch analysis, return:

• Ranked table.
• Per-KOL mini reports.
• Top 3 actions: engage, negotiate, skip.
• Optional DM drafts for only the selected KOLs unless the user asks for all.
• HTML report path when an artifact was generated.

Pricing Logic

Read references/pricing-logic.md before changing constants, explaining the model, or reviewing pricing behavior. It records the tier matrix, boosts, penalties, ROI formula, top-pick rules, warnings, and DM boundary from the original code.

Guardrails

• Be clear that pricing is a decision aid, not a guaranteed market rate.
• Do not hide low-confidence inputs. If tweets are unavailable, protected, too old, or too few, report that.
• Do not require ANTHROPIC_API_KEY; the agent using this skill can draft copy itself.
• Preserve author attribution when presenting this as an extension of the KOL Pricing framework.