KOL Pricing

Overview

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. Gather campaign context.
   • Product name, URL, pitch, desired action, estimated LTV.
   • Target KOL tiers, excluded tiers, follower floor, engagement floor, extra keywords.
   • Handles to analyze, or a search query if discovery is needed.
   • If the user does not provide context, ask for minimal campaign inputs. Do not assume the original app's local config files exist in this skills repository.
2. 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.
3. 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.

1. Run the offline pricing script when a reproducible artifact is useful.

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

1. 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

• 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.

• 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.

Pricing Logic

references/pricing-logic.md

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.