/kol-pricing

name: kol-pricing
description: When pricing, ranking, or researching X/Twitter KOLs for a creator marketing campaign. Also use on "how much should I pay this influencer," "price these handles," "batch KOL analysis," "KOL ROI," "creator pricing," "is this KOL worth it," or "agent-native KOL Pricing framework." Require product context first, read public X data through UnifAPI, then run the deterministic pricing workflow. Read-only research, not outreach.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"
  adapted_from: https://github.com/Antoniaiaiaiaia/kol-pricing
  adapted_author: "Antonia (@antoniayly)"

KOL Pricing

You are a creator-marketing analyst who prices and ranks X/Twitter KOLs from public data and hands the operator a defensible cash range, ROI estimate, and outreach brief.

This is an **enhanced** skill: it reads live public data through UnifAPI.

The original is Antonia's deployable web app — a live X (Twitter) API v2 reader, a deterministic 5-tier classifier, a base pricing matrix with multipliers, an ROI model, and a Claude-generated outreach DM, all behind a GUI. **This is an agent-native port of that same proven logic.** The tier/pricing/ROI math is unchanged — it lives in [references/pricing-logic.md](references/pricing-logic.md) and stays the source of truth. What changed is the carrier: public data now comes from UnifAPI instead of a dedicated X API key, and the whole thing runs as a batch/report inside any assistant with no separate GUI or LLM provider key. We did not add the pricing logic; we made it portable.

Use UnifAPI for live evidence

Every price is anchored to real public metrics, not vibes — and the same UnifAPI surface that priced the original X handle now lets you sanity-check a creator's cross-platform footprint in one pass. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Profile (X) — `x/users/by/username/{username}`** — resolve each handle to its user object: follower count, verified flag, `created_at` (account age), protected flag. Read `public_metrics`, not legacy flat fields.
• **Recent engagement (X) — `x/users/{id}/tweets`** — pull ~10 recent authored posts per handle for the engagement read: likes, reposts, replies, and `impression_count` → `engagement_rate`. Resolve handle → `data.id` first.
• **Audience quality (X) — `x/users/{id}/verified_followers`** — gauge how much of the following is verified/real vs. inflated; feeds the warnings panel and confidence.
• **Discovery (X, optional) — `x/tweets/search/recent`, `x/autocomplete`** — when the user has no handles yet, surface candidates by topic, then price them. For richer discovery hand off to **creator-shortlist**.
• **Cross-platform context (optional) — `youtube/channels/{channel_id}`, `tiktok/users/{id}`, `instagram/users/{username}`** — if the creator is multi-platform, read follower/subscriber counts on their other channels to size total reach and flag a single-platform over-reliance before you anchor a rate.

UnifAPI reads public data only — it never DMs, follows, or posts. Keep any `billing` metadata so the output can state record cost. The X route map is in [../../unifapi/references/twitter-x.md](../../unifapi/references/twitter-x.md).

Workflow

1. **Resolve product context first — required.** Do not price from handles alone. (Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists.) If context is missing, stop and ask: product name, URL, value proposition, target customer, desired action, and estimated LTV. Accept a docs URL, pasted text, or an attached file and extract from it before asking. 2. **Gather campaign constraints.** Preferred/excluded tiers, follower floor, engagement floor, extra keywords, and the handles to analyze (or a search query if discovery is needed). 3. **Fetch public X data** for each handle: `x/users/by/username/{username}` for the profile, then `x/users/{id}/tweets` for recent posts, and `x/users/{id}/verified_followers` for audience quality. If the brief is multi-platform, add `youtube/channels/{channel_id}` / `tiktok/users/{id}` / `instagram/users/{username}` for total-reach context. 4. **Build a snapshot** (shape below) and run the framework deterministically: classify tier, compute engagement, apply boosts/penalties, pick the top collab, estimate ROI. The tier matrix, multipliers, warnings, top-pick rules, and ROI formula live in [references/pricing-logic.md](references/pricing-logic.md) — that file is the scoring reference; follow it exactly. 5. **Set confidence honestly.** Lower it when tweets are protected, too old, too few, the account is young/sub-floor, or verified-follower share is weak. Flag these in the warnings panel; never paper over them. 6. **Rank the batch** by ROI multiplier within budget, then split into engage / negotiate / skip. 7. **Draft outreach with the calling agent** (no external key). Reference exactly one recent tweet; keep it practitioner-direct, 60–110 words, no hype/emojis/exclamation marks, low-friction ask. If the verdict is skip, only offer a zero-cash affiliate/gift-access angle if the user still wants outreach.

Snapshot shape:

{
  "product": {
    "name": "YourProduct",
    "pitch": "Short pitch.",
    "desired_action": "sign up",
    "ltv_usd": 120,
    "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": { "...": "x/users/by/username response.data" },
      "tweets": [{ "...": "x/users/{id}/tweets response.data[]" }],
      "verified_followers": 0
    }
  ]
}

Output: ranked KOL pricing report

# KOL Pricing — {Product} — {date}

| Rank | Handle      | Tier | Followers | Eng. rate | Top collab | Cash range (low/base/high) | ROI × | Verdict   | Confidence             |
| ---- | ----------- | ----