# Estimate Creator Rate > Use this skill when you need to price a B2B LinkedIn thought-leadership creator from their real post performance. A weighted-engagement model cross-checked against a CPM anchor produces an initial / budget / max rate band to open a negotiation from. - Skill: `swan-gtm/estimate-creator-rate` (Agent Skill) - Install (CLI): `npx skillmds@latest add swan-gtm/estimate-creator-rate` - Raw SKILL.md: https://api.skillmd.com/api/skills/swan-gtm/estimate-creator-rate/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: swan-gtm (https://skillmd.com/u/swan-gtm) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/swan-gtm/estimate-creator-rate --- # Estimate Creator Rate Price a B2B LinkedIn thought-leadership creator on what their content actually does, not on follower count. This skill turns a creator's recent post performance into an **initial / budget / max** rate band you can open a negotiation from, and works for a creator you know or a cold one you're pricing for the first time. ## Connectors for this skill Optional. The skill runs entirely from pasted inputs, but will use these if available: - **A LinkedIn or creator-analytics source** — to pull the creator's recent post metrics (impressions, likes, comments, reposts) automatically instead of by hand. - **Your past-deal records / rate card** — to calibrate the model's weighting multiples from rates you've already closed. If none are connected, gather the inputs manually in Step 0. ## Step 0 – Gather the inputs Collect, asking the user for anything missing: - The creator (LinkedIn profile, or their recent post metrics directly). - The niche and the brand they'd be posting for. - The deliverable: a single post, or a package (e.g. 3 posts). - A representative sample of recent posts: average impressions, likes, comments, and reposts per post. Exclude obvious viral outliers and dead posts. If the niche isn't given, use a general B2B CPM anchor and state the assumption. Niche changes the anchor, not the method. ## Step 1 – Weighted-engagement model (primary) LinkedIn engagement is weighted by intent, not counted flat. A repost is a far stronger signal than a like: - **Likes** = lowest weight - **Comments** = middle weight - **Reposts / shares** = highest weight ``` weighted engagement = (avg likes × W_like) + (avg comments × W_comment) + (avg reposts × W_repost) ``` Combine the weighted-engagement score with average impressions to produce three price points: - **Initial** — where you open (leaves room to move). - **Budget** — the number you expect to land at. - **Max** — the ceiling you won't cross for this creator. > **You set the multiples.** `W_like`, `W_comment`, `W_repost` and the mapping from (weighted score + impressions) to the band are yours to calibrate. Set them once, keep them consistent across creators, and document them. Back-solve from your own closed deals: find the multiples that reproduce rates you've already agreed. **Principle: historical performance is the driver.** A smaller, perfectly on-ICP audience with strong comment/repost velocity outprices a large generalist. Follower count is context only. ## Step 2 – CPM anchor (cross-check) ``` implied rate = (avg impressions / 1000) × target CPM ``` Organic thought-leadership content prices at a premium to paid social CPMs. Pick a target CPM band for the niche and audience quality (set your own; B2B organic creator content commonly anchors in the tens-to-low-hundreds per 1,000 impressions). > **You set the CPM bands.** Define a target CPM per niche (e.g. regulated/finance, technical/developer, marketing/GTM, general B2B) so the anchor reflects how much a relevant audience is worth to you. Use the CPM-implied rate as the sanity check against Step 1. The engagement model leads for highly engaged niche creators; the CPM anchor leads for high-impression, low-engagement reach plays. ## Step 3 – Reconcile and recommend State the initial / budget / max band, the CPM cross-check, and where to open. If the two methods disagree sharply, say which you trust and why rather than blindly averaging. Name the levers that move the number: single post vs package, content-usage / paid-ad rights, exclusivity, timeline, or a longer-term partnership. ## Output ``` CREATOR RATE ESTIMATE — [Creator], [niche], [brand] Data: [N] recent posts Avg impressions: [X] Weighted engagement: (likes × W) + (comments × W) + (reposts × W) = [score] RATE BAND (per [post / package]) Initial (open here): [ ] Budget (land here): [ ] Max (ceiling): [ ] CPM cross-check: [ ] implied at [target CPM] Verdict: [band holds / adjust because ...] Levers: [package, usage rights, exclusivity, timeline] ``` Use the user's own currency throughout. This band is the anchor a negotiation works from and the basis a benchmark-setting exercise uses to translate rate into expected reach; keep it consistent across those uses. ## What good looks like - The band is built on real post data, not follower count. - Both methods were run; any divergence between them is explained, not averaged away. - The opening number is defensible if the creator pushes back. - If the multiples or CPM bands haven't been set yet, the output shows the structure, states clearly that the numbers are pending calibration, and stops. A labelled gap beats a fabricated rate.