Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bobberrisford/affiliatemcp --skill publisher-performance-reviewgit clone --depth 1 https://github.com/bobberrisford/affiliatemcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bobberrisford/affiliatemcp/publisher-performance-review)<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/publisher-performance-review"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/publisher-performance-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/publisher-performance-review"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/publisher-performance-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.01316 |
| Opus 5 | $0.00051 | $0.00658 |
| Sonnet 5 | $0.00021 | $0.00263 |
| Haiku 4.5 | $0.00010 | $0.00132 |
Grade A, and why
publisher-performance-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operating instructions
You are producing a single-publisher performance review for one brand: a focused
profile of how one partner is performing, built so the account manager can walk
into a call knowing the numbers and the story behind them. Unlike
programme-performance-report (every publisher), this skill is about one
partner across the period.
Step 1 — resolve the brand
If the user did not name a brand, ask which one. Do not guess.
Call affiliate_resolve_brand. If the user named a network, pass { network: "<slug>" } to filter; otherwise call with no arguments and filter the result to the brand the user named.
The response is an array of { brand, network, networkBrandId }. Reduce it to the bindings whose brand matches the user's brand. If none remain, tell the user the brand is not registered, suggest affiliate_resolve_brand with no args to see what is, and stop.
Call affiliate_list_networks once and retain the metadata for those bindings.
Use each network's knownLimitations when deciding whether clicks or another
field are genuinely observed. Do not interpret a normalised zero as observed
zero when the network says that metric is unavailable.
Step 2 — identify the publisher
Publisher ids are network-specific. Call
affiliate_<network>_list_media_partners for each binding and match the user's
named partner against name separately on each network:
- Awin advertiser:
affiliate_awin-advertiser_list_media_partners({ brand })
This returns MediaPartner[] with id, name, status. Record a separate
publisherId and relationship status for every matching network binding. If a
name matches more than one partner on a network, list the candidates and ask
which. If it matches none on one network, report that gap and continue with
the remaining bindings. Stop only when no binding has a match.
Step 3 — pick the windows
Default period: the last 90 days, ending today, so the trend is visible. Honour explicit windows. Compute a comparison window of the same length immediately prior for the period-over-period view. Express all dates as ISO YYYY-MM-DD. State both windows at the top.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 87 lines · 103 tokens per session scan A 42820c486512
publisher-performance-review is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 1,316 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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