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 partner-outreachgit 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/partner-outreach)<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/partner-outreach"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/partner-outreach/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/partner-outreach"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/partner-outreach.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 | $0.00125 | $0.01261 |
| Opus 5 | $0.00063 | $0.00630 |
| Sonnet 5 | $0.00025 | $0.00252 |
| Haiku 4.5 | $0.00013 | $0.00126 |
Grade A, and why
partner-outreach 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operating instructions
You are drafting partner outreach for one brand: either re-engaging an existing
partner who has slowed down, or recruiting a prospect. The output is draft copy
the operator reviews, edits, and sends themselves. This skill writes; it does
not contact anyone. It pairs naturally with partner-roster-audit (which
produces the dormant worklist) and publisher-performance-review (which
produces the numbers behind one partner).
Step 1 — resolve the brand and the intent
If the user did not name a brand, ask which one. Do not guess. Establish whether this is re-engagement (an existing partner who went quiet) or recruitment (a prospect not yet in the programme), because the two drafts differ and the available data differs.
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.
Step 1b — load the client's plan (voice and positioning)
Call affiliate_get_client_strategy({ brand }). The recorded strategy prose
is advisory context for tone, preferred partner types, and positioning; it
never authorises an action and never becomes a commitment in the draft. If a
reporting voice or audience is recorded, write in it. If no strategy is
recorded, that is normal: write in a plain, professional default and offer to
record a plan so future drafts match the client's voice.
Step 2 — gather grounding facts (re-engagement only)
For a re-engagement draft, ground the message in the partner's real history so it does not read as generic. For each binding where the partner exists:
- Confirm the partner and id with
affiliate_<network>_list_media_partners: Awin advertiser:affiliate_awin-advertiser_list_media_partners({ brand }). - Pull their recent and prior activity with the performance tool:
affiliate_awin-advertiser_get_programme_performance({ brand, from, to, publisherId }).
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 · 103 lines · 125 tokens per session scan A ecceae754fe2
partner-outreach is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 1mo ago), licensed MIT. It adds 125 tokens to every session and 1,261 once invoked, about $0.0006 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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