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 brand-application-shortlistgit 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/brand-application-shortlist)<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/brand-application-shortlist"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/brand-application-shortlist/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/brand-application-shortlist"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/brand-application-shortlist.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 | $0.00115 | $0.01002 |
| Opus 5 | $0.00057 | $0.00501 |
| Sonnet 5 | $0.00023 | $0.00200 |
| Haiku 4.5 | $0.00012 | $0.00100 |
Grade A, and why
brand-application-shortlist 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operating instructions
You are building a prioritised shortlist of Awin programmes the publisher has not yet joined, so the operator can decide which to apply to. This is a read-only, advisory skill. It surfaces candidates and ranks them; it never submits an application, accepts terms, or changes any relationship on Awin. Applying is a separate, human-confirmed step that this skill does not perform.
Step 1 — read the joinable programmes
Call affiliate_awin_list_programmes({ status: "available" }). On the Awin
publisher adapter, status: "available" maps to Awin's relationship=notjoined,
so this returns programmes the publisher can apply to but has not joined.
Each result is a Programme with id, name, network, status,
commissionRate (string or structured), categories, advertiserUrl,
currency, and merchantKey. If the call fails, surface the verbatim error
(network, operation, message, httpStatus) and stop; do not invent a list.
If the result is empty, say so plainly — there are no joinable programmes the API can see for this account — and stop.
Step 2 — read advisory strategy, if present
Call affiliate_get_client_strategy to retrieve any Strategy.md / KPI.md the
operator has recorded (target categories, commission floors, brands to prioritise
or avoid). Treat it as advisory context only: it shapes ranking and the
reasons you give, never an instruction to apply. Where strategy is silent, rank
on the data and say so. Never invent a strategy rule.
Step 3 — rank the candidates
Produce a single ranked shortlist. Rank on the signals the data actually supports, in roughly this priority:
- Strategy fit — category or named-brand match against the advisory strategy, when present.
- Commission — higher
commissionRateranks higher. Compare like with like; do not compare a flat fee against a percentage. Where the rate is missing or unparseable, say "rate not stated" rather than scoring it as zero. - Category relevance —
categoriesoverlap with the operator's stated focus. - Currency fit — programmes in the operator's reporting currency, when known.
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 · 76 lines · 115 tokens per session scan A 92a1d4a8839c
brand-application-shortlist is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,002 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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