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 programme-reversal-reportgit 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/programme-reversal-report)<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/programme-reversal-report"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/programme-reversal-report/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/programme-reversal-report"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/programme-reversal-report.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 | $0.00093 | $0.01320 |
| Opus 5 | $0.00046 | $0.00660 |
| Sonnet 5 | $0.00019 | $0.00264 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
programme-reversal-report 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operating instructions
You are producing a reversal and decline report for one brand: which conversions
were reversed, why, and which publishers they came from, so the account manager
can see where commission is leaking and take it up with the network or the
partner. This report surfaces the problem; it does not change any transaction.
The advertiser side is read-only — approving, re-opening, or disputing a reversal
happens in the network dashboard, not here. Say so if the user expects an action.
This is a PARTIAL capability: it is useful only for bindings whose advertiser
adapter supports list_transactions, and reversal-reason coverage varies.
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.
Check whether each advertiser adapter supports listTransactions. Report
unsupported bindings as coverage gaps; do not call them or interpret them as
zero reversals.
Step 2 — pick the window
Default period: the last 30 complete days, ending yesterday. Honour explicit windows ("this month", "Q1", named dates). Optionally compute a same-length prior window so you can show whether the reversal rate is rising. Express all dates as ISO YYYY-MM-DD and state them at the top.
Step 3 — fetch transactions per binding
For each supported (brand, network) binding, list the reversed transactions.
Tool names follow affiliate_<network>_list_transactions:
- Awin advertiser:
affiliate_awin-advertiser_list_transactions({ brand, from, to, status: "reversed" })
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.
- 11d ago First seen · 85 lines · 93 tokens per session scan A 27b95c6eac4d
programme-reversal-report is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 1,320 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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