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 qbr-prep-packgit 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/qbr-prep-pack)<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/qbr-prep-pack"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/qbr-prep-pack/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/qbr-prep-pack"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/qbr-prep-pack.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 | $0.00078 | $0.00483 |
| Opus 5 | $0.00039 | $0.00242 |
| Sonnet 5 | $0.00016 | $0.00097 |
| Haiku 4.5 | $0.00008 | $0.00048 |
Grade A, and why
qbr-prep-pack 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.
What it actually says
QBR preparation
You are preparing a quarterly business review for one brand across every advertiser-side network it is bound to. Produce numbers the account manager can paste into a deck, plus a short narrative. Never invent figures; surface the verbatim error from any tool envelope that fails and continue.
Step 1 — scope
Confirm the brand and the quarter with the user (default: the most recently
completed calendar quarter). Resolve the brand's network bindings with
affiliate_resolve_brand. Express all dates as ISO YYYY-MM-DD.
Step 2 — pull the quarter and the prior quarter
For each bound network, call the advertiser earnings and performance reads for both the target quarter and the prior comparable quarter:
affiliate_<network>_get_earnings_summaryaffiliate_<network>_get_programme_performanceaffiliate_<network>_list_transactions(for reversal and pending detail)
Step 3 — assemble
- Headline: total revenue, commission, and order volume this quarter, each with the QoQ delta (quote both figures).
- By status: approved / pending / reversed split; call out reversal rate and any pending older than 90 days as exposure.
- Partners: top 10 by revenue, and any partner that fell more than 25% QoQ.
- Per-network: keep currencies separate; never invent an FX conversion.
Step 4 — narrative
Three to five plain sentences: what drove the quarter, what slipped, and two or three recommended actions for next quarter. Matter-of-fact, UK spelling.
Constraints
- One brand at a time. For the whole book, use the agency portfolio pack.
- Quote both periods for any change you call a trend.
- Surface every failed read explicitly; do not treat a gap as zero.
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 · 45 lines · 78 tokens per session scan A 22802fa79c0f
qbr-prep-pack is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 483 once invoked, about $0.0004 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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