programme-performance-report

programme-performance-report is a skill for Claude Code from bobberrisford/affiliatemcp. It costs 178 tokens per session (3,735 once invoked), scanned A, original, MIT.

A reporting workflow for summarising one brand’s affiliate programme across its advertiser-side affiliate networks. It groups results by publisher, separates transaction statuses, and compares the selected period with the previous one.

In plain words
What is it for?
Use it to prepare daily or periodic account reports showing publisher performance, status totals, and changes over time.
Why use it?
It removes the need to combine network data and calculate publisher-level changes by hand. It also makes clear when a brand or network binding is missing.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the affiliate-networks-mcp plugin — 37 skills, 1 MCP server shipped together

Good fit Use it to prepare daily or periodic account reports showing publisher performance, status totals, and changes over time.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bobberrisford/affiliatemcp/programme-performance-report
Install

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.

Any agent
npx skills add bobberrisford/affiliatemcp --skill programme-performance-report
Clone the repo
git clone --depth 1 https://github.com/bobberrisford/affiliatemcp

Made for: Claude Code.

Or install affiliate-networks-mcp, the plugin that ships this one along with the rest of its 37 skills, 1 MCP server.

Wrote 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.

agentmods badge for programme-performance-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/programme-performance-report/github.svg)](https://agentmods.dev/skills/bobberrisford/affiliatemcp/programme-performance-report)
Your own site
<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/programme-performance-report"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/programme-performance-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.

agentmods 80×15 button for programme-performance-report

Your own site · 80×15
<a href="https://agentmods.dev/skills/bobberrisford/affiliatemcp/programme-performance-report"><img src="https://agentmods.dev/badge/skills/bobberrisford/affiliatemcp/programme-performance-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 178 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,735 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00178 $0.03735
Opus 5 $0.00089 $0.01868
Sonnet 5 $0.00036 $0.00747
Haiku 4.5 $0.00018 $0.00374

Measured 9d ago against content hash 3adadd165fa2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

programme-performance-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 9d 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.

skills/programme-performance-report/SKILL.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Operating instructions

You are producing a per-publisher performance report for one brand across the networks it is bound to.

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.

Step 1b - load the client's plan (strategy and KPIs)

Call affiliate_get_client_strategy({ brand }). This returns the operator's recorded strategy (prose) and kpi ({ present, targets, parseErrors, ... }). It is advisory context: it changes how you read and frame the numbers; it never authorises any action and never changes what the data says.

  • No strategy recorded (strategy.present and kpi.present both false): this is normal, not an error. Produce the report exactly as you would today on bare deltas, and add one short line offering to record a plan: "No strategy is recorded for [brand]. I can set one up so future reports judge against its targets." Then carry on.
  • Orphan (orphan: true): a plan exists but the slug has no binding. Use the prose for framing, but say plainly that the strategy directory has no registered brand binding; do not invent network data for it.
  • Parse errors (kpi.parseErrors non-empty): report each malformed target line verbatim ("KPI line ignored: ...") and exclude it from every verdict. Never guess what a malformed target meant.
  • Targets (kpi.targets): each is { metric, comparator, value, unit?, period? }. Use them in Step 4 to turn deltas into verdicts. Metrics map onto the data as: revenue -> total grossSale, commission -> total commission, conversions -> total conversions, epc -> commission / clicks when clicks are nonzero, aov -> grossSale / conversions when conversions are nonzero, reversal_rate/approval_rate -> from the status split. If a denominator is zero, report that the derived metric is unavailable rather than inventing zero.
  • Unsupported per network: if a target names a metric a bound network cannot supply (for example a network with no get_programme_performance), say so for that network and exclude it from that network's verdict. Do not substitute zero, and do not blend it into a cross-network total without naming the gap.

Read the full file on GitHub · 154 lines

Files

What ships with it

4 files 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.

Changes

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.

  1. 9d ago First seen · 154 lines · 178 tokens per session scan A 3adadd165fa2

Subscribe to this mod's changes

programme-performance-report is a skill published in the GitHub repository bobberrisford/affiliatemcp (6 stars, last pushed 28d ago), licensed MIT. It adds 178 tokens to every session and 3,735 once invoked, about $0.0009 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens