Borrowing it
Nothing to install: this file belongs to richardthe3rd/cambridge-beer-festival-app. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/richardthe3rd/cambridge-beer-festival-app/main/.claude/skills/proof-and-analysis-toolkit/SKILL.mdgit clone --depth 1 https://github.com/richardthe3rd/cambridge-beer-festival-appWrote 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/richardthe3rd/cambridge-beer-festival-app/proof-and-analysis-toolkit)<a href="https://agentmods.dev/skills/richardthe3rd/cambridge-beer-festival-app/proof-and-analysis-toolkit"><img src="https://agentmods.dev/badge/skills/richardthe3rd/cambridge-beer-festival-app/proof-and-analysis-toolkit/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/richardthe3rd/cambridge-beer-festival-app/proof-and-analysis-toolkit"><img src="https://agentmods.dev/badge/skills/richardthe3rd/cambridge-beer-festival-app/proof-and-analysis-toolkit.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 | $0.00216 | $0.06472 |
| Opus 5.5 | $0.00086 | $0.02589 |
| Sonnet 5.5 | $0.00043 | $0.01294 |
| Haiku 4.5 | $0.00022 | $0.00647 |
Grade A, and why
proof-and-analysis-toolkit scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: First-principles analysis recipes for the Cambridge Beer Festival app — "prove it, don't just install it." Load before trusting or changing API-field parsing/schema code, before building the proof/forensic m How it starts
The opening of the file, as written. The whole thing — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proof and Analysis Toolkit
Doctrine: a claim about this codebase is not true because it sounds plausible, because a doc says so, or because a past PR title implies it — it is true because you counted, decoded, or modeled it yourself and the numbers came out the way your model predicted. Every recipe below ends in a number, a matched frame, or a written prediction that either held or didn't — never in "should be fine."
Six recipes. Each has: when to use, steps, a worked example from this repo's real
history (issue/PR numbers you can hand to gh issue view or grep the CHANGELOG for),
and what counts as proof.
Recipe 1 — Empirical data census
When to use: before writing or trusting any fromJson branch, before believing a
claim like "field X is always a string," before changing a status/enum mapping, or
before filing a bug that assumes one festival's data shape applies to all festivals.
Why it matters here: the live feeds at data.cambeerfestival.app are described in
AGENTS.md as having type-union fields (abv, allergens, year_founded, bar) — but a union
described in prose can silently be wrong, incomplete, or stale. The only way to know
what a field actually does is to fetch every festival/category combination and count.
Steps
- List the categories to check:
beer, cider, perry, mead, wine, international-beer, low-no, apple-juice(seelib/models/beverage_categories.dart), scoped to whichever festival(s) matter — checkavailable_beverage_typesindata/festivals.jsonfirst, since per-festival unions differ (a winter festival may omit categories a summer one has, or use different vocabulary in free-text fields). - Fetch each
{festivalId}/{category}.json— the shape is{"producers": [...], "timestamp": ...}, NOT a bare array (verify this before assuming the top-level type; seelib/services/beer_api_service.dart:110-127,parseProducers). - Run a
jq group_by(type)census over every field you're about to parse or change. - Compare across at least two festivals from different seasons before concluding a union is "always X" — a summer-only sample under-counts winter-only vocabulary (or vice versa).
- State the conclusion as a fraction, never a vibe: "branch X fires in N/M records" or "never fires in N/M records — candidate dead code" or "genuinely mixed — keep the branch."
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.
- 2mo ago First seen · 440 lines · 216 tokens per session scan A ac417a9bb3d9
proof-and-analysis-toolkit is a skill published in the GitHub repository richardthe3rd/cambridge-beer-festival-app (3 stars, last pushed today), licensed MIT. It adds 216 tokens to every session and 6,472 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
proof-and-analysis-toolkit compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| proof-and-analysis-toolkitrichardthe3rd/cambridge-beer-festival-app | Skill | 3 | today | MIT | A | 216 | Claude Code |
| systematic-debuggingopen-metadata/OpenMetadata | Skill | 16k | yesterday | Apache-2.0 | A | 37 | Claude Code |
| log-error-digestzebbern/claude-code-guide | Skill | 4.7k | 3d ago | MIT | A | 71 | Claude Code, Codex |
| cwv-optimizeradobe/skills | Skill | 319 | yesterday | Apache-2.0 | A | 112 | Claude Code |
| ci-fixersipyourdrink-ltd/bernstein | Skill | 1.2k | yesterday | Apache-2.0 | A | 15 | Claude Code, Codex |
| triaging-web-analytics-supportPostHog/posthog | Skill | 41k | yesterday | none | A | 151 | Claude Code, Codex |
| diagnosing-missing-recordingsPostHog/posthog | Skill | 41k | yesterday | none | A | 76 | Claude Code, Codex |