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.
git clone --depth 1 https://github.com/CBirkbeck/mathlib-qualityWrote 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/commands/cbirkbeck/mathlib-quality/generalise)<a href="https://agentmods.dev/commands/cbirkbeck/mathlib-quality/generalise"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/generalise/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/commands/cbirkbeck/mathlib-quality/generalise"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/generalise.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 | $0.00043 | $0.06339 |
| Opus 5.5 | $0.00017 | $0.02536 |
| Sonnet 5.5 | $0.00009 | $0.01268 |
| Haiku 4.5 | $0.00004 | $0.00634 |
Grade A, and why
generalise 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 1mo 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 — 604 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/generalise — Weaken Assumptions to Maximise Generality
Given a single lemma or definition, find every way its hypotheses can be weakened. Combines two passes:
- Mechanical pass — drop unused hypotheses, swap typeclasses for weaker ones from the
catalogue, point-localise global hypotheses. Each candidate is verified by
lean_diagnostic_messages. - Literature pass — online search (and ChatGPT, if MCP available) for the maximally general form known in the literature. Often the textbook statement is more general than what mathlib currently has.
Triage: small safe changes are auto-applied. Big changes (touch public API, restate the lemma, change typeclass to one with different operators) get presented to the user as a numbered menu of options with trade-offs — no auto-apply.
The hard rules:
- Verify after every weakening attempt. A weakening that breaks the proof is not a weakening; never leave the file in a broken state.
- Public API changes need user approval. A weakening that changes the call signature
of a public lemma (or
simplemma) is a big change, full stop. - Literature search is mandatory — the user explicitly asked for it. Skipping it is a defect.
- Every hypothesis must be examined — produce an artifact (the per-hypothesis status table) so a skipped hypothesis is detectable.
Usage
/generalise <file_path> <decl_name>
/generalise <file_path> # All public declarations in the file (one at a time)
A single declaration is the standard mode — generalisation is per-lemma surgery and shouldn't be batched casually.
Phases
PHASE 1 IDENTIFY find the declaration, its hypotheses, its proof
PHASE 2 CLASSIFY per-hypothesis: type-class? proposition? value?
PHASE 3 USAGE ANALYSIS find every use of every hypothesis in the proof body
PHASE 4 MECHANICAL drop-test + catalogue weakenings; verify each
PHASE 5 LITERATURE online search for max-generality form (REQUIRED)
PHASE 6 TRIAGE small safe vs big requires-approval; build the punch-list
PHASE 7 AUTO-APPLY apply the small changes; verify after each
PHASE 8 USER CHOICE present big-change options with trade-offs; STOP for approval
PHASE 9 APPLY + REPORT final apply + verification + report
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.
- 1mo ago First seen · 604 lines · 43 tokens per session scan A 59a532b59328
generalise is a command published in the GitHub repository CBirkbeck/mathlib-quality (35 stars, last pushed 5d ago), licensed MIT. It adds 43 tokens to every session and 6,339 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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