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/integrate-learnings)<a href="https://agentmods.dev/commands/cbirkbeck/mathlib-quality/integrate-learnings"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/integrate-learnings/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/integrate-learnings"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/integrate-learnings.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 | $0.00012 | $0.01892 |
| Opus 5.5 | $0.00005 | $0.00757 |
| Sonnet 5.5 | $0.00002 | $0.00378 |
| Haiku 4.5 | $0.00001 | $0.00189 |
Grade A, and why
integrate-learnings 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/integrate-learnings — Integrate community learnings into the reference docs
For repo maintainers: process community learning contributions (from /contribute
PRs) into the skill's reference documentation. Contributions land as JSONL files in
data/community_learnings/; this command reads them, counts occurrences, and
propagates the eligible ones into references/style-rules.md,
references/naming-conventions.md, references/proof-patterns.md,
references/pr-feedback-examples.md, etc. — wherever the teaching belongs.
Workers read the reference docs directly. There is no RAG index, no MCP server,
no merge_learnings.py; the reference docs are the single source of truth.
Usage
/integrate-learnings
/integrate-learnings --dry-run (preview without modifying files)
Prerequisites
- Must be run from within the mathlib-quality repo itself
- Community learnings exist in
data/community_learnings/*.jsonl
Workflow
Step 1 — Load all contributions
ls data/community_learnings/*.jsonl
Parse each file (JSONL: one JSON object per line). Skip .gitkeep.
If no files found:
No community learnings to process in data/community_learnings/.
Contributions arrive via `/contribute` PRs from users.
Step 2 — Validate and deduplicate
For each entry:
- Validate JSON structure — must have required fields (
command,type,description). - Dedup — same
before_code+after_codeis a duplicate (keep the most recent). For entries without code (pure teachings), dedup bydescriptionprefix. - Filter — drop entries with empty descriptions or missing
type.
Report:
## Validation Report
- Total entries: 150
- Valid: 142
- Invalid (missing fields): 5
- Duplicates removed: 3
- Ready to integrate: 134
Step 3 — Count occurrences (consensus filter)
Group validated entries by pattern_tags. Print the occurrence count:
| Pattern (representative description) | Occurrences | Sources | Eligible for ref docs? (≥3) |
|---------------------------------------|-------------|-------------------------------------------|------------------------------|
| junk_def / inline_def | 4 | 2026-05-03_mathlib4, 2026-04-21_mathlib4 | yes |
| splits_api / unary_predicate | 3 | 2026-05-06 mathlib-bump session (×3) | yes |
| review-meta-pattern | 1 | 2026-04-29 community contribution | no — single source |
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 · 212 lines · 12 tokens per session scan A 7ea3a693d0cd
integrate-learnings is a command published in the GitHub repository CBirkbeck/mathlib-quality (35 stars, last pushed 5d ago), licensed MIT. It adds 12 tokens to every session and 1,892 once invoked, about $0.0000 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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