Coolify is a self-hosted platform for managing servers and deploying applications, databases, static sites, and other services on hardware controlled by the user. Developers and teams use it as an alternative to hosted application platforms such as Heroku, Netlify, and Vercel, while the catalogue entries help coding agents operate Coolify.
Borrowing it
Nothing to install: this file belongs to coollabsio/coolify. 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/coollabsio/coolify/main/.agents/skills/infer-conventions/SKILL.mdgit clone --depth 1 https://github.com/coollabsio/coolifyWrote 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/coollabsio/coolify/infer-conventions)<a href="https://agentmods.dev/skills/coollabsio/coolify/infer-conventions"><img src="https://agentmods.dev/badge/skills/coollabsio/coolify/infer-conventions/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/coollabsio/coolify/infer-conventions"><img src="https://agentmods.dev/badge/skills/coollabsio/coolify/infer-conventions.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 | $0.00190 | $0.02768 |
| Opus 5.5 | $0.00076 | $0.01107 |
| Sonnet 5.5 | $0.00038 | $0.00554 |
| Haiku 4.5 | $0.00019 | $0.00277 |
Grade A, and why
infer-conventions 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 29d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- infer-conventions — 95% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Infer Conventions
Learn how this application writes Laravel, then record what you learn as durable, path-scoped rules other agents will read. You are documenting reality, not improving it.
Ground Rules (read before you start)
- Consistency first. The codebase's majority style is the convention. Never judge it, never propose a "better" pattern, never record what the code should do. If the app validates inline everywhere, that is the rule, even if Form Requests would be nicer.
- Skip what an active tool produces, keep what a tool would fight. Inspect the project's Pint and Rector configuration first; a Rector transformation is tooling-owned only when its package and relevant rule or set are installed and enabled. Active tools may rewrite code toward one canonical form:
$caststocasts(),$fillableto attributes, magic accessors to theAttributeclass, pipe-string rules to arrays,$signatureto#[Signature], named migrations to anonymous, and many more. When the app already sits at an active tool's target form, the tool owns it, so record nothing. But when the app deliberately holds a form an active tool would refactor away, such as legacygetXxxAttribute()accessors theAttributeclass would replace, no tool can reproduce that choice and an agent defaults the other way. That against-the-grain hold is exactly what to record. - Record decisions, not defaults. A consistent pattern earns a rule only when it reflects a choice: the app took one valid option where the framework or common practice offered others, or the pattern would surprise a competent agent. Framework defaults steer nothing, so skip them: anonymous migrations,
$signaturecommands,ShouldQueuejobs,casts()on Laravel 11+, named routes, Rule objects inapp/Rules, andMail::fake()orBus::fake()to isolate framework services. A real fork is not enough on its own. Weigh the side the app took, and record only the side an agent would not reach for by itself: inline closures everywhere, legacy accessors, a bespoke query layer. Watch for the false fork too. "No Mockery" next to facade fakes is not a choice against Mockery, because they double different things. The test for every candidate: without this rule, would the next agent plausibly write it differently? Only "yes" earns a rule. - Architecture choices are the gold. Record presence and deliberate absence. The structural pattern the app commits to is the highest-signal convention and the one no tool can decide: Action classes and how they are invoked (
handle/execute/__invoke), service objects, dedicated query objects exposingbuilder(), DTOs (spatie/laravel-data vs readonly classes), Form Request validation vs inline, an events and listeners spine vs direct calls, and domain or module folders. Also record a consistent non-pattern, such as "query Eloquent directly in controllers, no repository layer", so the next agent matches the app's altitude instead of over-engineering. - Never duplicate
.ai/rules. Read.ai/rules/index.mdand the area files before the sweep. A dimension already covered there is marked done and skipped. - Evidence or silence. A convention needs at least 3 consistent examples and no meaningful rival to become a candidate. Every Step 1 verdict applies this bar.
- The recorded rule states the convention, nothing else. One or two imperative lines: this project does X, so do X here. Keep detection evidence out. No counts, ratios, current usage, file lists, or example paths, because that is proof for the confirm step, not part of the rule. One short syntax fragment at most, and point to
search-docsfor API details.
What ships with it
1 file 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.
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.
- 29d ago First seen · 106 lines · 190 tokens per session scan A 6c17a1c29edf
infer-conventions is a skill published in the GitHub repository coollabsio/coolify (65,330 stars, last pushed today), licensed Apache-2.0. It adds 190 tokens to every session and 2,768 once invoked, about $0.0008 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 2026-10-02.
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