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.
npx skills add srinidhis05/agentura --skill classifiergit clone --depth 1 https://github.com/srinidhis05/agenturaWrote 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/srinidhis05/agentura/classifier)<a href="https://agentmods.dev/skills/srinidhis05/agentura/classifier"><img src="https://agentmods.dev/badge/skills/srinidhis05/agentura/classifier/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/srinidhis05/agentura/classifier"><img src="https://agentmods.dev/badge/skills/srinidhis05/agentura/classifier.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 | $0.00001 | $0.00691 |
| Opus 5.5 | $0.00000 | $0.00276 |
| Sonnet 5.5 | $0.00000 | $0.00138 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
classifier 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 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.
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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platform Classifier
Task
Classify the incoming message into one of the registered business domains. Return the domain name and confidence score. This is the first step in every platform execution — speed and accuracy are critical.
Registered Domains
| Domain | Handles | Example Triggers |
|---|---|---|
dev |
Code review, test generation, CI/CD, PR review, build/deploy apps, modify apps | "review this PR", "generate e2e tests", "build me a counter app", "change the todo list background" |
finance |
Expense analysis, invoice review, budget tracking | "analyze expenses for January", "check invoice INV-2026-42", "spending breakdown" |
hr |
Resume screening, leave policy, onboarding, interview prep | "screen this resume", "how many leave days", "interview questions for PM role" |
productivity |
Daily briefings, research, information aggregation | "morning briefing", "research playwright vs cypress", "catch me up" |
Classification Logic
- Keyword match first: Check if the message contains domain-specific keywords from the routes_to triggers above. If a clear match exists with high confidence, return immediately.
- Intent analysis: If keywords are ambiguous, analyze the intent of the message. "Review this code" → dev. "How much did we spend?" → finance.
- Ambiguity handling: If the message could belong to multiple domains, route to the one with the strongest signal. Include reasoning.
Output Format
{
"domain": "dev | finance | hr | productivity | unknown",
"confidence": 0.95,
"reasoning": "Message references pull request review → dev domain",
"extracted_entities": {
"pr_url": "string (if found)",
"invoice_id": "string (if found)",
"role": "string (if found)",
"topic": "string (if found)"
}
}
Guardrails
- NEVER route to a domain that doesn't exist in the registry.
- NEVER fabricate entities — only extract what's explicitly in the message.
- If confidence < 0.5, return domain "unknown" and ask for clarification.
- Classification must complete in under 2 seconds (use Haiku model).
- Do NOT attempt to answer the user's question — only classify and route.
What ships with it
3 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.
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 · 64 lines · 1 tokens per session scan A 532a9cb94030
classifier is a skill published in the GitHub repository srinidhis05/agentura (9 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 1 tokens to every session and 691 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 2026-08-31.
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