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/stratalab/strata-coreWrote 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/stratalab/strata-core/inference.generate)<a href="https://agentmods.dev/commands/stratalab/strata-core/inference.generate"><img src="https://agentmods.dev/badge/commands/stratalab/strata-core/inference.generate.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00156 |
| Opus 5 | $0.00000 | $0.00078 |
| Sonnet 5 | $0.00000 | $0.00031 |
| Haiku 4.5 | $0.00000 | $0.00016 |
Grade A, and why
inference.generate 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 3d 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.
What it actually says
Runs a text-generation request against a local or cloud model and returns the completion, the reason generation stopped, and the provider-reported prompt and completion token counts. The request controls the maximum completion tokens, sampling temperature, top-k and top-p cutoffs, an optional deterministic seed, string and token-id stop sequences, and an optional GBNF grammar for constrained generation. Chat models expect their chat template already applied in the prompt. Local models require a build with the local execution feature; cloud providers require the matching provider feature and an API key.
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.
- 3d ago First seen · 7 lines · 0 tokens per session scan A becc03f3ca2b
inference.generate is a command published in the GitHub repository stratalab/strata-core (2 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 156 tokens. 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 2026-09-03.
Other commands, from other repositories
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System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
prompt-show
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ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.