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 woliveiras/geremmyas --skill llm-integration-reviewgit clone --depth 1 https://github.com/woliveiras/geremmyasWrote 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/woliveiras/geremmyas/llm-integration-review)<a href="https://agentmods.dev/skills/woliveiras/geremmyas/llm-integration-review"><img src="https://agentmods.dev/badge/skills/woliveiras/geremmyas/llm-integration-review/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/woliveiras/geremmyas/llm-integration-review"><img src="https://agentmods.dev/badge/skills/woliveiras/geremmyas/llm-integration-review.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 | $0.00049 | $0.00332 |
| Opus 5 | $0.00024 | $0.00166 |
| Sonnet 5 | $0.00010 | $0.00066 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
llm-integration-review 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 12d 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
LLM Integration Review
Review LLM service boundaries before code spreads across handlers and domain logic.
Process
- Identify the user-facing capability, model provider, model, latency target, cost risk, and data sensitivity.
- Find the service boundary that owns model calls, prompts, tools, retries, and structured outputs.
- Verify inputs are validated and private data is minimized or redacted before logging/tracing.
- Prefer structured outputs for machine-read results.
- Define timeout, retry, backoff, rate limit, and fallback behavior.
- Treat tool calls as side effects: authorize, make idempotent where possible, and record audit context.
- Add contract tests for prompt inputs, tool schemas, structured outputs, refusals, provider errors, and retry behavior.
- Document operational knobs: model, temperature, token limits, and cost controls.
Rules
- Do not put provider SDK calls directly in route handlers.
- Do not parse critical machine-readable results from free-form prose.
- Do not log secrets, credentials, full private documents, or raw user data.
- Do not let model output authorize itself or choose privileged operations without application checks.
Output
- Boundary and data-flow summary
- Risk checklist
- Required tests
- Operational settings and follow-ups
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.
- 12d ago First seen · 43 lines · 49 tokens per session scan A 2ab4e032135c
llm-integration-review is a skill published in the GitHub repository woliveiras/geremmyas (10 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 332 once invoked, about $0.0002 per session on Opus 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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