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 ArieGoldkin/claude-forge --skill llm-patternsgit clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote 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/ariegoldkin/claude-forge/llm-patterns)<a href="https://agentmods.dev/skills/ariegoldkin/claude-forge/llm-patterns"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/llm-patterns/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/ariegoldkin/claude-forge/llm-patterns"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/llm-patterns.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 | $0.00119 | $0.00877 |
| Opus 5.5 | $0.00048 | $0.00351 |
| Sonnet 5.5 | $0.00024 | $0.00175 |
| Haiku 4.5 | $0.00012 | $0.00088 |
Grade A, and why
llm-patterns 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Patterns
Patterns for building, evaluating, streaming, and testing LLM-powered applications.
Evaluation
Quality assessment and validation of LLM outputs using LLM-as-judge, multi-dimension scoring, pairwise comparison, hallucination detection, and batch evaluation. Integrates with Langfuse for score tracking.
- Use a separate judge model (e.g., GPT-4o-mini, Claude Haiku) to avoid self-evaluation bias
- Score across 3-5 dimensions: relevance, accuracy, completeness, coherence
- Quality threshold: 0.7 for production, 0.6 for drafts
Full patterns and code: See references/evaluation.md
Autonomous Evaluation Loop
Use /experiment to autonomously iterate on evaluator quality (judge prompts, scoring rubrics) against golden datasets, tracking agreement via Langfuse Experiments API. See references/autonomous-evaluation.md for the full pattern and a worked example.
Streaming
Real-time token delivery via Server-Sent Events (SSE) for better UX. Covers OpenAI sync/async streaming, FastAPI SSE endpoints, TypeScript frontend consumers, streaming with tool calls, and backpressure handling.
- Use SSE for web, WebSocket for bidirectional communication
- Buffer 50-200 tokens; set 30-60s timeouts for long responses
- Always close connections properly and handle stream errors
Full patterns and code: See references/streaming.md
Testing
Deterministic testing patterns for non-deterministic LLM outputs. Covers quality gate tests, template rendering, edge cases (empty/long inputs, partial responses), and VCR.py for recording API responses in CI.
- Use VCR for integration tests, mocks for unit tests
- Always test with < 1s timeout; validate both valid and invalid schemas
- Record mode "none" in CI to avoid live API calls
Full patterns and code: See references/testing.md
What ships with it
4 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.
- 1mo ago First seen · 81 lines · 119 tokens per session scan A 8b04c1739cec
llm-patterns is a skill published in the GitHub repository ArieGoldkin/claude-forge (7 stars, last pushed 2mo ago), licensed MIT. It adds 119 tokens to every session and 877 once invoked, about $0.0005 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.
Other skills, from other repositories
dataset-evaluation
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model…
llm-evaluation
LLM evaluation and testing patterns including prompt testing, hallucination detection, benchmark creation, and quality metrics. Use when testing LLM applications, validating prompt quality, implementing systematic evaluation, or measuring LLM performance.
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
llm-eval
LLM evaluation: build evaluation datasets, choose metrics (RAGAS, G-Eval, LLM-as-judge), run automated evals, monitor production quality, and detect regressions.
scikit-learn-train-test-splittings
Guidelines and instructions for Scikit-learn train test splittings.
cli-eval
Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.