llm-patterns

Plugin llm-patterns from marketplace skillforge in Entelligentsia/skillforge. Commands read from the repo's manifest — 1 month ago.

Skills (10)

• agent-loop

  Use when a task requires autonomous multi-step reasoning — the LLM must observe, decide, act, and iterate until a goal is met or a termination condition is reached. Apply when a single prompt cannot solve the task, the number of steps is not known in advance, and the next step depends on the result

• evaluation-harness

  Use when you cannot systematically measure whether your LLM feature is working correctly. Apply when testing is based on vibes rather than metrics, when you cannot detect regressions after prompt changes, or when production quality is unknown. Covers evaluation datasets, metrics, regression testing,

• graceful-degradation

  Use when an LLM-powered feature must remain functional when the primary model is slow, down, over budget, or producing low-quality results. Apply when building any production AI feature that users depend on. Covers fallback chains, semantic routing, circuit breakers, cost management, and degradation

• guardrails

  Use when LLM inputs or outputs must be validated for safety, policy compliance, schema conformance, or content appropriateness before they reach users or downstream systems. Apply when LLM responses could contain harmful content, PII leakage, prompt injection, off-topic responses, or policy violatio

• llm-pattern-selection

  INVOKE THIS FIRST before designing any LLM-powered feature. Use when integrating an LLM as a component in a software system — not as a chat interface, but as a decision-making, data-processing, or logic-executing building block. Maps the friction you feel to the pattern that removes it.

• prompt-engineering

  Use when prompts are ad-hoc strings scattered through code, hard to test, version, or maintain. Apply when building any LLM-powered feature to ensure prompts are treated as first-class engineering artifacts — versioned, tested, composable, and separated from application logic. Covers prompt structur

• rag

  Use when LLM responses must be grounded in factual, up-to-date, or domain-specific data that the model was not trained on. Apply when the LLM hallucinates facts, gives outdated information, lacks knowledge of proprietary data, or needs to cite sources. Covers Retrieval-Augmented Generation — retriev

• structured-generation

  Use when LLM output must conform to a specific schema, type, or format — JSON responses, typed function returns, database records, API payloads. Apply when free-text LLM output breaks downstream parsing, requires fragile regex extraction, or produces inconsistent shapes across calls. Covers schema e

• tool-synthesis

  Use when an LLM is repeatedly performing a task that could be codified as a deterministic or semi-deterministic script, tool, or function. Apply when inference cost, latency, or non-determinism is the pain — the LLM evaluates whether to generate a reusable artifact that replaces itself for a class o

• tool-use

  Use when an LLM needs to interact with external systems — calling APIs, querying databases, reading files, executing code, or performing actions with side effects. Apply when the LLM must go beyond text generation to retrieve live data, perform calculations, or trigger real-world operations. Covers

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