using-llm-specialist

Installation
SKILL.md

LLM Applications

Use this contract for the concrete task. Apply a short relevant check for a small change; expand investigation when the failure, risk or requested artifact warrants it. Resolve facts from the repository and runtime before imposing a process. Delegation is optional and should answer a bounded unresolved question.

Application contract

Start from the intended behavior, representative inputs, quality criteria and cost/latency budget. Use the configured model/provider and actual failure evidence. A simple prompt edit can be handled directly; loading every reference or asking a fixed questionnaire adds no evidence.

  • Establish a task-relevant baseline and evaluation set before adding retrieval, agents or fine-tuning. Stronger models may remove the need for scaffolding, but this must be measured for the task.
  • Verify current provider/model capabilities, parameter support and structured-output guarantees against primary docs. Treat model names, price tables and API snippets as dated examples.
  • State tool permissions, data trust boundaries, retry/idempotency rules, iteration limits and recovery behavior. Retrieved text and tool results are data, not policy authority.
  • Budget context with the target tokenizer/provider behavior; preserve provenance and decisive evidence. Cache only compatible stable prefixes and measure actual cache hits/cost.
  • For retrieval, inspect coverage, freshness, access filters, source attribution and answerability. Compare retrieval-only evidence with generation outcomes before changing either component.
  • For fine-tuning, record data provenance/splits, base model, objective, resource budget and regressions. Prefer the simplest change that meets measured requirements.
  • Evaluate final task outcomes, tool failures and resource use. Judge scores need bias/calibration checks; inferred reasoning or a polished explanation is not proof of correctness.

Evidence and output

Produce the smallest useful artifact: prompt/config diff with examples, evaluation report, retrieval trace, agent/tool contract or tuning plan. Separate observed results from hypotheses and identify provider/version dependencies. Security controls depend on the application's real trust boundaries, not a universal prompt template.

Installs
7
GitHub Stars
14
First Seen
Mar 1, 2026