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SkillsMP a collecté 18 skills depuis cobusgreyling/agent-skills. Ouvrez un skill pour examiner sa source et ses détails.
Affichage de 18 skills collectés sur 18.
Manage what enters and stays in the context window — pruning, compaction, summary fidelity, ordering — so the agent stays coherent on long runs without inflating cost. Use when the user is hitting context limits, running long agentic loops, paying for…
Langue du texte source : anglais
Design where, when, and how a human gates, reviews, or rescues an LLM agent — without turning the agent into a button labelled "approve". Use when the user is building an agent that takes irreversible actions or operates in regulated workflows and mentions…
Langue du texte source : anglais
Design and validate LLM-as-judge scoring — pairwise vs pointwise, bias correction, anchor calibration, and the cases where a judge is the wrong tool. Use when the user is building an eval, scoring open-ended outputs, or comparing model versions and mentions…
Langue du texte source : anglais
Design memory for an LLM agent — what to keep, where to keep it, and when memory hurts more than it helps. Use when the user is adding memory to an agent and mentions short-term memory, long-term memory, episodic, semantic, conversation history, summary…
Langue du texte source : anglais
Pick the right model per call, not per project — route Opus/Sonnet/Haiku, GPT-5/4o/mini, Gemini Pro/Flash by task, and cut cost without losing quality. Use when the user is choosing model tiers, building a router, or debating Opus-only vs mixed-tier…
Langue du texte source : anglais
Decide when to split work across multiple agents vs one agent with tools, and design the handoffs when you do. Use when the user is sketching a multi-agent system or debugging one, and mentions handoff, delegation, supervisor, swarm, crew, sub-agent,…
Langue du texte source : anglais
Defend an LLM agent against prompt injection — direct, indirect, tool-result, and document-borne. Use when the user is building an agent that reads untrusted content (web pages, emails, documents, tool outputs) or exposes user-provided text to a downstream…
Langue du texte source : anglais
Get reliable structured output (JSON, typed objects) out of an LLM without regex repair, retry loops, or silent corruption. Use when the user is parsing model output, fighting malformed JSON, comparing JSON mode vs function calling vs structured outputs, or…
Langue du texte source : anglais
Design retry, idempotency, timeout, and recovery behaviour for an agent's tool calls — not the schema (that's a separate skill), but the runtime semantics. Use when the user is building or debugging an agent's tool loop and mentions retries, idempotency keys,…
Langue du texte source : anglais
Choose the right architecture for an LLM agent or multi-agent system. Use when the user is designing, comparing, or debugging agentic workflows and mentions ReAct, Reflexion, Plan-and-Execute, Router, Supervisor, Hierarchical, multi-agent, tool-use loop,…
Langue du texte source : anglais
Model the cost of an LLM agent before it ships, and after. Use when the user is planning a deployment, comparing patterns, choosing a model tier, or justifying a budget and mentions tokens per task, cost per task, unit economics, cost ceiling, cache hit rate,…
Langue du texte source : anglais
Design an evaluation harness for an LLM agent before shipping it. Use when the user is building or rewriting an agent, deciding ship/no-ship, debugging regressions, or mentions golden sets, eval suites, regression tests, trace-level evals, LLM-as-judge,…
Langue du texte source : anglais
Instrument an LLM agent so failures are diagnosable, traces are replayable, and evals can run against production data. Use when the user is moving an agent past prototype and mentions tracing, spans, OpenTelemetry, LangSmith, Langfuse, Arize, OpenLLMetry,…
Langue du texte source : anglais
Design guardrails for an LLM agent that handles user input, calls real tools, or operates in a regulated domain. Use when the user is building a user-facing agent and mentions guardrails, jailbreaks, prompt injection, content moderation, PII redaction, output…
Langue du texte source : anglais
Budget and engineer latency for an LLM agent — TTFT, tokens-per-second, tool round-trips, parallelism, streaming. Use when the user is building a user-facing or real-time agent and mentions latency, p50, p95, p99, TTFT, streaming, throughput,…
Langue du texte source : anglais
Use prompt caching correctly across Anthropic, OpenAI, Bedrock, and Gemini to cut cost and latency on hot paths. Use when the user is building a production LLM app and mentions prompt caching, cache hits, cache key, cache TTL, ephemeral cache, system-prompt…
Langue du texte source : anglais
Decide between RAG, long-context, structured tool retrieval, and prompt-only approaches for grounding an LLM in private or fresh data. Use when the user is designing a knowledge-grounded agent or chatbot and mentions RAG, vector search, embeddings, retrieval,…
Langue du texte source : anglais
Design tool schemas (function-calling definitions) that LLMs can use reliably. Use when the user is defining tools for Claude, GPT, Gemini, or any function-calling agent and mentions tool definitions, function calling, JSON schema, tool descriptions,…
Langue du texte source : anglais