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Visualize an agent's perceive→think→act loop. Inspects tool calls, latency per step, decision branches, retry patterns. Surfaces bottlenecks. Triggers on "trace agent", "why is agent slow", "agent loop analysis", "what did the agent do".
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Compute context window budget for a prompt/agent — sums system prompt, tool descriptions, examples, conversation history; flags overruns; suggests cuts. Triggers on "context budget", "how much context", "will this fit", "budget my prompt".
Treat dataset / eval JSONL as code — lint, validate schema, detect duplicates, leakage between train/eval, label imbalance, PII. Karpathy "Software 2.0" lens. Triggers on "audit dataset", "lint jsonl", "check for leakage", "validate eval data".
Enforce "evals over vibes" — before any prompt, agent, or model change, require an eval set with success criteria and baseline measurement. Blocks vibe-only changes. Triggers on "tune prompt", "improve agent", "change model" without prior eval set.
Build a 10-30 case eval dataset from a PRD, skill, or prompt — covering golden path, edge cases, and adversarial inputs. Output as JSONL with input/expected/category. Triggers on "build eval set", "create evals", "generate test cases for prompt".
Break down any ML/AI concept to first principles — strip jargon, derive from basics, build physical/mathematical analogy. Karpathy intuition-building style. Triggers on "first principles", "explain like I'm 5", "derive from scratch", "why does X actually work".
Generate minimal, runnable from-scratch implementation of LLM concepts (attention, BPE tokenizer, backprop, RoPE, KV-cache, MoE, LoRA, RLHF). Karpathy-style pedagogy — code first, theory in comments. Triggers on "explain X from scratch", "minimal X implementation", "show me how X works in code", "build X from zero".
| name | agent-loop-trace |
| description | Visualize an agent's perceive→think→act loop. Inspects tool calls, latency per step, decision branches, retry patterns. Surfaces bottlenecks. Triggers on "trace agent", "why is agent slow", "agent loop analysis", "what did the agent do". |
Run: agent_xyz (total 47s, $0.18, 23 steps)
Step | t(s)| Tokens | Action
1 | 0.4 | 220/80 | perceive: user prompt
2 | 2.1 | 280/40 | think: plan 3 steps
3 | 8.2 | 120/0 | tool: search ✓
4 | 1.1 | 140/60 | think: refine
5 | 7.8 | 110/0 | tool: search ✗ (same query) ← REDUNDANT
6 | ...
Anti-patterns detected:
• Same search query twice (steps 3, 5)
• Long think gap with no progress (steps 11-14)
• Final answer not grounded in tool results
"How I use LLMs" / "Deep Dive into LLMs" Karpathy talks