The ROI of Agent Skills: Where the Savings Come From
A practical cost model for Claude Agent Skills — where tokens go, how savings are created, and how to decide if a Skill is worth building.
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A practical cost model for Claude Agent Skills — where tokens go, how savings are created, and how to decide if a Skill is worth building.
A phased, low-risk plan to move an existing workflow onto Claude agents and Skills — shadow mode, human-in-the-loop, staged autonomy, and rollback.
Measure Claude agent quality and gate releases with an eval loop — test sets, scoring rubrics, LLM-as-judge, and CI gates that catch regressions early.
Harden Claude agents with sandboxing, least-privilege tool scopes, secret isolation, and layered defenses against prompt injection.
Keep Claude agents fast and cheap with prompt caching, batching, smart model routing, and context discipline — without sacrificing answer quality.
Diagnose the real failure modes of Claude agents — infinite loops, wrong tool calls, and hallucinated arguments — with concrete, trace-driven fixes.
What to put in an agent's context and what to leave out — practical prompt and context design for Claude Agent Skills that stay sharp over long runs.
Connect tools and MCP servers to Claude agents and skills with safe auth, clean schemas, honest error handling, and idempotency for reliable tool calls.
Code-level patterns for Claude Agent Skills: checkpointed procedures, deterministic cores, thin bodies, clean composition, and skill testing that scales.
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