# Agent Llm

> Patterns for building on the Claude/Anthropic API — agent loops, tool design, prompt structure, structured output, context management, prompt caching, and evals. Use when writing code that calls Claude, designing an agentic loop, defining tools for an LLM, building RAG or an LLM-judge, or debugging refusals, truncation, streaming, or tool-call failures.

- **Type:** Skill
- **Install:** `agentstack add skill-ksed8-cc-loopkit-agent-llm`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ksed8](https://agentstack.voostack.com/s/ksed8)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ksed8](https://github.com/ksed8)
- **Source:** https://github.com/ksed8/cc-loopkit/tree/main/.claude/skills/agent-llm

## Install

```sh
agentstack add skill-ksed8-cc-loopkit-agent-llm
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Agent & LLM Patterns (Claude)

Guidance for writing application code that calls Claude well. This is about _how to structure the integration_, not the API surface.

**Before writing any code that names a Claude model, calls the Anthropic SDK, or picks a model tier, load the `claude-api` skill** — it has current model IDs, pricing, limits, and parameter details. Do not hardcode model IDs or pricing from memory; they go stale.

## Prompt structure

- Put stable, cacheable content first (system prompt, tool defs, long context); put the variable turn last. This maximizes prompt-cache hits.
- Be explicit about the output contract. If you need JSON, say the exact shape and provide one example. Don't ask for "JSON" and hope.
- Give the model an escape hatch ("if you cannot determine X, return `null`") so it stops fabricating to satisfy the format.
- Separate instructions from data with clear delimiters (XML tags work well). Never interpolate untrusted user text directly into an instruction sentence — treat it as data inside a tag.

## Tool design

- One tool = one capability with a crisp verb name. Overlapping tools cause the model to dither.
- The `description` is the prompt the model reads to decide _when_ to call — write it for that decision, not as API docs. State when to use it and when NOT to.
- Make schemas strict: enums over free strings, required fields marked, no ambiguous optional soup. A tight schema removes a class of retries.
- Return errors the model can act on. `{"error":"row not found","hint":"check the id"}` beats a stack trace. The model reads tool results as its next observation.
- Prefer returning structured data over prose from a tool; let the model narrate.

## Agentic loops

- The loop is: model → tool_use → you execute → tool_result → model, until it stops calling tools. Keep executing until there are no more tool calls, then surface the final text.
- Bound the loop (max iterations / token budget) so a confused model can't spin forever. Log each step's tool + args for debuggability.
- For structured final output, force a terminal tool call (a `submit`/`finish` tool with the result schema) rather than parsing prose — validation happens at the tool layer and the model retries on mismatch.
- Isolate side-effecting tools (writes, sends, deletes) behind confirmation or a dry-run flag when the loop is autonomous.

## Context management

- Don't dump whole files/tables into context — retrieve the relevant slice. More context is slower, costlier, and dilutes attention.
- For long-running agents, summarize prior steps into a compact running state rather than carrying the full transcript.
- RAG: retrieve, then cite. Have the model quote the source span it used so answers are checkable; return `null`/"not found" when retrieval is empty instead of guessing.

## Prompt caching

- Cache the system prompt, tool definitions, and any large shared context (5-minute TTL). Structure calls so the cached prefix is byte-identical across requests.
- A single changed byte early in the prompt busts the whole downstream cache — keep volatile content (timestamps, user turn) at the end.

## Evals — don't ship a prompt you haven't measured

- Build a small labeled set of real inputs with expected outputs _before_ tuning the prompt. "It looked good in the playground" is not a signal.
- Use an LLM-judge only for fuzzy criteria; for anything checkable (JSON valid? field present? number in range?) assert it in code.
- Track a regression set. When you change the prompt or model, re-run it — prompt changes have non-local effects.
- Measure cost and latency alongside quality; the cheapest model that passes the eval wins.

## Common failure modes

- **Refusals / cutoffs / streaming / tool-call bugs**: load `claude-api` and check the model's limits and the exact request shape before assuming a logic bug.
- **Truncated output**: the response hit `max_tokens` — raise it or ask for less, don't retry blindly.
- **Model ignores the format**: your instruction and your example disagree, or the format is buried mid-prompt. Move it to the end and make the example match exactly.
- **Non-determinism in tests**: set `temperature: 0` for eval runs; assert on structure/invariants, not exact wording.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ksed8](https://github.com/ksed8)
- **Source:** [ksed8/cc-loopkit](https://github.com/ksed8/cc-loopkit)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ksed8-cc-loopkit-agent-llm
- Seller: https://agentstack.voostack.com/s/ksed8
- Browse the marketplace: https://agentstack.voostack.com/browse

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