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agent-architecture

Design agent loops: planning, tool use, subagents, memory, RAG, evals, budgets.

Skill by chnetajibc

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/chnetajibc-agent-architecture

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About

Design agent loops: planning, tool use, subagents, memory, RAG, evals, budgets. Use when building LLM features or multi-step agents. Security review via security/agent-security.

Use cases: design, ml, security, agent-building

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/agent-architecture

What's inside

Skill name
agent-architecture
Description
Design agent loops: planning, tool use, subagents, memory, RAG, evals, budgets. Use when building LLM features or multi-step agents. Security review via security/agent-security.
License
MIT
Instructions
27 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# agent-architecture

Plan → act → observe → verify. Every loop needs a budget, a memory policy, and an eval.

## Activate when

- Building tool-calling features, RAG pipelines, subagent workflows, or scheduled agents.
- Debugging agent loops, runaway cost, or stale context.

## Do NOT activate for

- MCP server mechanics (see mcp-engineering) or skill authoring (see skill-design).

## Procedure

1. Loop: define states (plan, act, verify, escalate), max steps, and stop conditions; stream progress for long runs.
2. Tools: narrow schemas, idempotent mutations, confirmation for irreversible actions; treat all tool output as untrusted input to the next step.
3. Context: retrieve-then-read (embeddings + rerank), cite sources, budget tokens per phase; compact with extractive summaries, never silent drops.
4. Memory: short-lived working memory vs persisted facts with TTL and provenance; no secrets in memory stores.
5. Models: route by difficulty with fallbacks; structured outputs with schema validation and repair retries.
6. Eval: task-based evals (success rate, cost, latency) before and after each change; log traces for replay.
7. Verify: replay a failing trace, confirm the fix, and record residual risk + owner.

## References
- `../mcp-engineering/`, `../skill-design/`, `../../security/agent-security/`.

From chnetajibc/skill-repo, MIT.

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Last commit (this folder)
Sep 22, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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agent-architecture skill. Agent Mart. https://agentmart.dev/skills/chnetajibc-agent-architecture (last updated 2026-10-24)