agent-architecture
Design agent loops: planning, tool use, subagents, memory, RAG, evals, budgets.
Skill by chnetajibc
Install
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
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, ml, security, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
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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Handle errors explicitly: typed errors, no silent swallowing, no catch-all.
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Per-language module structure: Python, TypeScript, JavaScript, Go, Rust, Java.
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Enforce module contracts and boundaries: frontend/backend separation, repo guardrails.
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Extendable data models: identity, lifecycle, constraints, indexes, transactions, versioning, migrations, deletion, auditing.
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authentication
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Authentication: JWT/OAuth-OIDC/sessions/API keys, password storage, secrets, CSRF/CORS/cookies.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 22, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/chnetajibc/skill-repo
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
agent-architecture skill. Agent Mart. https://agentmart.dev/skills/chnetajibc-agent-architecture (last updated 2026-10-24)