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Agenter Coder

通过自带 CLI 脚本把写代码、改代码、修 bug、生成测试等任务委派给独立进程中的自主编码代理，支持 Anthropic SDK、Claude Code、Codex、OpenHands 四种后端，带 AST 校验、可选安全扫描、自动重试，并对费用、token、时间设预算上限，返回结构化 JSON。

gabriel-hurtado
gabriel-hurtado
热度 37.05
最近更新 2026-05-11 17:36:07
分类：开发依赖：代码执行 / 读写文件 / 内部调用大模型 / 第三方 API应用场景：代码生成 / 调试排错 / 自动化脚本

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add agenter-coder
Metadata
name
agenter-coder
description
Delegate coding tasks to a separate autonomous agent with AST validation, security scanning, and automatic retry. Supports 4 backends (Claude Code, Codex, OpenHands, Anthropic SDK) with hard budget limits on cost, tokens, and time. Returns structured JSON — not chat text.
tags
["coding","agent","delegation","multi-backend","validation","budget-control","autonomous","claude","codex","openhands"]
user-invocable
true
metadata
{"openclaw":{"primaryEnv":"ANTHROPIC_API_KEY","requires":{"bins":["python3","uv"]},"installer":[{"type":"command","command":"uv pip install agenter>=0.1.2"}]}}

Agenter Coder

Instead of writing code tool-by-tool (filling your context window with file contents), delegate to a purpose-built coding agent that runs in its own process. It has its own tools, validates its output with AST parsing, and comes back with structured results. Your context stays clean.

Why this instead of coding directly

  • Your context window stays clean. The sub-agent does all the file reading, editing, and bash execution in its own process. You only see the final result.
  • Automatic validation and retry. Every iteration runs AST syntax checks (and optional Bandit security scans). If code has errors, the agent retries automatically — no manual back-and-forth.
  • Hard budget enforcement. Set a dollar limit, token limit, or time limit. The agent stops when it hits the cap — no surprise bills.
  • Backend portability. Same interface whether you're using Claude, GPT, or open-source models. Switch with one flag.

When to use

Use this skill when the user asks to:

  • Write, create, or generate code for a project
  • Modify, refactor, or update existing code
  • Fix bugs in a codebase
  • Create entire applications or components
  • Generate tests for existing code

Do NOT use for: reading files, explaining code, or answering questions. Use your own tools for those — they don't need a sub-agent.

How to run

python3 {SKILL_DIR}/scripts/agenter_cli.py \
  --prompt "<the coding task>" \
  --cwd "<workspace directory>" \
  --backend "anthropic-sdk" \
  --max-cost-usd 2.0 \
  --max-iterations 5 \
  --sandbox

Parameters

Flag Required Default Description
--prompt Yes — The coding task. Be specific about what to build.
--cwd Yes — Working directory. Use the current workspace or a subdirectory.
--backend No anthropic-sdk Runtime: anthropic-sdk, claude-code, codex, or openhands.
--model No auto Model override (e.g., claude-sonnet-4-20250514, gpt-5.4).
--max-cost-usd No unlimited Maximum spend in USD.
--max-tokens No unlimited Maximum total tokens (input + output).
--max-time-seconds No unlimited Maximum wall clock time.
--max-iterations No 5 Max validation/retry iterations.
--allowed-write-paths No all in cwd Glob patterns for allowed writes (e.g., "*.py" "*.ts").
--sandbox / --no-sandbox No --sandbox Sandboxed execution (recommended).
--stream No off Emit NDJSON progress events for real-time updates.

Cost awareness

Set budget limits based on task complexity. Always tell the user the estimated cost.

Task type Suggested --max-cost-usd Suggested --max-iterations
Simple script / single file 0.50 3
Small app / multiple files 2.00 5
Complex refactoring / full project 5.00 7

Backend selection

Default to anthropic-sdk unless the user asks for a specific backend. Check {SKILL_DIR}/references/backends.md if the user asks about backend differences.

  • anthropic-sdk — Default. Claude Sonnet/Opus. Works with ANTHROPIC_API_KEY or AWS Bedrock.
  • claude-code — Claude Code CLI runtime. Native OS-level sandbox, battle-tested file tools.
  • codex — OpenAI's gpt-5.4/gpt-5.4-mini. Requires OPENAI_API_KEY.
  • openhands — Any model via litellm (including local). Must use --no-sandbox.

Reading the output

The script outputs JSON to stdout:

{
  "status": "completed",
  "summary": "Created main.py with FastAPI app and test_main.py",
  "files_modified": ["main.py", "test_main.py"],
  "files": {"main.py": "...", "test_main.py": "..."},
  "iterations": 2,
  "total_tokens": 15000,
  "total_cost_usd": 0.045,
  "total_duration_seconds": 12.3
}

Status values

Status Meaning What to do
completed Task succeeded, files written to disk. Report summary and files to user.
completed_with_limit_exceeded Task succeeded but used more resources than configured. Report success + warn about cost.
budget_exceeded Stopped because budget ran out before completion. Tell user, ask if they want to retry with higher budget.
refused The model refused the request (safety/policy). Report refusal reason to user.
failed Unrecoverable error. Report error, suggest checking logs.

After running

  1. Check the status field.
  2. If completed: the files are already written to disk in --cwd. Use read to inspect them if the user wants to review.
  3. Report the summary, cost, and files modified to the user.
  4. If failed or budget_exceeded: report the issue and ask how to proceed.