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Supervised Agentic Loop

作者 Nefas11 · GitHub ↗ · v0.1.2 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install supervised-agentic-loop
功能描述
Self-improving AI agent loop with built-in misalignment detection. An AI agent autonomously runs Brainstorm → Plan → Implement → Review → Evolve cycles — kee...
安全使用建议
This package appears coherent for its stated purpose, but it runs code and modifies your repository: 1) Run it only in an isolated working directory or disposable git clone (it creates branches and will reset/rollback). 2) Review/limit the metric_command you pass — it will be executed (it can do anything the shell allows). 3) The agent you provide can be a local subprocess (MONITOR_LLM_COMMAND or an AgentCallable); that subprocess may itself perform network I/O — treat it as untrusted unless you control it. 4) Optional Telegram alerts require you to provide a bot token/chat id; leave those unset to prevent outbound alerts. 5) Inspect git_isolation and verification gate code before use to confirm rollback semantics and which files are checked. If you want tighter isolation, run inside a container or VM, or add stricter verification/read-only rules before enabling autonomous runs.
功能分析
Type: OpenClaw Skill Name: supervised-agentic-loop Version: 0.1.2 The 'supervised-agentic-loop' bundle is a sophisticated framework for autonomous code optimization with a strong emphasis on security and alignment monitoring. It implements a multi-phase loop (Brainstorm, Plan, Implement, Review, Verify, Evolve) that uses git-based isolation for safe rollbacks and reputation scoring to suspend underperforming or 'hallucinating' agents. Most notably, the 'sal/monitor' subpackage provides a two-phase defense system: a synchronous prefilter that blocks dangerous commands (e.g., privilege escalation, persistence, and exfiltration) and an asynchronous LLM-based session review. It also includes a credential sanitizer (sal/monitor/sanitizer.py) to redact secrets from logs and a self-monitoring heartbeat with canary tests. All high-risk behaviors, such as command execution and optional Telegram alerting, are well-documented and strictly aligned with the tool's defensive and experimental purpose.
能力评估
Purpose & Capability
Name/description match the code and metadata: the repository implements an evolve loop that modifies a single target file, uses git isolation, reputation DB, monitoring, and optional Telegram alerts. Required binaries (git, python3) and the declared optional binaries are appropriate for the described functionality.
Instruction Scope
SKILL.md and the code limit modifications to a single target_file and persist state under .state and results.tsv. The loop runs user-supplied metric commands and accepts a user agent callable or local subprocess agent; this necessarily runs arbitrary commands and executes agent outputs (with verification gates). This scope is expected for an autonomous experiment loop, but it means the skill will run arbitrary metric commands and run an agent (possibly a local subprocess) which could perform any network or filesystem actions if configured to do so.
Install Mechanism
Install is a simple 'pip install -e .' via install.sh (no external downloads or opaque URLs). pyproject.toml lists no runtime dependencies, matching the README claim of stdlib-only. No high-risk download/extract operations are present in the manifest.
Credentials
No required environment variables are declared. Optional env vars (SAL_DB_PATH, MONITOR_TELEGRAM_BOT_TOKEN, MONITOR_TELEGRAM_CHAT_ID, MONITOR_LLM_COMMAND, MONITOR_STATE_DIR) are directly related to monitoring, Telegram alerts, or local review subprocess configuration and are justified by the monitor features described.
Persistence & Privilege
The skill persists experiment state to results.tsv and .state/* which is consistent with its purpose. It is not force-included (always: false). The skill can run autonomously (model invocation not disabled), which is typical for an agent skill — combine this with the fact it can run arbitrary metric commands and agent subprocesses when evaluating risk.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install supervised-agentic-loop
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /supervised-agentic-loop 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.2
- Updated description and capability wording to clarify focus on "autonomous-evolution". - Changed user-facing language from "experiment run" to "evolve run" for consistency. - Removed references to "Self-improving experiment loop" in favor of "Self-improving AI agent loop". - Minor formatting and clarity improvements throughout documentation. - No code logic changes.
v0.1.1
- Clarified network access is opt-in and off by default; SAL operates fully local unless specific env vars are set. - Updated description and env_vars to emphasize local operation and user-controlled network behavior. - Revised network_access section: Telegram alerts are optional and only used if configuration is present; LLM subprocesses run locally with user-controlled network access. - Minor edits to metadata for consistency on install methods. - Removed references to “session-review” capability for accuracy and streamlined documentation language.
v0.1.0
- Initial release of supervised-agentic-loop: a self-improving AI agent loop with built-in misalignment detection. - Implements Brainstorm → Plan → Implement → Review → Verify → Evolve experimentation cycles with persistent learning and auto-git isolation. - Introduces a two-phase safety system: synchronous rule-based blocking for destructive commands, and asynchronous LLM-based review for subtle misalignment. - Includes reputation scoring, 10 misalignment behavior patterns, and optional Telegram alerting for high-severity events. - Offers flexible usage: CLI, Python API, and standalone monitoring module. - No external Python dependencies; all core functions run on stdlib only.
元数据
Slug supervised-agentic-loop
版本 0.1.2
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Supervised Agentic Loop 是什么?

Self-improving AI agent loop with built-in misalignment detection. An AI agent autonomously runs Brainstorm → Plan → Implement → Review → Evolve cycles — kee... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 165 次。

如何安装 Supervised Agentic Loop?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install supervised-agentic-loop」即可一键安装,无需额外配置。

Supervised Agentic Loop 是免费的吗?

是的,Supervised Agentic Loop 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Supervised Agentic Loop 支持哪些平台?

Supervised Agentic Loop 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Supervised Agentic Loop?

由 Nefas11(@nefas11)开发并维护,当前版本 v0.1.2。

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