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Synaptic Pruning
作者
John DeVere Cooley
· GitHub ↗
· v1.0.0
283
总下载
0
收藏
2
当前安装
1
版本数
在 OpenClaw 中安装
/install synaptic-pruning
功能描述
Identifies vestigial code — not just unused imports, but dead feature branches still compiled, zombie configurations nobody reads, orphaned tests that valida...
安全使用建议
This skill's goal (find and remove 'vestigial' code) is reasonable, but the runtime instructions are broad and imply access to CI/deployment logs and production telemetry that are not described in the skill metadata. Before installing or running it: (1) insist on human review and require the skill to run in a sandbox or on a local copy of the repo first; (2) do not provide production/analytics credentials until you verify exactly which endpoints will be accessed and why; (3) require the skill author to specify the exact data sources (logs, analytics, CI) and the minimal credentials needed; (4) apply conservative thresholds (explicit N) and require an approval step before any code deletion or automated pruning; (5) keep backups and ensure changes go through code review/PRs rather than automatic removal. If the author can provide the full SKILL.md runtime steps that show constrained, read-only access patterns and explicit handling of production telemetry, that would raise confidence; if they expect the agent to autonomously query production systems without explicit credential scoping or human-in-the-loop safeguards, treat it as high risk.
功能分析
Type: OpenClaw Skill
Name: synaptic-pruning
Version: 1.0.0
The OpenClaw skill 'synaptic-pruning' is designed for identifying and reporting dead or vestigial code within a codebase. The `SKILL.md` file contains detailed instructions for an AI agent on how to perform this analysis, including detection strategies for various types of dead code and a multi-phase pruning process. All instructions are directly aligned with the stated purpose of code analysis and health reporting. There is no evidence of malicious intent, such as data exfiltration, unauthorized command execution, persistence mechanisms, or prompt injection aiming to subvert the agent for harmful activities. The skill explicitly states 'Zero external dependencies. Zero API calls.', reinforcing its benign nature.
能力评估
Purpose & Capability
Name and description claim a codebase 'pruner' that finds unused features, configs, tests, shims, and modules. The SKILL.md describes detection techniques that operate over source, tests, config, and docs — this aligns with the stated purpose and does not request unrelated capabilities.
Instruction Scope
The provided instructions are high-level and grant broad discretion (e.g., 'Trace every UI element and API endpoint to user-reachable paths', 'Flag features with zero invocations in the last N deployment cycles', 'Cross-reference documentation against CI/CD and infrastructure definitions'). These steps implicitly require reading the repository, CI configs, deployment metadata, and production/telemetry logs or analytics. The SKILL.md does not detail how such logs or external systems should be accessed, nor does it constrain what external endpoints or data sources the agent may query. That vagueness could cause the agent to attempt access to sensitive systems or to make destructive changes if executed without human oversight.
Install Mechanism
This is an instruction-only skill with no install spec and no code files. Nothing will be written to disk by an installer managed by the skill package itself, which minimizes installation risk.
Credentials
The skill declares no required environment variables, credentials, or config paths. However, the detection techniques described make it likely that the agent will need access to CI/CD configs, deployment metadata, invocation logs, analytics, or cloud provider consoles to meaningfully determine 'zero invocations in the last N deployment cycles'. The absence of declared credential needs is a mismatch: either the skill expects the agent to run with repository/local context only, or it omits asking for the specific credentials required to access production telemetry. This should be clarified and credential requests scoped minimally.
Persistence & Privilege
The skill does not request always: true, does not include install-time hooks or persistent presence, and does not declare permissions to modify other skills or global agent settings. Autonomous invocation is allowed by default (normal), but there are no exceptional persistence privileges requested.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install synaptic-pruning - 安装完成后,直接呼叫该 Skill 的名称或使用
/synaptic-pruning触发 - 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
v1.0.0
Initial release — Identifies vestigial code, zombie features, and fossil configurations
元数据
常见问题
Synaptic Pruning 是什么?
Identifies vestigial code — not just unused imports, but dead feature branches still compiled, zombie configurations nobody reads, orphaned tests that valida... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 283 次。
如何安装 Synaptic Pruning?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install synaptic-pruning」即可一键安装,无需额外配置。
Synaptic Pruning 是免费的吗?
是的,Synaptic Pruning 完全免费(开源免费),可自由下载、安装和使用。
Synaptic Pruning 支持哪些平台?
Synaptic Pruning 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux, win32)。
谁开发了 Synaptic Pruning?
由 John DeVere Cooley(@jcools1977)开发并维护,当前版本 v1.0.0。
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