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jcools1977

Synaptic Pruning

by John DeVere Cooley · GitHub ↗ · v1.0.0
darwinlinuxwin32 ⚠ suspicious
283
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0
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2
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1
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Install in OpenClaw
/install synaptic-pruning
Description
Identifies vestigial code — not just unused imports, but dead feature branches still compiled, zombie configurations nobody reads, orphaned tests that valida...
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install synaptic-pruning
  3. After installation, invoke the skill by name or use /synaptic-pruning
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release — Identifies vestigial code, zombie features, and fossil configurations
Metadata
Slug synaptic-pruning
Version 1.0.0
License
All-time Installs 2
Active Installs 2
Total Versions 1
Frequently Asked Questions

What is Synaptic Pruning?

Identifies vestigial code — not just unused imports, but dead feature branches still compiled, zombie configurations nobody reads, orphaned tests that valida... It is an AI Agent Skill for Claude Code / OpenClaw, with 283 downloads so far.

How do I install Synaptic Pruning?

Run "/install synaptic-pruning" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Synaptic Pruning free?

Yes, Synaptic Pruning is completely free (open-source). You can download, install and use it at no cost.

Which platforms does Synaptic Pruning support?

Synaptic Pruning is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux, win32).

Who created Synaptic Pruning?

It is built and maintained by John DeVere Cooley (@jcools1977); the current version is v1.0.0.

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