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splitsym
by
TurinFohlen
· GitHub ↗
· v1.0.0
· MIT-0
44
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0
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0
Active Installs
1
Versions
Install in OpenClaw
/install splitsym
Description
Extract comments and split symbols from source files. Use when users want to extract inline comments, docstrings, or block comments from code files to unders...
Usage Guidance
This skill appears to do what it says: it reads the target source file and a local symbols.json to extract comments/docstrings and prints snippets. It does not contact the network or request secrets. Two practical notes before installing or running: (1) the tool expects a config at ~/.config/splitsym/symbols.json by default — either create that file or invoke with --config pointing to the included symbols.json, and (2) the config contains regex patterns: only use/trust configs from sources you control because malicious or pathological regexes could cause unexpected behavior or processing delays on very large files.
Capability Analysis
Type: OpenClaw Skill
Name: splitsym
Version: 1.0.0
The 'splitsym' skill is a legitimate utility designed to extract comments and docstrings from source code files. The Python implementation (splitsym.py) uses standard libraries to perform regex-based pattern matching against a configurable set of rules (symbols.json). There are no indicators of data exfiltration, malicious command execution, or prompt injection; the tool's behavior is entirely consistent with its documentation.
Capability Assessment
Purpose & Capability
Name/description match the shipped files: splitsym.py implements a comment/docstring extractor and symbols.json contains the extraction rules. There are no unrelated env vars, binaries, or cloud credentials requested.
Instruction Scope
SKILL.md instructs running the tool against a file and (optionally) a config; the implementation only reads the target file and a local config and prints comment snippets. It does not read other system files, environment variables, or send data to external endpoints.
Install Mechanism
This is an instruction-only/standalone Python script with no install spec. There is no remote download, package install, or archive extraction specified in the metadata.
Credentials
No environment variables, credentials, or config paths beyond a user-provided config file (~/.config/splitsym/symbols.json) are required. The default config location is reasonable for a CLI tool and is not excessive.
Persistence & Privilege
The skill does not request persistent/always-on presence and does not modify other skills or global agent settings. It runs only when invoked.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install splitsym - After installation, invoke the skill by name or use
/splitsym - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
splitsym 1.0.0 – Initial release
- Extracts inline comments, docstrings, and block comments from source code files.
- Supports multiple file types and comment styles (e.g., Python, C, JavaScript, SQL, HTML, OCaml).
- Allows extracting comments from specific line ranges.
- Configurable with custom symbols via a JSON config file.
- Outputs right-aligned line numbers, preserves indentation, and marks block comments with a PAIR: prefix.
Metadata
Frequently Asked Questions
What is splitsym?
Extract comments and split symbols from source files. Use when users want to extract inline comments, docstrings, or block comments from code files to unders... It is an AI Agent Skill for Claude Code / OpenClaw, with 44 downloads so far.
How do I install splitsym?
Run "/install splitsym" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is splitsym free?
Yes, splitsym is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does splitsym support?
splitsym is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created splitsym?
It is built and maintained by TurinFohlen (@turinfohlen); the current version is v1.0.0.
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