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scan-projects

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Actualizado3 de abril de 2026, 03:13

Scan subdirectories and files to create/update projects.tsv with metadata and LLM-generated summaries

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Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

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SKILL.md

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name	scan-projects
description	Scan subdirectories and files to create/update projects.tsv with metadata and LLM-generated summaries

Scan Projects Skill

Scans all entries (git repos, plain directories, and standalone files) in the current folder and creates/updates a projects.tsv file with metadata and summaries.

Output Format

The projects.tsv file contains tab-separated columns:

folder: Entry name (directory or filename)
type: Entry type — git, dir, or file
summary: High-level description
language: Primary programming language (or file type for standalone files)
license: License type (git repos and directories only)
earliest_commit: Date of first commit (git repos only, ISO format)
latest_commit: Date of most recent commit on main/master (git repos only, ISO format)
url: Remote git URL (git repos only)

Execution Instructions

Phase 1: Scan Metadata

python3 ~/.claude/skills/scan-projects/scan.py

Collects metadata for all entries. Git repos get full metadata (language, license, commits, URL). Plain directories get language detection and license scanning. Files get type classification. All summaries start as "NEEDS_ANALYSIS".

Phase 2: Generate Summaries with Claude Analysis

Read projects.tsv to find entries with "NEEDS_ANALYSIS"
Batch process entries (20+ at a time using parallel Explore agents)
For each entry, analyze to determine purpose/goal:
- Git repos/dirs: Read README, main code, package metadata, directory structure
- Files: Read content (if text), infer purpose from name and context
Generate a concise summary (1-2 sentences, max 150 chars):
- Focus on purpose and goal, not implementation
- Be specific about the domain
- Avoid generic phrases — just state what it does
Batch update the TSV via inline Python or batch_update.py

Summary Guidelines

Good: "BIDS validator for neuroimaging datasets"
Good: "CLI tool for downloading CI logs from GitHub Actions, Travis, Appveyor"
Good: "GPG-encrypted passwords/credentials file"
Bad: "This is a Python library"
Bad: "A directory with some files"

Helper Scripts

update_summary.py --list [N]: List N entries needing analysis
update_summary.py <folder> <summary>: Update single entry
batch_update.py -: Batch update from JSON (stdin)
batch_update.py <file.json>: Batch update from JSON file

Entry Type Handling

Type	Language detection	License	Commits	URL
`git`	File extension counting	LICENSE/COPYING parsing	Earliest + latest	`git remote get-url origin`
`dir`	File extension counting	LICENSE/COPYING parsing	N/A	N/A
`file`	Extension mapping	N/A	N/A	N/A

Error Handling

Report entries where scanning fails
Handle missing LICENSE/README gracefully
Handle repositories with no commits
Skip hidden directories and .claude

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Scan Projects Skill

Scans all entries (git repos, plain directories, and standalone files) in the current folder and creates/updates a projects.tsv file with metadata and summaries.

Output Format

The projects.tsv file contains tab-separated columns:

folder: Entry name (directory or filename)

type: Entry type — git, dir, or file

summary: High-level description

language: Primary programming language (or file type for standalone files)

license: License type (git repos and directories only)

earliest_commit: Date of first commit (git repos only, ISO format)

latest_commit: Date of most recent commit on main/master (git repos only, ISO format)

url: Remote git URL (git repos only)

Execution Instructions

Phase 1: Scan Metadata

python3 ~/.claude/skills/scan-projects/scan.py

Phase 2: Generate Summaries with Claude Analysis

Read projects.tsv to find entries with "NEEDS_ANALYSIS"

Batch process entries (20+ at a time using parallel Explore agents)

For each entry, analyze to determine purpose/goal:

Git repos/dirs: Read README, main code, package metadata, directory structure
Files: Read content (if text), infer purpose from name and context

Generate a concise summary (1-2 sentences, max 150 chars):

Focus on purpose and goal, not implementation
Be specific about the domain
Avoid generic phrases — just state what it does

Batch update the TSV via inline Python or batch_update.py

Summary Guidelines

Good: "BIDS validator for neuroimaging datasets"

Good: "CLI tool for downloading CI logs from GitHub Actions, Travis, Appveyor"

Good: "GPG-encrypted passwords/credentials file"

Bad: "This is a Python library"

Bad: "A directory with some files"

Helper Scripts

update_summary.py --list [N]: List N entries needing analysis

update_summary.py <folder> <summary>: Update single entry

batch_update.py -: Batch update from JSON (stdin)

batch_update.py <file.json>: Batch update from JSON file

Entry Type Handling

Type

Language detection

License

Commits

URL

git

File extension counting

LICENSE/COPYING parsing

Earliest + latest

git remote get-url origin

dir

File extension counting

LICENSE/COPYING parsing

N/A

file

Extension mapping

N/A

Error Handling

Report entries where scanning fails

Handle missing LICENSE/README gracefully

Handle repositories with no commits

Skip hidden directories and .claude