generate-tech-profile-json

Generate Tech Profile JSON Skill

Goal

Read all project docs (doc_filename) with the latest skill_version, extract structured frontmatter data, then generate a tech-profile/ split-file directory (schema version 2.0).

Output layout:

tech-profile/
├── meta.json                  # meta + profile + linkedin + id_map
├── domains-<lang>.json        # one per configured language
├── tag-index-<lang>.json      # one per configured language
├── product-groups-<lang>.json # one per configured language
└── projects/
    ├── <hash>.<lang>.json     # one file per project, per configured language

Why split? Each file is 1–15KB. Incremental runs only rewrite changed project files + rebuild the index files. Token cost scales with changed projects, not total project count.

Incremental update logic:

1. Load existing tech-profile/meta.json → get id_map and source_version
2. Load all tech-profile/projects/*.<primary_lang>.json → build in-memory cache
3. For each project: compute hash, compare cached skill_version
   • Unchanged → skip (reuse cached file, zero token cost)
   • New or changed → read the doc file, write <hash>.<lang>.json for each configured language
4. Always rebuild index files (domains, tag-index, product-groups) from all project files
5. Write meta.json with updated totals

On first run (no existing tech-profile/), process all projects.

Step 0: Read config

Read profile.config.json from the current working directory. If missing, tell the user to copy examples/profile.config.example.json to profile.config.json and fill it in, then stop.

• profile.name, profile.email, profile.title, profile.years_of_experience — used directly in the profile JSON block (if years_of_experience is absent, derive from the oldest project's last commit)
• doc_filename (default CLAUDE.md)
• languages (default ["en"])
• timezone (default +00:00)
• privacy_blocklist (default [])
• custom_domains (default {}) — merged into the built-in domain table in Step 4