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-.json        # one per configured language
├── tag-index-.json      # one per configured language
├── product-groups-.json # one per configured language
└── projects/
    ├── ..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/*..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 ..json for each configured language

1. Always rebuild index files (domains, tag-index, product-groups) from all project files
2. 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

---

JSON Schema

Each language's file set conforms to this schema:

{
  "meta": {
    "generated_at": "2026-06-19T12:00:00+00:00",  // ISO8601, configured timezone
    "source_version": "2.0",
    "total_projects": 95,
    "lang": "en"
  },

  "profile": {
    "name": "",
    "title": "",
    "title_alt": "",
    "email": "",
    "summary": "",                 // 3–5 sentences, synthesized from all Highlights, in this file's language
    "linkedin_about": "",          // LinkedIn-ready About section, in this file's language
    "years_of_experience": 8,                 // from config, or derived from oldest project last_commit
    "total_projects": 95
  },

  "domains": [
    {
      "id": "frontend",                       // snake_case, stable identifier
      "label": "Frontend",                    // translated per this file's language
      "icon": "🖥",
      "project_count": 10,
      "summary": "",               // domain expertise paragraph, in this file's language
      "skills": [
        {
          "tag": "React",
          "level": "expert",                  // "expert" | "proficient" | "familiar" — derived from project count + status
          "project_count": 8,                 // MUST equal projects.length — derived from it, not estimated separately
          "projects": [                       // ALL project ids that have this tag — no sampling, no truncation
            "project-management-system-frontend",
            "lab-space-rack-management-frontend"
            // ... every matching project id
          ]
        }
      ]
    }
  ],

  "projects": [
    {
      "id": "3a7f2c1b09e4",                            // SHA-256[:12] of project folder path relative to scan_root
      "name": "Project Management System — Frontend",  // display_name (privacy-safe), never a brand/client name
      "category": "Enterprise Web Application",   // translated per this file's language
      "status": "Production",                      // translated per this file's language
      "status_badge": "[P]",
      "featured": false,
      "tags": ["React", "NestJS", "Azure AD", "WebSocket", "CASL"],
      "core_tech": "TypeScript / React 18 + Vite 3",
      "database": "PostgreSQL, Redis",
      "deployment": "Docker Swarm / GitLab CI",
      "description": "...",                        // translated per this file's language
      "domain_primary": "frontend",                // primary domain id
      "domains": ["frontend", "backend"]           // all applicable domain ids
    }
  ],

  "tag_index": [
    {
      "tag": "React",
      "domain_id": "frontend",
      "domain_label": "Frontend",             // translated per this file's language
      "project_count": 8,
      "level": "expert"
    }
  ],

  "linkedin": {
    "headline": " | key tech A · key tech B · key tech C",   // translated per this file's language
    "about": "",      // same as profile.linkedin_about, formatted for direct paste
    "skills_list": ["TypeScript", "React", "NestJS", "Next.js", "Docker", "PostgreSQL", "LangGraph", "Azure OpenAI"],
    // top 20 skills sorted by: featured projects first, then project_count desc
    "experience_highlights": [
      // For each domain with ≥3 projects, one bullet point summarizing key projects by display_name (no raw project codes)
      "Led development of an enterprise platform serving multiple business units, built with NestJS + Next.js + Docker Swarm."
    ]
  },

  "product_groups": [
    {
      "product_name": "Lab Space & Rack Management",
      "project_count": 4,
      "roles": ["Frontend", "Backend (Laravel)", "Backend (NestJS)", "Docker / Infra"],
      "status": "Production",
      "projects": [
        { "id": "lab-space-rack-management-frontend", "role": "Frontend", "status": "Production" }
      ]
      // projects[] contains id + role + status only — full data lives in projects[]
    }
  ]
}

---

Execution Steps

Step 1: Get the latest version

Run the /get-latest-version skill.

• If none, abort and tell the user to run /batch-init first.

Step 2: Filter projects

Read projects-list.md, filter rows where:

1. "Active" column is ✅
2. Doc-file column is ✅
3. "Version" column equals the latest version string

Collect each matching project's Path column. Expand ~ via $HOME.

Step 3: Read project doc frontmatter (batch up to 10 in parallel)

For each project, read / and extract:

• project_name, category, status, tags (array), one_line_description
• core_tech, database, deployment, featured
• Also extract the Highlights bullet text from ## 📋 Portfolio Summary section body
• Also record path — the project's absolute path (used for grouping in Step 3b)

After extracting fields, derive a display_name for each project:

Privacy-Safe Display Name Rules

The core rule: display_name must describe only system function — never a client, company, brand, or product name.

Two categories of names require sanitization:

Category 1 — Project code prefixes (structural identifiers in project_name):

• Patterns: any 2–3 letter prefix followed by 2 digits (e.g. pa27, pt07, wp18)
• Action: strip prefix, derive name from one_line_description

Category 2 — Client/brand/product names (appear in project_name directly or as the display name after stripping suffixes):

• Names listed in the config's privacy_blocklist — apply judgment for any unfamiliar name that looks like a client/company/brand token too
• Also applies when the derived display name itself still contains a brand/client name — re-derive from one_line_description
• Action: replace with a purely functional description from one_line_description

Deriving a safe display name:

1. Extract the core system/feature name from one_line_description — translate per each configured language's file
2. Append role suffix: -web → — Frontend, -server → — Backend, -nest-server → — Backend (NestJS), -docker → — Docker / Infra, -ios / -android → — iOS App / — Android App; or derive from core_tech
3. If one_line_description also contains the client/brand name, paraphrase around the function not the brand

Final check before writing output: scan every display_name against privacy_blocklist. If found, re-derive.

• Assign each project a stable id: SHA-256(relPath)[:12] where relPath is the project folder path relative to scan_root (e.g. "clients/acme/acme-lab-space-and-rack-management-web"). This keeps IDs opaque (no brand/client leak) and stable (path doesn't change unless project is moved). In case of collision (extremely rare), append __N suffix before re-hashing.
• Use display_name as the name field in projects[]. Store the original project_name as raw_name for internal reference only (omit from output JSON).
• In domains[].skills[].projects[]: store only the id strings — no other fields. Consumer looks up full data in projects[] by id.
• In product_groups[].projects[]: store { id, role, status } only — no display_name field.

Step 3b: Group projects by product

After deriving display names, group projects belonging to the same product/system:

Grouping logic (apply both; either match triggers grouping):

1. Projects sharing the same parent directory path → one product group
2. Projects whose project_name shares a common prefix after stripping role suffixes (-web, -server, -nest-server, -docker, -backend, -frontend, -api, -cli) → one product group

For each group:

• product_name: the shared functional name without role suffix (anonymized per display name rules)
• projects: array of members, each with display_name, role (Frontend / Backend / Backend NestJS / Docker / Infra / CLI), and status

Add a product_groups top-level array to every language's file set (after tag_index):

"product_groups": [
  {
    "product_name": "Lab Space & Rack Management",
    "project_count": 4,
    "roles": ["Frontend", "Backend (Laravel)", "Backend (NestJS)", "Docker / Infra"],
    "status": "Production",   // highest-priority status among members
    "projects": [
      { "id": "3a7f2c1b09e4", "role": "Frontend", "status": "Production" },
      { "id": "9f1bc234a5e7", "role": "Backend (Laravel)", "status": "Production" },
      { "id": "c82d0f6173ab", "role": "Backend (NestJS)", "status": "Production" },
      { "id": "7e45a912b0cf", "role": "Docker / Infra", "status": "Production" }
    ]
  }
]

Individual projects still appear in the projects[] array — product_groups is additive.

Step 4: Build domain mapping

Use the domain → tags mapping from the generate-tech-profile skill (same built-in table), merged with custom_domains from config.

For each tag in any project, assign it to a domain. A tag may belong to multiple domains.

Step 5: Derive skill levels

For each (domain, tag) pair, collect every project that has this tag — no sampling, no truncation. Then:

• Set projects = the complete array of those project ids (strings only) — ALL of them
• Set project_count = projects.length exactly — this is the source of truth, not a separate estimate
• Derive level from project_count and the statuses of the matched projects:
• expert: project_count ≥ 5, and ≥1 is Production or In Progress
• proficient: project_count 2–4, or project_count = 1 with Production status
• familiar: project_count = 1, status is Archived / Prototype / Completed only

Step 6: Derive yearsofexperience

Use profile.years_of_experience from config if set. Otherwise, not reliably derivable from project docs — output null and note in the summary that the user should set it manually.

Step 7: Build profile.summary and linkedin fields

Synthesize from all Highlights texts, once per configured language:

• profile.summary: 3–5 sentence paragraph covering top domains, key technologies, and scale
• linkedin.about: LinkedIn-optimized About section, 3–5 sentences, first-person, action verbs

Step 8: Build linkedin.skills_list

Take all tags, sort by:

1. Tags appearing in featured: true projects first
2. Then by project_count descending

Take top 20.

Step 9: Build linkedin.experience_highlights

For each domain with ≥3 projects, write one bullet, first-person, action verb, mentioning key projects by display name, including scale/impact if available, in this file's language.

Step 10: Write split files

Write to tech-profile/ under the current working directory:

1. tech-profile/projects/..json for each project and each configured language (only new/changed ones on incremental runs)
2. tech-profile/domains-.json for each configured language
3. tech-profile/tag-index-.json for each configured language
4. tech-profile/product-groups-.json for each configured language
5. tech-profile/meta.json — include id_map: { "": "" } for all projects

meta.json structure:

{
  "meta": { "generated_at", "source_version", "total_projects", "schema_version": "2.0", "lang": "" },
  "profile": { ...same as before... },
  "linkedin": { ...same as before... },
  "id_map": {
    "3a7f2c1b09e4": "Lab Space & Rack Management — Frontend",
    ...
  }
}

Use 2-space indentation. Ensure valid JSON (no trailing commas, no comments).

Step 11: Report results

✅ Tech profile JSON generated (split-file format)
- Projects: N total, M updated (skill_version: X.Y)
- Unique tags: T
- Domains: [frontend, backend, ai_llm, ...]
- Languages: [en, ...]
- tech-profile/ → 
  ├── meta.json (id_map: N entries)
  ├── domains-.json (one per language)
  ├── tag-index-.json (one per language)
  ├── product-groups-.json (one per language)
  └── projects/ (N × languages files)

---

Domain IDs and Labels

| id | Label | |----|----------| | frontend | Frontend | | backend | Backend | | ai_llm | AI / LLM | | database | Database | | devops | DevOps / Infrastructure | | cloud | Cloud Services | | mobile | Mobile | | tools | Tools & Automation | | languages | Languages | | other | Other |

Translate labels per configured language using the same translation approach as generate-tech-profile (Domain name translation table). Use icon: 🖥 frontend, ⚙️ backend, 🤖 ai_llm, 🗄 database, 🚀 devops, ☁️ cloud, 📱 mobile, 🔧 tools, 💻 languages, 📦 other.

Cloud domain tags

Tags that belong to cloud (remove from other/mobile/ai_llm if present): Firebase, Firebase Admin, Firestore, Firebase FCM, Firebase Hosting, Azure AD, Azure OpenAI, Azure Functions, Google Cloud Storage, Google Maps, Google Sign-In, Gmail API, Google Maps Geocoding API, Serverless, Self-Hosted

Status Translation

| English | badge | |----|-------| | Production | [P] | | In Progress | [IP] | | Maintenance | [M] | | Side Project | [S] | | Archived | [A] | | Prototype | [Proto] | | Completed | [C] |

Translate status labels per configured language using the same translation table as generate-tech-profile.

Notes

• Read project docs in parallel batches of up to 10
• Write project files in parallel batches; write index files after all projects are done
• Skip malformed doc files, log warning, continue
• For each non-source language file: translate all description/summary text into natural target-language text
• For the file matching the source data's original language: descriptions may be used as-is; still translate domain/status labels
• featured: true projects are marked with "featured": true in the JSON — do NOT add ⭐ emoji inside JSON strings
• Always overwrite index files (domains, tag-index, product-groups, meta) on each run; project files are only rewritten when changed
• The linkedin_about field must be copy-paste ready — no placeholders, no markdown formatting, plain text only
• tag_index must be sorted by project_count descending
• projects array sorted by: status priority (Production first)

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: jasonChen0604
• Source: jasonChen0604/codebase-to-portfolio
• License: MIT

Install and usage instructions live in the source repository linked above.