Claude-skill-registry config-seeder
Seed JSON configuration files into database. Use ONCE at BAZINGA session initialization, BEFORE spawning PM.
git clone https://github.com/majiayu000/claude-skill-registry
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/config-seeder" ~/.claude/skills/majiayu000-claude-skill-registry-config-seeder && rm -rf "$T"
skills/data/config-seeder/SKILL.mdConfig Seeder Skill
You are the config-seeder skill. Your role is to seed JSON configuration files into database tables at session start.
Overview
This skill seeds workflow configuration from JSON files into database tables:
→workflow/transitions.json
tableworkflow_transitions
→workflow/agent-markers.json
tableagent_markers
from transitions.json →_special_rules
tableworkflow_special_rules
Note: In dev mode,
bazinga/config is a symlink to ../workflow for path compatibility.
Prerequisites
- Database must be initialized (
exists)bazinga/bazinga.db - Config files must exist:
(source) orworkflow/transitions.json
(symlink)bazinga/config/transitions.json
(source) orworkflow/agent-markers.json
(symlink)bazinga/config/agent-markers.json
When to Invoke This Skill
- ONCE at session start, BEFORE spawning PM
- Part of session initialization (after create-session, before PM spawn)
- Should NOT be invoked during normal orchestration flow
Your Task
When invoked, you must:
Step 1: Call the Session Init Script (RECOMMENDED)
Use the unified initialization script that handles DB + config in one step:
python3 .claude/skills/bazinga-db/scripts/init_session.py --session-id "{session_id}"
This script:
- Ensures database exists with correct schema
- Seeds workflow configs (transitions, markers, rules)
- Creates artifacts directory
- Verifies everything is ready
Alternative: Call seed script directly (requires DB to exist first):
python3 .claude/skills/config-seeder/scripts/seed_configs.py --all
With auto-init: Initialize DB if missing:
python3 .claude/skills/config-seeder/scripts/seed_configs.py --all --auto-init-db
Step 2: Verify Success
Expected output on success:
Seeded 45 transitions Seeded 7 agent marker sets Seeded 5 special rules ✅ Config seeding complete
Step 3: Report Result
If successful:
- Report: "Configuration seeded successfully"
- Orchestration can proceed
If failed:
- Report the error message
- Orchestration CANNOT proceed without routing config
What Gets Seeded
Transitions (45+ entries)
State machine rules for routing:
+developer
→READY_FOR_QAqa_expert
+developer
→READY_FOR_REVIEWtech_lead
+qa_expert
→PASStech_lead
+tech_lead
→ merge → check phaseAPPROVED- etc.
Agent Markers (7 entries)
Required markers per agent type:
: "NO DELEGATION", "READY_FOR_QA", "BLOCKED", etc.developer
: "PASS", "FAIL", "Challenge Level", etc.qa_expert
: "APPROVED", "CHANGES_REQUESTED", etc.tech_lead- etc.
Special Rules (5 entries)
Workflow modification rules:
: Skip QA entirelytesting_mode_disabled
: Skip QA Experttesting_mode_minimal
: Escalate to SSE after 2 failuresescalation_after_failures
: Force SSE + mandatory TL reviewsecurity_sensitive
: Route to RE with limited parallelismresearch_tasks
Output Format
Return success/failure message. No structured output needed.
Error Handling
| Error | Meaning |
|---|---|
| Config file not found | JSON files missing - check workflow/ (or bazinga/config/ symlink) |
| Database not found | Run init_db.py first |
| Insert failed | Database schema mismatch - run migration |
If seeding fails, orchestration CANNOT proceed. The prompt-builder and workflow-router skills depend on these database tables.