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Last active February 19, 2026 03:32
generate-ai-configs.ts — transpiles .claude/ sources into GitHub Copilot, Gemini, and Codex config files

generate-ai-configs

Keeps GitHub Copilot, Google Gemini, and OpenAI Codex config files in sync with a single canonical source: your .claude/ directory.

Instead of maintaining copilot-instructions.md, GEMINI.md, and AGENTS.md by hand, you edit CLAUDE.md and your rule/skill files once — this script generates everything else automatically.

What it generates

Output Target AI
flowchart TD
    A[create-feature-plan] --> B[plan.md + agents/*.agent.md created]
        B --> C[copy agent file to Claude]
            C --> D[agent implements]
                D --> E[PR merged]
                    E --> F["just work  auto-detect plan, assess status, suggest next task"]
                        F --> G{cleanup needed?}
                            G -->|yes| H["just heal (auto-fix completed agents)"]
Command Purpose
work Auto-detect plan from last PR, assess agent status, suggest next task
heal Auto-move completed agents to completed/, detect stuck agents
run-agent --auto Run next agent in auto-detected plan
assess-agents Check completion status across all agents
Phase Verbosity Purpose
Planning (plan.md) Verbose Figure things out, iterate, full specs
Execution (*.agent.md) Minimal Distilled context for agent, ~200-400 lines
Metric Count
Pull Requests Merged 14
Commits 100+
Lines Changed 30,000+
Files Changed 200+
Copilot Review Comments 200+
Gap Postman Finding runi Solution
Contract testing Only 17% do it Continuous drift detection against bound specs
Semantic versioning Only 26% use it Temporal awareness with version history & diffs
AI agent security 51% cite it as #1 concern AI verification validates LLM output before execution
Documentation scatter 55% struggle with inconsistency Single source of truth in Git-friendly YAML
API discovery 34% can't find existing APIs Semantic links map cross-API relationships
Security Concern % of Developers
Unauthorized/excessive API calls from AI agents 51%
AI systems accessing sensitive data 49%
AI systems leaking API credentials 46%
Testing Type Adoption
Functional testing 67%
Integration testing 67%
Performance testing 57%
Contract testing 17%
MCP Status % of Developers
Use MCP regularly 10%
Plan to explore it 24%
Used occasionally for experiments 19%
Evaluated but chose not to implement 7%
Not familiar with MCP 31%