fix(providers): remove ~anthropic, skip ~ prefixes in sync script (#69)

* fix(providers): remove ~anthropic, skip ~ prefixes in sync script

OpenRouter uses ~ prefixes for internal auto-routing aliases (e.g. ~anthropic).
These are not real providers — they already route through openrouter.toml.
The generated ~anthropic.toml was confusing (looked like a stale backup)
and redundant with the existing openrouter provider.

- Delete providers/~anthropic.toml
- Skip provider IDs starting with ~ in sync-pricing.py --create-missing

* fix(providers): remove morph, aider, kwaipilot

- morph: specialized code-editing/patching tool, not a general LLM provider
- aider: CLI meta-tool wrapper (base_url empty), redundant with claude-code/codex-cli/gemini-cli/qwen-code
- kwaipilot: Kwai internal coding assistant routed via OpenRouter, niche

* fix(sync): add morph/aider/kwaipilot to SKIP_PROVIDERS to prevent re-creation

* feat(sync): merge OpenRouter-only providers into openrouter.toml

Instead of generating standalone .toml files that just wrap the OpenRouter
endpoint, merge their models directly into openrouter.toml with the
standard 'openrouter/{provider}/{model}' ID convention.

- Add _build_model_fields() and _model_lines() helpers to deduplicate
  model rendering between standalone and merged paths
- Add merge_into_openrouter() that appends new models idempotently
- generate_provider_toml() now only runs for providers in PROVIDER_API
- --create-missing routes OpenRouter-only providers to merge_into_openrouter

* fix(providers): remove 14 OpenRouter-only standalone files

These providers have no direct public API and all route through
openrouter.ai/api/v1. Per the new sync-pricing.py policy, their models
will be merged into openrouter.toml on the next CI run instead of
living in separate files that just wrap the OpenRouter endpoint.

Removed: allenai, deepcogito, essentialai, inclusionai, inflection,
liquid, meituan, nex-agi, nousresearch, prime-intellect, relace,
switchpoint, tngtech, writer

* fix(providers): remove 7 niche providers with no driver support

No dedicated LLM driver code exists for these providers — they rely
purely on OpenAI-compatible passthrough with no special handling.
Removing them reduces registry noise; users can still reach them via
openrouter.toml if needed.

Removed: microsoft, ibm-granite, xiaomi, upstage, inception, aion-labs, arcee-ai

* fix(providers): remove ai21, chutes, venice

All three use ApiFormat::OpenAI with no special handling — pure passthrough.
No registry entry needed; users can reach them via openrouter.toml or by
adding a custom provider.

* docs(providers): rewrite README with full provider catalog and inclusion criteria

- List all 46 providers grouped by category with descriptions
- Document why each provider exists (direct API, unique endpoint, dedicated driver, local, CLI)
- Add inclusion criteria section explaining when to create standalone files vs merging into openrouter.toml
- Document sync script routing logic
- Update model counts: 49→46 providers, 339→232 models

* docs: add comprehensive READMEs for all registry sections + deepinfra provider

- agents/README.md: 32 agents across 7 categories with capability field reference
- channels/README.md: 44 channels across 5 categories with protocol reference table
- hands/README.md: 18 hands across 5 categories with HAND.toml format guide
- mcp/README.md: 33 MCP servers across 5 categories with transport/auth format
- plugins/README.md: 12 plugins with hook protocol documentation
- skills/README.md: 60 skills across 9 categories with SKILL.md format guide
- providers/deepinfra.toml: add DeepInfra serverless inference (5 models)

providers/microsoft.toml

@@ -1,28 +0,0 @@
-# microsoft — auto-generated from OpenRouter API
-
-[provider]
-id = "microsoft"
-display_name = "Microsoft"
-api_key_env = "GITHUB_TOKEN"
-base_url = "https://models.inference.ai.azure.com"
-key_required = true
-
-[[models]]
-id = "phi-4"
-display_name = "Microsoft: Phi 4"
-tier = "fast"
-context_window = 16384
-max_output_tokens = 16384
-input_cost_per_m = 0.065
-output_cost_per_m = 0.14
-supports_streaming = true
-
-[[models]]
-id = "wizardlm-2-8x22b"
-display_name = "WizardLM-2 8x22B"
-tier = "smart"
-context_window = 65535
-max_output_tokens = 8000
-input_cost_per_m = 0.62
-output_cost_per_m = 0.62
-supports_streaming = true