`parseMarkdown` previously walked the lines after the opening `---` and
recorded the first `^#{1,6}\s`-shaped line as a `headingBeforeClose`,
then flagged MISSING_CLOSE when that index came before the actual closing
fence. YAML allows `#` comment lines anywhere inside the document, so a
template that leads with annotation comments inside the fence (e.g. a
`# Research Template` header before the keys) hit a false-positive
MISSING_CLOSE even though the closing `---` was present.
Fix: only walk for the closing `---`. When it is found, content between
the fences is YAML; `#` lines are comments, not headings. When the close
is genuinely missing, surface the first heading-shaped line as a
where-it-went-off-the-rails hint (this path was already correct; we keep
it for the genuine missing-close case).
Two regression tests added to `test/markdown-validation.test.ts`:
- `#` comment lines at the top of a closed frontmatter
- `#` comment lines interleaved with keys
All 68 tests across the four markdown/frontmatter test files stay green.
The litellm recipe shipped only an `embedding` touchpoint. `getProviderCapabilities()` in `src/core/ai/capabilities.ts` throws when `recipe.touchpoints.chat` is missing, `classifyCapabilities()` returns `'unknown'`, and `enforceSubagentCapable()` in `src/core/model-config.ts` silently falls back to `TIER_DEFAULTS.subagent` (anthropic). Any brain that routes paid traffic through a litellm-style proxy AND has no `ANTHROPIC_API_KEY` then sees every subagent loop dispatch throw `AIConfigError: Anthropic ... requires ANTHROPIC_API_KEY`. The user's explicit `models.tier.subagent = litellm:*` choice is overridden without their knowledge.
Declare `chat` and `expansion` touchpoints mirroring the openai recipe's shape: `models: []` (litellm proxies arbitrary backends; allowlist is intentionally empty, since `assertTouchpoint` already skips allowlist checks for `tier: 'openai-compat'`), `supports_tools: true`, `supports_subagent_loop: true`, `supports_prompt_cache: false` (OpenAI-compat backends don't honor Anthropic-style `cache_control`), `max_context_tokens: 200_000` (conservative GPT-5-family default; per-deployment override needed for smaller-context backends), costs `undefined` (varies by proxied provider).
Reproduction (deterministic):
1. Fresh brain with no `ANTHROPIC_API_KEY` in env.
2. `gbrain config set models.tier.subagent litellm:gpt-5.4` (or any `litellm:*` string).
3. `gbrain models` warns and falls back to `anthropic:claude-sonnet-4-6`.
4. Submit any subagent job; throws `AIConfigError: Anthropic ... requires ANTHROPIC_API_KEY`.
After the patch, `classifyCapabilities('litellm:gpt-5.4', recipe)` returns `degraded:no_caching` (chat-capable, no Anthropic-style prompt cache). `enforceSubagentCapable` no longer steals the model choice. The subagent loop emits a one-time `degraded:no_caching` warn about prompt-cache absence; cost scales linearly with conversation length, accepted trade for the proxy path.
