fix(config): rewrite all skill discipline files for simplified model

Remove JSON output contracts from all skill files (debug, review, spec,
tdd, retrospective, trainer-reader, trainer-writer). Local models now
return markdown prose — Claude Code reads and acts on the text.

Keep the substantive discipline (iron laws, approach rules, output
structure) but replace 'return JSON with status/phase/skill/...' with
clear markdown format instructions.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

config/supervisor/trainer-writer.md

@@ -1,35 +1,31 @@
 # Trainer Writer Discipline
 
-You receive candidate learning moments from the reader and write clean SFT/DPO training pairs.
+You receive candidate learning moments from the reader and write knowledge entries for the brain.
 
-## Quality gate (apply before writing)
-- SFT: prompt must be phrased so it could come from any project, not just this one
-- DPO: chosen and rejected must be clearly distinguishable — skip if a reader can't tell which is better
-- Never include project-specific paths, variable names, or identifiers in any pair
+## Quality gate (apply before writing each entry)
 
-## Output contract
-Return JSON result with:
-- `status`: "pass" (pairs written or skipped due to quality) or "error" (candidates JSON was malformed)
-- `phase`: "trainer"
-- `skill`: "trainer"
-- `file_path`: path of the last file written (empty if nothing passed quality gate)
-- `runner_output`: "N SFT pairs written to brain/training-data/sft/, M DPO pairs to brain/training-data/dpo/" or "0 pairs passed quality gate"
-- `verified`: true if files were written; false if nothing passed
-- `message`: "N sft + M dpo pairs for session <id>" or "no pairs passed quality gate"
+- The lesson must be phrased so it could apply to any project, not just this one
+- No project-specific paths, variable names, or identifiers
+- The insight must be stated clearly enough that someone reading it cold would understand it
 
-## File format
-JSONL — one JSON object per line.
+## Output format
 
-SFT: `{"prompt": "...", "completion": "..."}`
-DPO: `{"prompt": "...", "chosen": "...", "rejected": "..."}`
+For each candidate that passes the quality gate, write a knowledge entry in this format:
 
-Write SFT to: `<brain_dir>/training-data/sft/<session_id>.jsonl`
-Write DPO to: `<brain_dir>/training-data/dpo/<session_id>.jsonl`
+```
+# [Topic]
 
-Append to existing files if they exist (don't overwrite).
+## Lesson
+[The key insight in 1-3 sentences]
 
-## Rules
-1. Parse the `reader_candidates` JSON from the task prompt
-2. For each candidate: apply quality gate
-3. Write passing SFT candidates to sft JSONL, DPO candidates to dpo JSONL
-4. If nothing passes, return status "pass" with verified: false and message "no pairs passed quality gate"
+## When it applies
+[Conditions under which this pattern is relevant]
+
+## Example
+[A brief, generic example that illustrates the lesson]
+```
+
+After presenting all entries, end with a summary:
+"N entries ready for brain_write" or "0 entries passed quality gate — [reason]"
+
+The caller will write passing entries to the brain using brain_write.