Fixes surfaced running SkillOpt end-to-end on the bundled `claude` backend
(local Claude CLI) on Windows. None changes the OpenAI/GPT happy path.
1. skillopt/engine/trainer.py — the final test-eval directory
(test_eval_final/) is written to before being created; add
os.makedirs(..., exist_ok=True), matching the two sibling test-eval dirs.
Without it, summary.json raises FileNotFoundError when a rollout yields
zero predictions.
2. skillopt/model/claude_backend.py
a. Pass the prompt via stdin (not argv): on Windows the whole command line
is capped at ~32 KB and a large optimizer prompt (the success-analyst
minibatch carrying several report trajectories) overflows it with
[WinError 206], killing the run after retries.
b. Pass the system prompt via --append-system-prompt-file (a temp file),
not argv. The system prompt here is the skill being optimized, which
SkillOpt grows over training; since the ~32 KB cap applies to the SUM of
all argv, a grown skill would re-hit [WinError 206] even with the prompt
on stdin.
c. Pin the subprocess encoding to utf-8 (errors="replace"). With text=True
and no encoding=, stdin is encoded with the system codepage; on a zh-CN
box (cp936/GBK) a prompt containing an emoji or some Latin-1 characters
raises UnicodeEncodeError before the CLI even starts, failing every retry.
3. skillopt/model/codex_backend.py — the same utf-8 encoding pin on its
subprocess.run(input=...) call (identical unpinned-encoding pattern).
4. skillopt/utils/json_utils.py — extract_json() returned None for valid-
looking JSON that strict json.loads rejects (unescaped ASCII quotes inside
CJK string values, trailing commas), silently dropping the analyst's edits
on non-schema backends (Claude/Qwen): reflect produces N edits, 0 applied.
Add a json_repair fallback, but only on a single unambiguous object — a
balanced-brace extractor plus a refuse-on-multiple-objects guard — so a
chain-of-thought "scratch + final" response can't make repair silently
return the wrong (discarded) object, which would be worse than None (None is
detectable and retryable; a wrong-but-valid edit is applied blind). Declare
json_repair in requirements.txt and the claude/qwen optional extras so the
fallback is actually present (it otherwise no-ops, dropping edits silently).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
samuelgoofus-boop