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microsoft-SkillOpt/requirements.txt
Yifan Yang 14c045f04f Windows robustness for claude/codex backends (+ hardened JSON fallback) (#79)
* Robustness for the claude/codex backends on Windows: argv overflow, subprocess encoding, tolerant JSON, test-eval dirs

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>
(cherry picked from commit dca74a683e)

* fix(json_utils): harden tolerant JSON fallback from PR #77

Follow-up fixes on top of the cherry-picked Windows-robustness change:

1. Make _top_level_brace_objects() fully string-aware in its OUTER scan, not
   just inside an object. A '{' inside quoted prose (e.g. '"set it to {x}"')
   no longer starts a candidate object, so extract_json() returns None for
   prose pseudo-JSON instead of repairing it into a bogus dict — which would
   be strictly worse than dropping the edit, since extract_json feeds the
   optimizer's skill edits.

2. Pick the repair candidate BEFORE importing json_repair, so the missing-
   dependency RuntimeWarning only fires when there is genuinely a single
   malformed object that could have been repaired. Ordinary no-JSON / prose
   replies (the common case) now return None silently instead of warning on
   every call.

3. Resolve dependency-metadata inconsistency: json_repair is optional, so add
   it to the `all` extra (it was already in `claude`/`qwen`) and demote it
   from a hard requirement to an optional/commented entry in requirements.txt,
   matching the project's convention for backend-specific deps.

Adds regression tests for prose-with-braces (-> None), no-warning-on-plain-
text, single-object repair, and multi-object ambiguity. Existing 22 json
tests still pass with and without json_repair installed.

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

---------

Co-authored-by: samuelgoofus-boop <260247789+samuelgoofus-boop@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-23 19:00:23 +08:00

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# ── Core ──────────────────────────────────────────
openai>=1.30.0
pyyaml>=6.0
numpy>=1.24.0
openpyxl>=3.1.0
azure-identity>=1.15.0
azure-core>=1.30.0
httpx>=0.27.0
# ── Optional: ALFWorld benchmark ──────────────────
# alfworld>=0.4.0
# gymnasium>=0.29.0
# ── Optional: Claude model backend ────────────────
# claude-agent-sdk>=0.1.0
# ── Optional: Qwen local model (via vLLM) ────────
# vllm>=0.4.0
# ── Optional: tolerant JSON repair for free-form output from non-OpenAI
# backends (Claude/Qwen). Without it extract_json() falls back safely and
# drops a malformed analyst edit instead of repairing it. Installed by the
# `claude`, `qwen`, and `all` extras in pyproject.toml.
# json_repair>=0.61.0
# ── Optional: WebUI dashboard ────────────────────
# gradio>=4.0.0
# ── Optional: Documentation site ─────────────────
# mkdocs-material>=9.5.0
# mkdocstrings[python]>=0.24.0