Addresses maintainer review on the OpenAI-compatible endpoints PR:
1. CLI: accept --backend azure_openai in skillopt_sleep/__main__.py (the
documented command was rejected by the argparse choices).
2. Example runner: exit with the child's return code so watchdog/supervisors
see a failed sleep run as a failure.
3. Error state: clear last_call_error when a retry recovers; set an explicit
"empty response on all N attempts" diagnostic when every attempt returns
empty text.
4. Security guard: the managed-identity path now refuses to send an Azure AD
bearer token to any endpoint outside *.openai.azure.com /
*.cognitiveservices.azure.com — a custom endpoint requires explicit
AZURE_OPENAI_AUTH_MODE=openai_compatible + API key.
5. Provider-neutral requests: compat mode sends only the standard contract
(max_tokens, default 8192 via SKILLOPT_SLEEP_COMPAT_MAX_TOKENS);
provider-specific body fields are opt-in via SKILLOPT_SLEEP_CHAT_EXTRA_BODY
(JSON) — the deepseek model-name inference is removed.
6. Docs: removed the unimplemented OPTIMIZER_*/TARGET_* env-var claim; added a
configuration reference matching the implementation exactly.
7. Tests: tests/test_azure_openai_compat.py — 17 deterministic no-network
unittest cases covering CLI acceptance, compat-vs-Azure client selection,
endpoint resolution, the credential guard, request kwargs (opt-in extra
body / token cap), retry-success error clearing, empty-response
diagnostics, and runner exit-code propagation.
Re-verified live against DeepSeek (deepseek-v4-pro, openai_compatible mode)
after the rework: client type OpenAI, completion returned, no error state.
Let AzureOpenAIBackend drive any OpenAI-compatible chat-completions server
(DeepSeek, self-hosted vLLM/Ollama, ...) alongside native Azure deployments.
- __init__ resolves the endpoint as: explicit arg > AZURE_OPENAI_ENDPOINT env
> the built-in _AZURE_ENDPOINTS table (previously a non-Azure endpoint could
not be supplied at all).
- _get_client() builds a plain openai.OpenAI(base_url=...) client when
AZURE_OPENAI_AUTH_MODE=openai_compatible, matching the auth mode already
supported by the sibling skillopt/model/azure_openai.py. This avoids the
AzureOpenAI SDK rewriting request URLs with Azure-only ?api-version= query
params and deployment path segments, which non-Azure servers reject with 404.
- _call() sends max_tokens + extra_body thinking flag for deepseek* models and
records the last exception in self.last_call_error so a failed night is
diagnosable instead of collapsing to a silent empty->0 score.
Adds docs/sleep/openai-compatible-endpoints.md and sanitized example
runner/watchdog scripts documenting an Antigravity + DeepSeek integration.
The default managed-identity Azure path is unchanged.
Three filters to stop machine-generated prompts polluting the mined task
pool (they dominated ~80% of tasks on this machine):
- _is_meta_prompt: drop expanded slash-command bodies (<command-message>
tags or '# /' headers) — plugin self-invocations are not user intents
- _AGENT_SESSION_MARKERS + _is_agent_session: skip sessions whose first
prompt is another tool's agent brief (claude-mem observers, CLAUDE.md
critic sub-agents, SkillOpt-Sleep's own command body)
- load walk: skip <session>/subagents/ dirs and agent-*.jsonl files —
Agent-tool sidechain transcripts are Claude-authored, not user tasks
Verified: harvest went from 120 sessions / mostly-noise tasks to 55
sessions / 38 real user tasks.
Co-authored-by: codeL1985 <l@cypherlab.tech>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Adds --backend handoff: the engine runs all deterministic stages and
outsources attempt/judge/reflect to prompt/answer files an interactive
agent session fills between runs (exit 3 = pending batch, re-run to
resume). Deterministic replay + the prompt-hash answer cache make resume
stateless; sentinel detection aborts any call built from unanswered
output so placeholders never reach scores or staging. Session digests
and mined tasks are pinned per night (secret-redacted) so the sessions
answering prompts cannot shift the task set, and LLM mining is routed
through the same handoff files. Ships a /skillopt-sleep-handoff Claude
Code command that answers each prompt in a fresh-context subagent to
protect the held-out gate.
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* fix(skillopt-sleep): surface Claude CLI spawn failures instead of silent zero scores
In _call and attempt_with_tools, the bare 'except Exception: return ""'
swallows FileNotFoundError (e.g. bare 'claude' on Windows with npm .cmd
shim) and any other spawn failure, returning an empty string that the
trainer treats as a legitimate model response that scores 0.0 everywhere.
Now the exception is caught explicitly: last_call_error is set, a
warning is logged, and the empty-string return is preserved for
backward compatibility of the control flow. This mirrors the pattern
from #92 (codex backend) which fixed the same class of 'dead CLI
masquerades as nothing to learn' bug.
Issue: #121
* test(skillopt-sleep): add tests verifying Claude CLI spawn failures are surfaced
Add two tests to TestClaudeCliBackendBare:
- test_spawn_failure_sets_last_call_error: _call sets last_call_error
and returns '' when subprocess.run raises FileNotFoundError.
- test_attempt_tools_spawn_failure_sets_last_call_error: same for
attempt_with_tools.
These prove the fix from the parent commit (surface spawn failures
instead of silently scoring 0) and guard against regressions.
* fix(backend): make ClaudeCliBackend work on Windows (.cmd shim + argv limit)
Every claude call failed with WinError 2 and was swallowed by the bare
except -> return '', so real-backend cycles silently scored 0.000/0.000
and produced zero edits.
- resolve claude_path via shutil.which: the npm-installed claude is a
.cmd shim that CreateProcess cannot resolve by bare name
- pass the prompt via stdin instead of argv: the .cmd shim routes
through cmd.exe whose command line caps at ~8K chars, which
reflect/judge prompts exceed
Verified: reflect() on a synthetic max_chars failure now returns a
concrete bounded edit via the real CLI (was [] before).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(backend): suppress console window flashes on Windows (CREATE_NO_WINDOW)
Every CLI subprocess (claude/codex/copilot) spawned from a console-less
parent allocates a visible console window on Windows. A cycle making
hundreds of calls strobes cmd windows and steals focus, making the
machine unusable while the engine runs. Pass CREATE_NO_WINDOW on all
CLI call sites; no-op on POSIX.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: codeL1985 <l@cypherlab.tech>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
PR #92 added a per-cycle diagnostics.json that surfaces backend stderr,
optimizer replies, and task responses so a 0.0 night is self-diagnosing.
Those free-text fields can carry credentials (e.g. a codex 401 stderr dump
containing an auth token), so persisting them verbatim was a new on-disk
leak surface.
- Add a shared redact_secrets() in staging.py and route diagnostics.json's
call_error / reflect_raw_head / holdout_detail through it before writing.
- Redact the codex and Claude auth-error log lines too (a secondary sink
when a file log handler is attached); last_call_error stays raw in memory
so _AUTH_MARKERS matching is unaffected.
- Centralize _SECRET_PATTERNS in staging.py (harvest_codex now reuses them)
and extend coverage to AWS / GitHub / Slack / Google / JWT token shapes.
- Tests: secret-shape coverage, private-key blocks, recursive/scalar
passthrough, no over-redaction of plain prose, fail-fast auth-error log
redaction, and an end-to-end check that diagnostics.json has no secret.
Observability-only; the gate and learning algorithm are unchanged.
Co-Authored-By: Claude <noreply@anthropic.com>
Follow-up from a fresh-context review of the prior commit: CodexCliBackend.attempt_with_tools
(the rollout path for tool-requiring tasks) ran codex exec inline, swallowed all exceptions,
and never set last_call_error — so an auth/model/version failure on the tool path still
produced a silent empty->0 with no diagnostic signal, the exact failure class the prior commit
fixed for the _call path. Now it surfaces timeout/exception/non-zero-exit via last_call_error
(response stays empty; never leaks the CLI error text), so a failed tool rollout shows up in
diagnostics.json. Adds a regression test.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A nightly sleep cycle could run for weeks emitting held-out 0.0 -> 0.0 (gate reject, zero
edits), indistinguishable from "nothing to learn", when the real cause was the codex backend
returning an error (expired auth / model unsupported on the account / outdated CLI) that got
scored as a failed rollout.
backend (CodexCliBackend):
- split _call into _call_once + a retry wrapper: transient empties/timeouts are retried
instead of silently returning "" (mirrors AzureOpenAIBackend's guard);
- on a non-zero exit, surface the reason via last_call_error and return "" rather than
leaking the CLI error text as if it were a model response;
- fail fast (no retries) on fatal auth/model/version errors (401, refresh_token_reused,
token_expired, "not supported when using Codex with a ChatGPT account",
"requires a newer version of Codex").
backend (CliBackend.reflect): retain last_reflect_raw so a no-edits night is diagnosable.
consolidate: ConsolidationResult now carries per-task held-out detail (response, hard/soft,
fail_reason) + reflect_raw + call_error.
cycle: write diagnostics.json per cycle so a 0.0 night self-explains instead of being a black box.
tests: 4 new (retry-not-silent-zero, auth-error-surfaced-not-scored, holdout-detail, reflect-raw).
Also gitignore the .skillopt-sleep/ runtime dir.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Codex round 3: argparse default=0 made every CLI invocation without
--lookback-hours clobber the config's 72h default. Now default=None;
only explicit --lookback-hours N (including 0) overrides config.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- harvest.py: revert break to continue — mtime ordering can diverge
from embedded ended_at timestamps (copy/touch), so we must check all
files rather than early-exiting on the first old one
- cycle.py: use `is not None and > 0` so lookback_hours=0 means
"scan full history" (opt-out of the cutoff)
- __main__.py: propagate --lookback-hours 0 to config as explicit 0
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- cycle.py: use supplied `clock` parameter (not wall time) for the
lookback cutoff, so deterministic tests/experiments get reproducible
harvest windows
- harvest.py: break (not continue) when a file is older than since_iso,
since files are sorted newest-first by mtime — avoids scanning the
entire transcript directory for quiet projects with large histories
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Two fixes from issue #57 feedback:
1. run-sleep.sh: support SKILLOPT_SLEEP_PYTHON env var to explicitly set
the Python interpreter. Useful on macOS where system Python is 3.9 but
a newer Python is available elsewhere (e.g. Codex Desktop's bundled
Python 3.12). Applied to both the shared runner and the bundled
Claude Code plugin copy.
2. cycle.py: on first run (no prior harvest recorded), apply the
lookback_hours config (default 72h) as a time cutoff. Previously,
first run scanned the entire transcript history, which could trigger
massive LLM mining on users with months of session data.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Address codex review: "API key" was too generic — a model response
about configuring API keys would trigger a false auth warning. Now:
- Use specific phrases ("Invalid API key", "Unauthorized: invalid x-api-key")
- Only check short stdout (<300 chars) to skip real model responses
- Still check stderr unconditionally
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
ClaudeCliBackend._call() and attempt_with_tools() hardcoded --bare,
which skips Claude CLI's credential resolution. This broke subscription-
token auth: every model call silently returned "Not logged in" and
scored 0 — the user saw "baseline 0.0 → candidate 0.0, gate reject"
with no indication of an auth failure.
Fix: only pass --bare when ANTHROPIC_API_KEY is set. The remaining
isolation flags (--disable-slash-commands, --disallowedTools,
--exclude-dynamic-system-prompt-sections, clean temp cwd) already
provide the needed isolation without --bare.
Also adds _detect_cli_error() to log a warning when CLI output matches
known auth error patterns, so auth failures surface loudly instead of
deflating every score to 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- harvest: tighten sub-3s filter to also require prompt < 200 chars,
avoiding false positives on fast real one-shot questions
- openclaw schedule_cmd: add docstring clarifying it schedules the
shared engine, not the OpenClaw-native runner
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Adds honest tool-call detection for CopilotCliBackend, mirroring the
Claude/Codex backends. Writes per-tool executable shims into the work dir
and detects real invocations from a calllog (not self-reported markers).
The Copilot backend is Windows-validated, so shims are cross-platform:
a .cmd batch shim on Windows and a chmod'd bash shim on POSIX, with an
OS-specific tool hint. Mirrors _call's flags/env (isolated COPILOT_HOME,
--allow-all-tools, MCP/instruction disabling) and the UTF-8 subprocess fix.
Adds test_attempt_with_tools_honest_detection: a CI-friendly, OS-aware
stub stands in for the CLI, runs the shim, and asserts both JSONL parsing
and log-based detection. Validated live on Windows (real Copilot call) and
on Linux/WSL (POSIX path).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Add CopilotCliBackend that drives the GitHub Copilot CLI in
non-interactive mode (copilot -p ... --output-format json) and parses the
JSONL event stream for assistant.message content. Registered as the
'copilot' backend (with aliases) and wired through the CLI, config,
experiment harness, and the Copilot MCP server's backend enum.
- Force UTF-8 decoding of CLI output (fixes cp1252 UnicodeDecodeError on
Windows when responses contain non-cp1252 bytes).
- Minimise per-call startup: isolated COPILOT_HOME with built-in MCPs and
custom instructions disabled, so user MCP servers are not spawned per
call (~5x faster: 36s -> 7.4s). Override via SKILLOPT_SLEEP_COPILOT_HOME
/ SKILLOPT_SLEEP_COPILOT_MODEL / SKILLOPT_SLEEP_COPILOT_FULL_ENV.
Validated end-to-end on real held-out tasks (researcher persona:
0.42 -> 1.00 lift; gate correctly rejects non-improving edits).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Wires two consolidation mechanisms into the shipped nightly cycle, both default
OFF so existing behavior is unchanged:
- dream_rollouts (>1): multi-rollout contrastive reflection per task
- recall_k (>0): associative recall of the K most-similar past tasks (from a
capped task_archive persisted in state.json) into tonight's dream
- dream_factor (>0): synthetic task variants
New shared engine module skillopt_sleep/dream.py (recall_similar, dream_augment,
dream_consolidate) is called by both the plugin cycle and the experiment harness,
so reported numbers exercise the exact shipped code. Built on the existing
rollouts_k/sample_id support already in consolidate.py/rollout.py.
Validated (5 nights x 10 real tasks/night, full held-out test, GPT-5.5, gated):
the gain scales with recall depth on a clean signal —
SearchQA recall_k=10 +3.1, recall_k=20 +4.5, full-history reference +5.6;
SpreadsheetBench (nano, gate-free) +3.6. Flat within noise on saturated/noisy
cells. See docs/sleep/EXPERIENCE_REPLAY.md (+ raw runs under blog_runs/v2_port/).
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Updates the SkillOpt-Sleep plugin on top of the current main. User-facing and
engine improvements since the initial drop:
* Command renamed /sleep -> /skillopt-sleep across Claude Code + Codex shells;
refreshed plugin READMEs and install scripts.
* Built-in scheduling (skillopt_sleep/scheduler.py + __main__): schedule /
unschedule the nightly cycle without external cron wiring.
* Backend robustness: bounded retry with backoff (no more silent empty-string
on transient 429/timeout), content-filter-safe rollout prompt, an
output-contract guardrail that rejects edits violating the task's required
format, and a per-sample cache key so repeated dream rollouts are independent
samples (fixes degenerate single-sample reflection).
* consolidate / rollout / replay: parallel multi-rollout dreaming, gate-mode
controls, TaskRecord.system framing field.
Scope: this commit ships only the plugin engine + shells. Research/benchmark
harnesses and their data are intentionally not included; the public package
has no dependency on them (the one research-evaluator import is now guarded).
Marked as an early preview in the README; we'll keep iterating.
99/99 unit tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Remove every non-ASCII/CJK character for a professional open-source repo:
- harvest.py: drop hardcoded Chinese feedback phrases; add an env-based
extensibility hook (SKILLOPT_SLEEP_NEG_FEEDBACK / _POS_FEEDBACK) so any
locale can be added without baking one in. Verified with a German example.
- rollout.py / consolidate.py: English comments.
- README.md section heading + anchor, CONTROLLABLE_DREAMING.md, plugin.json,
marketplace.json (also fixed stale path skillopt-sleep-plugin ->
plugins/claude-code), SKILL.md: English only.
- Remove the internal WAKE_UP_SUMMARY.md note (not user-facing, not referenced).
Verified: zero CJK chars remain anywhere; 29 tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Open-source-tool / research-code separation:
- git mv skillopt/sleep/ -> skillopt_sleep/ (top-level, sibling to the research
skillopt/ package). History preserved as renames.
- All imports skillopt.sleep.* -> skillopt_sleep.*.
- Vendor the validation gate into skillopt_sleep/gate.py (a self-contained copy
of skillopt.evaluation.gate). The engine now has ZERO dependency on the
research package — verified: grep finds no `from skillopt.` in skillopt_sleep/,
and consolidate's gate resolves to skillopt_sleep.gate.
- Plugin scripts/commands/skill call `-m skillopt_sleep`.
29 tests pass; `python -m skillopt_sleep` runs standalone.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Yifan Yang