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>
Splits CodexCliBackend._call into _call_once + a retry wrapper so transient empties/timeouts are retried instead of silently scored 0, and fails fast on fatal auth/model/version errors (401, refresh_token_reused, token_expired, ChatGPT-account-unsupported, newer-Codex-required). On non-zero exit the CLI error text is surfaced via last_call_error instead of being returned as a model response. Adds per-cycle diagnostics.json (observability only; gate and learning algorithm unchanged) so a 0.0 night self-explains.
Adds a reward-hacking stress test ensuring the consolidation gate rejects skill edits that game train/replay tasks while degrading held-out behavior. Also wires the minimax_chat backend into scripts/eval_only.py (coexisting with the qwen wiring from #85). Closes#67.
Add a regression test to ensure the validation gate correctly rejects
reward-hacking skill edits. It has been observed that optimizers
sometimes propose shortcuts that improve train/replay metrics but fail
to improve held-out behavior. This test codifies that the gate blocks
such artifacts.
Add TestVerifierDiscipline to the test_sleep_engine.py suite:
- Create MockRewardHackingBackend that simulates a reward-hacking rule
which passes the train set but degrades the held-out tasks.
- Assert that the proposed edit is rejected by the gate.
Add missing configuration setup in scripts/eval_only.py to properly
support the minimax_chat backend, which was entirely omitted.
Fix the following coverage gaps in eval_only.py:
- Add minimax CLI arguments
- Include the minimax config mappings in _MAP
- Update the backend parsing logic
- Call configure_minimax_chat
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>
Wires skillopt_sleep into Devin via a stdlib-only MCP server and an ATIF-v1.7 harvester, following the plugins/copilot thin-shell pattern. Includes path-expansion fix, tests + ATIF fixture, schema/tool parity with copilot, and a harvest fix so single-turn sessions aren't dropped by the <3s replay filter.
A harvested single-turn Devin session spanned only 1s (reply written 1000ms
after the prompt), which the engine's harvest filter conservatively classifies
as a <3s headless replay (skillopt_sleep Issue #62) and skips — so a real
single-turn session mined 0 tasks. Widen the prompt->reply gap to 5s. With this,
an end-to-end dry-run mines the task: "night 1: 1 sessions -> 1 tasks".
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Mirror the copilot MCP server: same rich _TOOL_SCHEMA (source, model,
tasks_file, target_skill_path, max_sessions, max_tasks, lookback_hours,
auto_adopt, json, edit_budget, hour, minute) and generic flag forwarding, plus
sleep_schedule / sleep_unschedule. Devin specifics retained: the ATIF-v1.7
harvest step (run before data-reading actions, engine pointed at it via
--claude-home, default --source claude) and post-adopt sync into .devin/skills/.
Tests + README + rules snippet updated for the 7-tool interface.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Review fixes:
- Path bug: SKILLOPT_DEVIN_CLAUDE_HOME (and SKILLOPT_SLEEP_REPO) read from the
env are now wrapped in os.path.expanduser, so the documented "~/..." config
no longer passes a literal ~ to --claude-home (which yielded zero mined
sessions). expanduser on an absolute default is a no-op.
- tests/test_devin_plugin.py: tool-schema completeness, action→subcommand map,
backend enum, the CLAUDE_HOME expansion regression, and an ATIF-v1.7 harvest
shape test against a bundled fixture.
- plugins/devin/fixtures/devin_sample.json: sample ATIF-v1.7 transcript.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replicates the trainer's backend setup in scripts/eval_only.py so eval-only no longer silently falls back to an unconfigured local endpoint. Closes#84.
Wires the skillopt_sleep engine into Devin (Cognition) via an MCP server,
following the same thin-shell pattern as plugins/copilot.
- mcp_server.py: stdlib-only stdio MCP server exposing the standard sleep_*
tools (status, dry-run, run, adopt, harvest). REPO_ROOT defaults to ../.. so
it finds skillopt_sleep automatically when run from plugins/devin/.
- harvest_devin.py: converts Devin ATIF-v1.7 transcripts, agentmemory, and
.devin/skills/*/SKILL.md into the Claude Code-compatible JSONL the engine
consumes; enriches with taskKey + outcome envelopes (hard test/build signal
or judge rubric). Workspace auto-detection; cross-platform paths.
- judge.py, mcp-config.example.json, devin-rules.snippet.md, README.md.
- plugins/README.md: add Devin to the platform + install tables.
No changes to skillopt_sleep; shells out to `python -m skillopt_sleep` like the
other plugins. Pure stdlib; default backend mock (no API spend).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The eval-only tool skipped configuring some of the backend types, that
the training did configure. Because of this, the eval is silently
fell back to a local endpoint that wasn't actually configured, and
all evaluations runs failed.
Replicate the backend setup based on the trainer's code, and eval-only
can run with the qwen_chat backends.
Co-authored-by: Qwen-Coder <noreply@qwen.ai>
Follow-up to the string-aware brace scan: that change only skipped
double-quoted prose, so brace-shaped text in single quotes, backticks, or
bare prose (e.g. `{op: delete}`, '{x: 1}') still reached json_repair and was
fabricated into a bogus dict — strictly worse than None, since extract_json
feeds the optimizer's skill edits.
Add a _looks_json_like() guard before repair: a genuine JSON object's first
non-space char after `{` is `"` (a key) or `}` (empty). Prose pseudo-objects
start with a bare word and are rejected, while legitimate repair targets
(trailing commas, unescaped quotes inside string values) all begin with `"`
and pass — including objects whose string VALUES contain single quotes or
backticks, which must not be rejected.
Found by an independent GPT-5.5 re-review of the merged #79 code. Adds
regression tests for single-quoted / backticked / bare prose (-> None) and
for legitimate objects with quote/backtick string values (still repaired).
Tests: 30 pass (+3 skip) without json_repair, 33 pass with it, both clean
under -W error::RuntimeWarning.
Co-authored-by: Claude <noreply@anthropic.com>
The contributor is already credited via the Co-authored-by trailer carried
into main by #79; a dedicated README section is unnecessary.
Co-authored-by: Claude <noreply@anthropic.com>
Add an Acknowledgements section crediting @samuelgoofus-boop for the
Windows-robustness work on the Claude/Codex backends (originally #77,
merged via #79).
Co-authored-by: Claude <noreply@anthropic.com>
* 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>
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>
The advertised backend choices in scripts/train.py use 'azure_openai',
not 'openai'; align the inputSchema description hint accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.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>
Exposes scripts/train.py and scripts/eval_only.py as Copilot MCP tools
(skillopt_list_configs, skillopt_train, skillopt_eval) via a stdlib-only
stdio server, mirroring the existing SkillOpt-Sleep plugin layout.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
