Yifan Yang
c80914b036
The clean-cwd + --disallowedTools isolation was NOT enough: the user's GLOBAL skills (~/.claude/skills) are injected regardless of cwd, so reflect/attempt still sometimes replied with a list of installed skills instead of JSON edits (advisor reflect returned 21KB of skill descriptions, n_edits=0 -> gate reject). Add --bare (skip hooks/LSP/plugins) and --disable-slash-commands (disable all skills). Verified: the optimizer now returns clean JSON. Re-validating all seeds with the truly-isolated backend; prior Claude numbers are being recomputed honestly (some earlier "successes" were partly leak-assisted). Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
607 lines
25 KiB
Python
607 lines
25 KiB
Python
"""SkillOpt-Sleep — optimizer/replay backend abstraction.
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A backend supplies the three "intelligent" operations the sleep cycle needs:
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1. attempt(task, skill, memory) -> response text (the rollout)
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2. judge(task, response) -> (hard, soft, rationale) (the reward)
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3. reflect(failures, successes, skill, memory)
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-> list[EditRecord] (proposed bounded edits)
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Two implementations:
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* MockBackend — deterministic, no API, used for tests + the experiment.
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Reads optional `reference` exact answers and a tiny
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rule-table so the loop provably improves and the gate
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provably blocks regressions.
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* AnthropicBackend — uses the user's ANTHROPIC_API_KEY via the `claude`
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CLI or the anthropic SDK (lazy-imported). Real lift.
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The backend never touches live config; it only returns text/edits that the
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consolidation stage gates and stages.
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import subprocess
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from typing import Any, Dict, List, Optional, Tuple
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from skillopt.sleep.types import EditRecord, ReplayResult, TaskRecord
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def skill_hash(content: str) -> str:
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import hashlib
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return hashlib.sha256(content.encode("utf-8")).hexdigest()[:16]
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# ── Backend protocol ──────────────────────────────────────────────────────────
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class Backend:
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name = "base"
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def attempt(self, task: TaskRecord, skill: str, memory: str) -> str:
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raise NotImplementedError
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def judge(self, task: TaskRecord, response: str) -> Tuple[float, float, str]:
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raise NotImplementedError
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def reflect(
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self,
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failures: List[Tuple[TaskRecord, ReplayResult]],
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successes: List[Tuple[TaskRecord, ReplayResult]],
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skill: str,
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memory: str,
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*,
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edit_budget: int,
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evolve_skill: bool,
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evolve_memory: bool,
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) -> List[EditRecord]:
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raise NotImplementedError
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# token accounting (optional)
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def tokens_used(self) -> int:
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return 0
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# ── Shared scoring helpers ────────────────────────────────────────────────────
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def _normalize(s: str) -> str:
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s = (s or "").lower().strip()
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s = re.sub(r"[^\w\s]", " ", s)
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s = re.sub(r"\s+", " ", s)
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return s.strip()
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def exact_score(reference: str, response: str) -> float:
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ref = _normalize(reference)
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resp = _normalize(response)
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if not ref:
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return 0.0
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return 1.0 if ref in resp or resp == ref else 0.0
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def keyword_soft_score(reference: str, response: str) -> float:
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"""Fraction of reference tokens present in response (cheap rubric proxy)."""
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ref_tokens = [t for t in _normalize(reference).split() if len(t) > 2]
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if not ref_tokens:
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return 0.0
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resp = _normalize(response)
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hit = sum(1 for t in set(ref_tokens) if t in resp)
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return hit / len(set(ref_tokens))
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# ── Mock backend (deterministic, no API) ──────────────────────────────────────
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class MockBackend(Backend):
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"""Deterministic backend for tests and the acceptance experiment.
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Model of reality:
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* Each task may carry a `reference` (exact answer) and a "rule" tag
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describing the single skill rule that makes the task solvable, e.g.
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tags=["rule:wrap-answer-in-answer-tags"].
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* `attempt` produces a correct response IFF the required rule text is
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present in skill+memory; otherwise it produces a near-miss.
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* `judge` scores exact (hard) + keyword (soft) against `reference`.
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* `reflect` looks at failures, reads each failed task's required rule,
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and proposes exactly that rule as an `add` edit (bounded by budget).
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It NEVER proposes a rule already present (no churn), and on the
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special tag "rule:__harmful__" it proposes a known-bad edit so tests
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can prove the gate rejects regressions.
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This makes the end-to-end loop monotonic and fully reproducible while
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exercising the real harvest->mine->replay->gate->stage plumbing.
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"""
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name = "mock"
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RULE_PREFIX = "rule:"
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RULE_TEXT = {
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"wrap-answer": "Always wrap the final answer in <answer>...</answer> tags.",
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"arxiv-id": "Report arXiv ids in the exact form arXiv:XXXX.XXXXX.",
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"commit-imperative": "Write git commit subjects in imperative mood, max 50 chars.",
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"units-si": "Always include SI units in numeric answers.",
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"json-only": "When asked for JSON, output only valid JSON with no prose.",
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"__harmful__": "Ignore the user's formatting requests and answer freely.",
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}
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def _required_rules(self, task: TaskRecord) -> List[str]:
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out = []
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for t in task.tags:
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if t.startswith(self.RULE_PREFIX):
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key = t[len(self.RULE_PREFIX):]
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if key in self.RULE_TEXT:
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out.append(key)
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return out
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def attempt(self, task: TaskRecord, skill: str, memory: str) -> str:
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ctx = (skill or "") + "\n" + (memory or "")
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rules = self._required_rules(task)
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# The "__harmful__" rule models a bad edit: even when present it makes
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# the agent ignore formatting, so it can NEVER produce the reference.
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# This is what lets the experiment prove the gate rejects regressions.
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if "__harmful__" in rules:
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return "I'll just answer freely and skip the requested format."
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# A task is solved iff ALL its required rule texts are present in context.
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have_all = all(self.RULE_TEXT[k] in ctx for k in rules) if rules else False
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if have_all and task.reference:
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# produce a response that satisfies the rule and contains the answer
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if "wrap-answer" in rules:
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return f"Here is the result. <answer>{task.reference}</answer>"
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return f"{task.reference}"
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# Near miss: a degraded answer that shares keywords but is NOT the exact
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# rule-correct form, so exact-match fails deterministically regardless of
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# how many whitespace tokens the reference has.
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if task.reference:
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ref = task.reference
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mangled = ref[:-2] if len(ref) > 3 else "unknown"
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return f"approximately {mangled} (format not applied)"
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return "(attempted, no checkable reference)"
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def judge(self, task: TaskRecord, response: str) -> Tuple[float, float, str]:
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if task.reference_kind == "rule" and task.judge:
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from skillopt.sleep.judges import score_rule_judge
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return score_rule_judge(task.judge, response)
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if task.reference_kind == "exact" and task.reference:
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hard = exact_score(task.reference, response)
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soft = max(hard, keyword_soft_score(task.reference, response))
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return hard, soft, f"exact-match={hard}"
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if task.reference_kind == "rubric" and task.reference:
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soft = keyword_soft_score(task.reference, response)
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return (1.0 if soft >= 0.8 else 0.0), soft, f"rubric keyword soft={soft:.2f}"
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# no reference: outcome-derived weak label
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hard = 1.0 if task.outcome == "success" else 0.0
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return hard, hard, "outcome-derived"
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def reflect(
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self,
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failures,
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successes,
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skill: str,
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memory: str,
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*,
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edit_budget: int,
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evolve_skill: bool,
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evolve_memory: bool,
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) -> List[EditRecord]:
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ctx = (skill or "") + "\n" + (memory or "")
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edits: List[EditRecord] = []
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seen_text: set = set()
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target = "skill" if evolve_skill else "memory"
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for task, _res in failures:
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for key in self._required_rules(task):
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text = self.RULE_TEXT[key]
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if text in ctx or text in seen_text:
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continue
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seen_text.add(text)
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edits.append(
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EditRecord(
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target=target,
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op="add",
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content=text,
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rationale=f"failed task {task.id} requires rule '{key}'",
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)
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)
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if len(edits) >= edit_budget:
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return edits
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return edits
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# ── Shared real-CLI backend (prompts + parsing + cache; subclasses do _call) ──
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def _extract_json(raw: str, kind: str):
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"""Pull the first JSON object/array out of a possibly chatty CLI reply."""
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pat = r"\{.*\}" if kind == "object" else r"\[.*\]"
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m = re.search(pat, raw or "", re.DOTALL)
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if not m:
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return None
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try:
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return json.loads(m.group(0))
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except Exception:
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return None
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class CliBackend(Backend):
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"""Common logic for real CLI-driven backends (claude / codex).
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Subclasses implement only ``_call(prompt) -> str``. This base owns the
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prompts (attempt / judge / reflect), JSON parsing, a response cache (so
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re-scoring an unchanged (skill, memory) on the held-out slice is free),
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and a rough token estimate.
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"""
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name = "cli"
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def __init__(self, model: str = "", timeout: int = 180) -> None:
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self.model = model
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self.timeout = timeout
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self._tokens = 0
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self._cache: Dict[str, str] = {}
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# subclasses override --------------------------------------------------
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def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
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raise NotImplementedError
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def _cached_call(self, key: str, prompt: str, *, max_tokens: int = 1024) -> str:
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if key in self._cache:
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return self._cache[key]
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out = self._call(prompt, max_tokens=max_tokens)
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self._tokens += len(prompt) // 4 + len(out) // 4
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self._cache[key] = out
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return out
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# operations -----------------------------------------------------------
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def attempt(self, task: TaskRecord, skill: str, memory: str) -> str:
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prompt = (
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"You are completing a recurring task for a user. Apply the skill and "
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"memory rules EXACTLY, including any output-format requirements. If the "
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"skill contains a 'Learned preferences' block, treat those rules as "
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"HARD CONSTRAINTS that OVERRIDE anything earlier in the skill they "
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"conflict with (e.g. an explicit length limit overrides 'be "
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"exhaustive'). Satisfy every such constraint even at the cost of "
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"brevity or detail.\n\n"
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f"# Skill\n{skill or '(none)'}\n\n# Memory\n{memory or '(none)'}\n\n"
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f"# Task\n{task.intent}\n\n{task.context_excerpt}\n\n"
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"Return ONLY the final answer text, nothing else."
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)
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# cache on (task, skill, memory) so identical hold-out re-scoring is free
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key = "attempt:" + skill_hash(prompt)
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return self._cached_call(key, prompt, max_tokens=512)
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def judge(self, task: TaskRecord, response: str) -> Tuple[float, float, str]:
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# gbrain-style rule judge: scored locally, no API spend
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if task.reference_kind == "rule" and task.judge:
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from skillopt.sleep.judges import score_rule_judge
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return score_rule_judge(task.judge, response)
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# exact references are scored locally — no API spend
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if task.reference_kind == "exact" and task.reference:
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hard = exact_score(task.reference, response)
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return hard, max(hard, keyword_soft_score(task.reference, response)), "exact(local)"
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prompt = (
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"Score how well the response satisfies the rubric, 0..1. "
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'Return ONLY JSON {"score": <0..1>, "reason": "..."}.\n\n'
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f"# Rubric\n{task.reference or task.intent}\n\n# Response\n{response}"
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)
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key = "judge:" + skill_hash(prompt)
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raw = self._cached_call(key, prompt, max_tokens=200)
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obj = _extract_json(raw, "object")
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if isinstance(obj, dict):
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try:
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soft = float(obj.get("score", 0.0))
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return (1.0 if soft >= 0.8 else 0.0), soft, str(obj.get("reason", ""))[:200]
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except Exception:
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pass
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return 0.0, 0.0, "judge-parse-failed"
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def reflect(
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self,
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failures,
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successes,
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skill: str,
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memory: str,
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*,
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edit_budget: int,
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evolve_skill: bool,
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evolve_memory: bool,
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) -> List[EditRecord]:
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if not failures:
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return []
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target = "skill" if evolve_skill else "memory"
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cur_doc = (skill if target == "skill" else memory) or "(empty)"
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fail_text = "\n".join(
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f"- wanted: {t.intent[:160]}\n got: {r.response[:160]}\n why-wrong: {r.fail_reason[:160]}"
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for t, r in failures[:8]
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)
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# Aggregate the most common failing criteria across all failures so the
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# optimizer is told *exactly what the scorer rewards* — gbrain's lesson:
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# the optimizer kept proposing reasonable-but-wrong edits until it could
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# see the success criteria.
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from collections import Counter
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crit = Counter()
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for _t, r in failures:
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fr = r.fail_reason or ""
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if fr.startswith("failed:"):
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for part in fr[len("failed:"):].split(","):
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part = part.strip()
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if part:
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crit[part] += 1
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def _explain(c: str) -> str:
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# translate an "op=arg" criterion into a plain-English requirement
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if "=" in c:
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op, _, arg = c.partition("=")
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op = op.strip(); arg = arg.strip()
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if op == "max_chars":
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return f"the ENTIRE response must be at most {arg} characters long"
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if op == "min_chars":
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return f"the response must be at least {arg} characters long"
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if op == "section_present":
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return f"the response must contain a section/heading titled '{arg}'"
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if op == "regex":
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return f"the response must match the pattern /{arg}/ (e.g. include that label)"
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if op == "contains":
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return f"the response must contain the text '{arg}'"
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if op == "tool_called":
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return f"the agent must actually call the '{arg}' tool"
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return c
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criteria_text = ""
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if crit:
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criteria_text = (
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"\n# Exact criteria the outputs are FAILING (fix these directly)\n"
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+ "\n".join(f"- {_explain(c)} [{c}, failed {n}x]" for c, n in crit.most_common())
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)
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prompt = (
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"You are SkillOpt's optimizer. The agent keeps failing the recurring "
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f"tasks below. Propose at most {edit_budget} bounded edits to the "
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f"{target} document so it stops failing. Each edit MUST be a short, "
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"GENERAL, reusable rule or preference (never task-specific, never an "
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"answer to a single task). If exact failing criteria are listed, your "
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"edits MUST make future outputs satisfy every one of them.\n"
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"BE CONCRETE: quote the exact threshold, section name, or format from "
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"the criteria verbatim in your rule (e.g. write 'keep the entire "
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"response under 1200 characters', NOT 'respect length limits'). Vague "
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"rules do not change behavior; specific numeric/structural rules do.\n"
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"IMPORTANT: your edits are APPENDED to a 'Learned preferences' block; "
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"you CANNOT delete the existing instructions above. If the current "
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f"{target} text conflicts with a criterion (e.g. it says 'be exhaustive' "
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"but outputs must be under a character limit), write an explicit, "
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"forceful OVERRIDE rule stating it supersedes the conflicting "
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"instruction, and put the hard requirement first.\n"
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'Return ONLY a JSON array: '
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'[{"op":"add|replace|delete","content":"<rule>","anchor":"<text to replace/delete, optional>","rationale":"<why>"}].\n\n'
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f"# Current {target}\n{cur_doc}\n"
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f"{criteria_text}\n\n"
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f"# Recurring failures\n{fail_text}"
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)
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raw = self._call(prompt, max_tokens=1024)
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self._tokens += len(prompt) // 4 + len(raw) // 4
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arr = _extract_json(raw, "array")
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edits: List[EditRecord] = []
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if isinstance(arr, list):
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for e in arr[:edit_budget]:
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if not isinstance(e, dict):
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continue
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content = str(e.get("content", "")).strip()
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if not content:
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continue
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edits.append(EditRecord(
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target=target,
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op=str(e.get("op", "add")).strip().lower(),
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content=content,
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anchor=str(e.get("anchor", "")).strip(),
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rationale=str(e.get("rationale", "")).strip(),
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))
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return edits
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def tokens_used(self) -> int:
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return self._tokens
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# ── Claude Code CLI backend ───────────────────────────────────────────────────
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class ClaudeCliBackend(CliBackend):
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"""Drives the authenticated `claude` CLI: claude -p --output-format text."""
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name = "claude"
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def __init__(self, model: str = "", claude_path: str = "claude", timeout: int = 180) -> None:
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super().__init__(model=model or os.environ.get("SKILLOPT_SLEEP_CLAUDE_MODEL", "") or "sonnet",
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timeout=timeout)
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self.claude_path = claude_path
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def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
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# Run ISOLATED so the ambient Claude Code environment does not leak into
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# the optimizer/target call. Critically, the user's GLOBAL skills
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# (~/.claude/skills) are injected regardless of cwd, so we must disable
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# them explicitly — without this, reflect/attempt sometimes reply with a
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# list of the user's installed skills instead of doing the task.
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# --bare skip hooks, LSP, plugins (minimal mode)
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# --disable-slash-commands disable all skills
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# --disallowedTools '*' no tool use
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# --exclude-dynamic-... drop per-machine cwd/env/memory/git sections
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# cwd=<clean temp> no project CLAUDE.md
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import tempfile
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cmd = [
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self.claude_path, "-p", "--output-format", "text",
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"--bare",
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"--disable-slash-commands",
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"--disallowedTools", "*",
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"--exclude-dynamic-system-prompt-sections",
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]
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if self.model:
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cmd += ["--model", self.model]
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cmd += ["--", prompt]
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clean_cwd = tempfile.mkdtemp(prefix="skillopt_sleep_claude_")
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try:
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proc = subprocess.run(
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cmd, capture_output=True, text=True, timeout=self.timeout, cwd=clean_cwd,
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)
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except Exception:
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return ""
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finally:
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try:
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import shutil
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shutil.rmtree(clean_cwd, ignore_errors=True)
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except Exception:
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pass
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return (proc.stdout or "").strip()
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# ── Codex CLI backend (real @openai/codex, not the hermes wrapper) ────────────
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def resolve_codex_path(explicit: str = "") -> str:
|
|
"""Find the REAL `@openai/codex` binary, skipping the hermes wrapper.
|
|
|
|
The wrapper at ~/.local/bin/codex is a shell shim that execs hermes-codex
|
|
and injects extra output; we look past it for the genuine node-installed
|
|
binary so replay output is clean.
|
|
"""
|
|
if explicit:
|
|
return explicit
|
|
env = os.environ.get("SKILLOPT_SLEEP_CODEX_PATH")
|
|
if env:
|
|
return env
|
|
candidates = [
|
|
os.path.expanduser("~/.nvm/versions/node/v22.22.3/bin/codex"),
|
|
]
|
|
# any nvm node version
|
|
nvm = os.path.expanduser("~/.nvm/versions/node")
|
|
if os.path.isdir(nvm):
|
|
for ver in sorted(os.listdir(nvm), reverse=True):
|
|
candidates.append(os.path.join(nvm, ver, "bin", "codex"))
|
|
for c in candidates:
|
|
if not c or not os.path.exists(c):
|
|
continue
|
|
try:
|
|
with open(c, "rb") as f:
|
|
head = f.read(64)
|
|
# skip the bash shim that execs hermes
|
|
if head.startswith(b"#!") and b"bash" in head:
|
|
continue
|
|
except Exception:
|
|
pass
|
|
return c
|
|
return "codex" # last resort (may be the wrapper)
|
|
|
|
|
|
class CodexCliBackend(CliBackend):
|
|
"""Drives the real Codex CLI: `codex exec -o <file>` for clean output."""
|
|
|
|
name = "codex"
|
|
|
|
def __init__(self, model: str = "", codex_path: str = "", timeout: int = 240,
|
|
sandbox: str = "read-only") -> None:
|
|
super().__init__(model=model or os.environ.get("SKILLOPT_SLEEP_CODEX_MODEL", ""),
|
|
timeout=timeout)
|
|
self.codex_path = resolve_codex_path(codex_path)
|
|
self.sandbox = sandbox
|
|
|
|
def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
|
|
import tempfile
|
|
out_path = tempfile.NamedTemporaryFile(
|
|
prefix="codex_last_", suffix=".txt", delete=False
|
|
).name
|
|
cmd = [
|
|
self.codex_path, "exec", "--skip-git-repo-check",
|
|
"--color", "never", "--sandbox", self.sandbox,
|
|
"-o", out_path,
|
|
]
|
|
if self.model:
|
|
cmd += ["-m", self.model]
|
|
cmd += ["--", prompt]
|
|
try:
|
|
subprocess.run(cmd, capture_output=True, text=True, timeout=self.timeout)
|
|
except Exception:
|
|
return ""
|
|
try:
|
|
with open(out_path, encoding="utf-8") as f:
|
|
return f.read().strip()
|
|
except Exception:
|
|
return ""
|
|
finally:
|
|
try:
|
|
os.unlink(out_path)
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
# ── Dual backend: target runs the task, optimizer proposes/judges edits ───────
|
|
|
|
class DualBackend(Backend):
|
|
"""Route operations to two backends, à la SkillOpt's target vs optimizer.
|
|
|
|
* attempt -> TARGET backend (the model the skill is deployed on)
|
|
* reflect -> OPTIMIZER backend (the stronger/cheaper model writing edits)
|
|
* judge -> OPTIMIZER backend (graded by the optimizer when no local rule)
|
|
|
|
This lets you optimize a skill with one model and run tasks on another, and
|
|
is the basis of the sleep-scenario transfer experiment (optimize cheap,
|
|
deploy expensive — or vice-versa).
|
|
"""
|
|
|
|
name = "dual"
|
|
|
|
def __init__(self, target: Backend, optimizer: Backend) -> None:
|
|
self.target = target
|
|
self.optimizer = optimizer
|
|
self.name = f"target={target.name}/optimizer={optimizer.name}"
|
|
|
|
def attempt(self, task, skill, memory):
|
|
return self.target.attempt(task, skill, memory)
|
|
|
|
def judge(self, task, response):
|
|
# local rule/exact judging needs no model; delegate to target which
|
|
# already short-circuits those. For rubric judging use the optimizer.
|
|
if task.reference_kind in {"rule", "exact"}:
|
|
return self.target.judge(task, response)
|
|
return self.optimizer.judge(task, response)
|
|
|
|
def reflect(self, failures, successes, skill, memory, **kw):
|
|
return self.optimizer.reflect(failures, successes, skill, memory, **kw)
|
|
|
|
def _call(self, prompt, *, max_tokens=1024):
|
|
# used by the LLM miner; prefer the optimizer (the "thinking" model)
|
|
return self.optimizer._call(prompt, max_tokens=max_tokens) # type: ignore[attr-defined]
|
|
|
|
def tokens_used(self):
|
|
return self.target.tokens_used() + self.optimizer.tokens_used()
|
|
|
|
|
|
def get_backend(
|
|
name: str,
|
|
*,
|
|
model: str = "",
|
|
claude_path: str = "claude",
|
|
codex_path: str = "",
|
|
) -> Backend:
|
|
n = (name or "mock").strip().lower()
|
|
if n in {"claude", "anthropic", "claude_cli", "claude_code"}:
|
|
return ClaudeCliBackend(model=model, claude_path=claude_path)
|
|
if n in {"codex", "codex_cli", "openai_codex"}:
|
|
return CodexCliBackend(model=model, codex_path=codex_path)
|
|
return MockBackend()
|
|
|
|
|
|
def build_backend(
|
|
*,
|
|
backend: str = "mock",
|
|
model: str = "",
|
|
optimizer_backend: str = "",
|
|
optimizer_model: str = "",
|
|
target_backend: str = "",
|
|
target_model: str = "",
|
|
codex_path: str = "",
|
|
) -> Backend:
|
|
"""Build a single or dual backend.
|
|
|
|
If optimizer_* or target_* are given, returns a DualBackend routing
|
|
attempt->target and reflect/judge->optimizer. Otherwise a single backend
|
|
from (backend, model).
|
|
"""
|
|
has_split = any([optimizer_backend, optimizer_model, target_backend, target_model])
|
|
if not has_split:
|
|
return get_backend(backend, model=model, codex_path=codex_path)
|
|
tgt = get_backend(target_backend or backend, model=target_model or model, codex_path=codex_path)
|
|
opt = get_backend(optimizer_backend or backend, model=optimizer_model or model, codex_path=codex_path)
|
|
return DualBackend(target=tgt, optimizer=opt)
|