Yifan Yang
4e7add899d
Add skillopt/sleep — a deployment-time companion to SkillOpt that gives a
local Claude agent a nightly "sleep cycle":
harvest ~/.claude transcripts -> mine recurring tasks -> replay offline
-> consolidate (reflect -> bounded edit -> held-out GATE) -> stage -> adopt
Synthesizes SkillOpt (validation-gated bounded text optimization, reusing
skillopt.evaluation.gate verbatim), Claude Dreams (offline consolidation;
input never mutated; review-then-adopt), and the agent-sleep paper
(short-term experience -> long-term competence).
Engine (skillopt/sleep/, import-light, py>=3.10):
- harvest.py read-only parse of session JSONL + history.jsonl
- mine.py sessions -> TaskRecords (heuristic miner + LLM hook)
- backend.py MockBackend (deterministic, no API) + AnthropicBackend
- replay.py offline re-run -> (hard, soft) scores
- consolidate.py one SkillOpt epoch behind a held-out gate
- memory.py protected-region edits to SKILL.md / CLAUDE.md
- staging.py stage proposals; adopt with backup (Dreams safety contract)
- cycle.py + __main__.py orchestrator + CLI (run/dry-run/status/adopt/harvest)
Plugin (skillopt-sleep-plugin/): plugin.json, /sleep command, skillopt-sleep
skill, SessionEnd hook, bundled runner + cron generator.
Validation (deterministic, no API): persona experiment proves held-out lift
(researcher 0.33->1.0, programmer 0.32->1.0) AND that the gate rejects an
injected harmful edit. 13 stdlib-unittest tests pass, incl. full cycle +
adopt-with-backup and parsing of real on-disk transcripts.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
335 lines
14 KiB
Python
335 lines
14 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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# ── 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 == "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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# ── Anthropic backend (real API; lazy, optional) ──────────────────────────────
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class AnthropicBackend(Backend):
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"""Uses the user's Anthropic budget. Prefers the `claude` CLI (already
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authenticated on the box); falls back to the anthropic SDK if present.
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This is intentionally thin for Phase 1 — it wires the prompts and parses
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JSON. Phase 3 will expand prompts/judging to match SkillOpt's analyst
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prompts under skillopt/prompts/.
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"""
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name = "anthropic"
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def __init__(self, model: str = "", claude_path: str = "claude") -> None:
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self.model = model or os.environ.get("ANTHROPIC_MODEL", "") or "sonnet"
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self.claude_path = claude_path
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self._tokens = 0
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# -- low-level call -----------------------------------------------------
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def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
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# Try the CLI first (non-interactive, text output).
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try:
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cmd = [self.claude_path, "-p", "--output-format", "text"]
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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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proc = subprocess.run(
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cmd, capture_output=True, text=True, timeout=180,
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)
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out = (proc.stdout or "").strip()
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if out:
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self._tokens += len(prompt) // 4 + len(out) // 4
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return out
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except Exception:
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pass
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# SDK fallback
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try:
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import anthropic # type: ignore
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client = anthropic.Anthropic()
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msg = client.messages.create(
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model=self.model or "claude-sonnet-4-5",
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max_tokens=max_tokens,
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messages=[{"role": "user", "content": prompt}],
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)
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text = "".join(getattr(b, "text", "") for b in msg.content)
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self._tokens += getattr(msg.usage, "input_tokens", 0) + getattr(
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msg.usage, "output_tokens", 0
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)
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return text.strip()
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except Exception:
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return ""
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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 exactly.\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."
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)
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return self._call(prompt)
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def judge(self, task: TaskRecord, response: str) -> Tuple[float, float, str]:
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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"
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prompt = (
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"Score the response against the rubric on a 0-1 scale. "
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"Return 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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raw = self._call(prompt, max_tokens=256)
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m = re.search(r"\{.*\}", raw, re.DOTALL)
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if m:
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try:
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obj = json.loads(m.group(0))
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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", ""))
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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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fail_text = "\n".join(
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f"- intent: {t.intent[:200]}\n got: {r.response[:200]}\n why: {r.fail_reason[:160]}"
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for t, r in failures[:8]
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)
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target = "skill" if evolve_skill else "memory"
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prompt = (
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"You are SkillOpt's optimizer. Propose at most "
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f"{edit_budget} bounded edits to the {target} document so the agent "
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"stops failing these recurring tasks. Each edit must be a short, "
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"general, reusable rule (not task-specific). Return JSON list: "
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"[{\"op\":\"add|replace|delete\",\"content\":\"...\",\"rationale\":\"...\"}].\n\n"
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f"# Current {target}\n{(skill if target=='skill' else memory) or '(empty)'}\n\n"
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f"# Recurring failures\n{fail_text or '(none)'}"
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)
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raw = self._call(prompt, max_tokens=1024)
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m = re.search(r"\[.*\]", raw, re.DOTALL)
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edits: List[EditRecord] = []
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if m:
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try:
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for e in json.loads(m.group(0))[:edit_budget]:
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edits.append(
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EditRecord(
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target=target,
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op=str(e.get("op", "add")),
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content=str(e.get("content", "")).strip(),
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anchor=str(e.get("anchor", "")),
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rationale=str(e.get("rationale", "")),
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)
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)
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except Exception:
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pass
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return [e for e in edits if e.content]
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def tokens_used(self) -> int:
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return self._tokens
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def get_backend(name: str, *, model: str = "", claude_path: str = "claude") -> Backend:
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if name == "anthropic":
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return AnthropicBackend(model=model, claude_path=claude_path)
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return MockBackend()
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