Bogdan (Dan) Baciu
faf4700ae2
* feat(sleep): adopt reviewed skill subsets safely * fix(sleep): wire cycle staging and adopt-time review checks Address PR 212 review: run_sleep_cycle stages resolved SkillProposals, status/adopt list and select a subset, uniqueness is rechecked at adopt, and a failed adopted_skills.json write rolls live files back. Refs microsoft/SkillOpt#212 * test(sleep): mega-cover PR 212 review paths Adversarial CLI, adopt-time, cycle-staging, and auto-adopt cases for Yifan's five review items. Also tidy isort on the files this slice touches. Refs microsoft/SkillOpt#120 * fix(sleep): pin staged skill hashes and confine adopt targets Harden PR 212 adopt: sha256 pin each staged skill, revalidate the whole manifest before any live write, refuse symlink/missing-parent targets, skip notes on the cycle report, and reject empty --skill. Refs microsoft/SkillOpt#212 * fix(sleep): harden multi-skill fan-out adoption end to end --------- Co-authored-by: Yif-Yang <yif_yang@qq.com>
227 lines
10 KiB
Python
227 lines
10 KiB
Python
"""SkillOpt-Sleep — configuration.
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Config is JSON-first (yaml optional) so the engine and the deterministic
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experiment run with zero external dependencies. Defaults are safe:
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review-gated adoption, single-project scope, bounded token/task budgets.
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Resolution order (later wins):
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1. built-in DEFAULTS
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2. ~/.skillopt-sleep/config.json (or .yaml if PyYAML available)
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3. explicit overrides passed to load_config(**overrides)
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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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from dataclasses import dataclass, field
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from typing import Any, Dict, Optional
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HOME_STATE_DIR = os.path.expanduser("~/.skillopt-sleep")
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CLAUDE_HOME = os.path.expanduser("~/.claude")
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CODEX_HOME = os.path.expanduser("~/.codex")
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PI_HOME = os.path.expanduser("~/.pi")
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CURSOR_HOME = os.path.expanduser("~/.cursor")
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DEFAULTS: Dict[str, Any] = {
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# ── scope ──────────────────────────────────────────────────────────────
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"claude_home": CLAUDE_HOME,
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"codex_home": CODEX_HOME,
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"pi_home": PI_HOME,
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"cursor_home": CURSOR_HOME,
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"vscode_workspace_storage": "", # "" => auto-detect platform defaults
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"copilot_cli_session_store": "", # "" => ~/.copilot/session-store.db
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"opencode_db": "", # "" => OPENCODE_DB or the OpenCode XDG data path
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# Explicit sources also include copilot, copilot_cli, cursor, pi, and opencode.
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# ``auto`` keeps the established Codex-then-Claude precedence.
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"transcript_source": "claude",
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"projects": "invoked", # "invoked" | "all" | [list of abs paths]
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"invoked_project": "", # filled at runtime (cwd) when projects == "invoked"
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"lookback_hours": 72, # harvest window when no prior sleep recorded
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# ── budgets ────────────────────────────────────────────────────────────
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"max_tasks_per_night": 40,
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"max_tokens_per_night": 400_000,
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"holdout_fraction": 0.34, # legacy alias for val_fraction
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"val_fraction": 0.34, # real tasks reserved to gate updates
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"test_fraction": 0.0, # real tasks reserved as the final held-out measure
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# ── optimizer ──────────────────────────────────────────────────────────
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"backend": "mock", # "mock" | "claude" | "codex" | "copilot" | "cursor" | "pi" | "opencode"
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"model": "", # backend-specific; "" => backend default
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# Dual-backend split (both empty => single backend above plays all roles).
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# target = the model whose skill is deployed (runs `attempt` rollouts);
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# optimizer = the model that mines tasks, judges rubrics, writes edits.
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"optimizer_backend": "",
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"optimizer_model": "",
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"target_backend": "",
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"target_model": "",
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"azure_endpoint": "", # explicit endpoint for azure/compat backends
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"gate_mode": "on", # "on" (validation-gated) | "off" (greedy, no hard filter)
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"codex_path": "", # "" => auto-detect the real @openai/codex binary
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"pi_path": "", # "" => use `pi` on PATH
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"cursor_path": "", # "" => auto-detect the Cursor Agent CLI
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"opencode_path": "", # "" => SKILLOPT_SLEEP_OPENCODE_PATH, then `opencode` on PATH/PATHEXT
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"opencode_tool_replay": False, # explicit opt-in for OpenCode tool-aware replay
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"edit_budget": 4, # textual learning rate (max edits/night)
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"preferences": "", # free-text house rules injected into reflect as a prior
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"gate_metric": "mixed", # hard | soft | mixed (mixed best for tiny holdouts)
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"gate_mixed_weight": 0.5,
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"gate_no_regression": False, # reject any candidate that lowers a val-task score
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"replay_mode": "mock", # report label; fresh-worktree replay is not implemented
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# ── dream + recall (opt-in; defaults reproduce the prior single-shot loop) ─
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"dream_rollouts": 1, # >1 => multi-rollout contrastive reflection per task
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"dream_factor": 0, # >0 => add N synthetic variants of each task to the dream
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"recall_k": 0, # >0 => recall the K most-similar past tasks into the dream
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"evolve_memory": True, # consolidate CLAUDE.md
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"evolve_skill": True, # consolidate the managed SKILL.md
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"llm_mine": True, # use the backend to mine checkable tasks (real backends)
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"target_skill_path": "", # explicit SKILL.md target for repo-scoped agents
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"skill_roots": [], # extra explicit roots containing <name>/SKILL.md
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"target_task_filter": True, # prefer mined tasks matching target_skill_path/text
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"progress": False, # print phase progress to stderr
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# ── observability ──────────────────────────────────────────────────────
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"evidence_log": True, # write per-night evidence.jsonl (full evidentiary chain)
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"evidence_max_chars": 4000, # per-field truncation cap for evidence events
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# ``multi_skill_report`` is the compatibility alias used before fan-out
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# began staging independently adoptable proposals.
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"multi_skill_fanout": None,
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"multi_skill_report": False,
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# ── adoption / safety ──────────────────────────────────────────────────
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"auto_adopt": False, # default: stage + require explicit `adopt`
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"managed_skill_name": "skillopt-sleep-learned",
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"redact_secrets": True,
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"seed": 42,
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}
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@dataclass
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class SleepConfig:
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data: Dict[str, Any] = field(default_factory=lambda: dict(DEFAULTS))
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# convenient attribute access -------------------------------------------
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def __getattr__(self, name: str) -> Any:
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# only called when normal attribute lookup fails
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data = object.__getattribute__(self, "data")
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if name in data:
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return data[name]
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raise AttributeError(name)
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def get(self, key: str, default: Any = None) -> Any:
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return self.data.get(key, default)
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def to_dict(self) -> Dict[str, Any]:
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return dict(self.data)
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# paths ------------------------------------------------------------------
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@property
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def state_dir(self) -> str:
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# Allow full isolation: if the caller overrides state_dir explicitly,
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# honor it; else derive from claude_home's parent so a single
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# --claude-home flag isolates transcripts AND state together; else the
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# default ~/.skillopt-sleep.
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explicit = self.data.get("state_dir")
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if explicit:
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return explicit
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ch = self.data.get("claude_home", CLAUDE_HOME)
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if os.path.abspath(ch) != os.path.abspath(CLAUDE_HOME):
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return os.path.join(os.path.dirname(os.path.abspath(ch)), ".skillopt-sleep")
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return HOME_STATE_DIR
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@property
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def state_path(self) -> str:
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return os.path.join(self.state_dir, "state.json")
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@property
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def transcripts_dir(self) -> str:
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return os.path.join(self.data["claude_home"], "projects")
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@property
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def codex_archived_sessions_dir(self) -> str:
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return os.path.join(self.data["codex_home"], "archived_sessions")
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@property
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def pi_sessions_dir(self) -> str:
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pi_home = os.path.abspath(os.path.expanduser(str(self.data["pi_home"])))
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return os.path.join(pi_home, "agent", "sessions")
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@property
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def cursor_projects_dir(self) -> str:
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cursor_home = os.path.abspath(os.path.expanduser(str(self.data["cursor_home"])))
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return os.path.join(cursor_home, "projects")
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@property
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def copilot_cli_session_store(self) -> str:
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value = self.data.get("copilot_cli_session_store", "") or ""
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if not value:
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return ""
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return os.path.abspath(os.path.expanduser(str(value)))
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@property
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def opencode_db_path(self) -> str:
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value = self.data.get("opencode_db", "") or ""
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if not value:
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return ""
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if str(value) == ":memory:":
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return ":memory:"
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return os.path.abspath(os.path.expanduser(str(value)))
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@property
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def vscode_workspace_storage(self) -> str:
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value = self.data.get("vscode_workspace_storage", "") or ""
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if not value:
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return ""
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return os.path.abspath(os.path.expanduser(str(value)))
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@property
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def history_path(self) -> str:
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return os.path.join(self.data["claude_home"], "history.jsonl")
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@property
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def skills_dir(self) -> str:
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return os.path.join(self.data["claude_home"], "skills")
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def managed_skill_path(self) -> str:
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target = self.data.get("target_skill_path") or ""
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if target:
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target = os.path.expanduser(str(target))
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if not os.path.isabs(target):
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base = self.data.get("invoked_project") or os.getcwd()
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target = os.path.join(base, target)
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return os.path.abspath(target)
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return os.path.join(
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self.skills_dir, self.data["managed_skill_name"], "SKILL.md"
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)
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def _user_config_path() -> Optional[str]:
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for name in ("config.json", "config.yaml", "config.yml"):
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p = os.path.join(HOME_STATE_DIR, name)
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if os.path.exists(p):
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return p
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return None
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def _load_file(path: str) -> Dict[str, Any]:
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if path.endswith((".yaml", ".yml")):
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try:
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import yaml # optional
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with open(path) as f:
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return yaml.safe_load(f) or {}
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except Exception:
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return {}
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with open(path) as f:
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return json.load(f)
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def load_config(**overrides: Any) -> SleepConfig:
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data = dict(DEFAULTS)
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path = _user_config_path()
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if path:
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try:
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data.update(_load_file(path) or {})
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except Exception:
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pass
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data.update({k: v for k, v in overrides.items() if v is not None})
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if data.get("projects") == "invoked" and not data.get("invoked_project"):
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data["invoked_project"] = os.getcwd()
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return SleepConfig(data=data)
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