Add reviewed task-file flow for Codex sleep runs

skillopt_sleep/__main__.py

@@ -9,6 +9,10 @@
 Common flags:
     --project PATH      project to evolve (default: cwd)
     --scope all|invoked harvest scope (default: invoked)
+    --max-sessions N    cap transcript sessions per run
+    --max-tasks N       cap mined tasks per run
+    --target-skill-path PATH explicit live SKILL.md to stage/adopt
+    --tasks-file PATH   reviewed TaskRecord JSON file to replay instead of harvesting
     --backend mock|claude|codex|copilot
     --source claude|codex|auto
     --model NAME
@@ -31,6 +35,35 @@ from skillopt_sleep.mine import mine
 from skillopt_sleep.staging import adopt as adopt_staging
 from skillopt_sleep.staging import latest_staging
 from skillopt_sleep.state import SleepState
+from skillopt_sleep.tasks_file import load_tasks_file, make_tasks_payload, write_tasks_file
+
+
+def _read_text(path: str) -> str:
+    try:
+        with open(path, encoding="utf-8") as f:
+            return f.read()
+    except Exception:
+        return ""
+
+
+def _report_payload(rep, outcome) -> Dict[str, Any]:
+    return {
+        "night": rep.night,
+        "accepted": rep.accepted,
+        "gate_action": rep.gate_action,
+        "no_edits_reason": getattr(rep, "no_edits_reason", ""),
+        "baseline": rep.baseline_score,
+        "candidate": rep.candidate_score,
+        "n_tasks": rep.n_tasks,
+        "n_sessions": rep.n_sessions,
+        "n_accepted_edits": len(rep.edits),
+        "n_rejected_edits": len(rep.rejected_edits),
+        "edits": [e.__dict__ for e in rep.edits],
+        "rejected_edits": [e.__dict__ for e in rep.rejected_edits],
+        "notes": rep.notes,
+        "staging_dir": outcome.staging_dir,
+        "adopted": outcome.adopted,
+    }
 
 
 def _add_common(p: argparse.ArgumentParser) -> None:
@@ -45,11 +78,21 @@ def _add_common(p: argparse.ArgumentParser) -> None:
                    help="session transcript source")
     p.add_argument("--lookback-hours", type=int, default=0)
     p.add_argument("--edit-budget", type=int, default=0)
+    p.add_argument("--max-sessions", type=int, default=0,
+                   help="cap harvested sessions before mining; default derives from max tasks")
+    p.add_argument("--max-tasks", type=int, default=0,
+                   help="cap mined tasks for this run")
+    p.add_argument("--target-skill-path", default="",
+                   help="explicit live SKILL.md path to evolve/stage/adopt")
+    p.add_argument("--tasks-file", default="",
+                   help="reviewed TaskRecord JSON file to replay instead of harvesting")
+    p.add_argument("--progress", action="store_true",
+                   help="print phase progress to stderr")
     p.add_argument("--auto-adopt", action="store_true")
     p.add_argument("--json", action="store_true")
 
 
-def _cfg_from_args(args) -> Any:
+def _cfg_from_args(args, task_meta: Dict[str, Any] | None = None) -> Any:
     overrides: Dict[str, Any] = {}
     if args.project:
         overrides["invoked_project"] = os.path.abspath(args.project)
@@ -72,30 +115,63 @@ def _cfg_from_args(args) -> Any:
         overrides["lookback_hours"] = args.lookback_hours
     if getattr(args, "edit_budget", 0):
         overrides["edit_budget"] = args.edit_budget
+    if getattr(args, "max_sessions", 0):
+        overrides["max_sessions_per_night"] = args.max_sessions
+    if getattr(args, "max_tasks", 0):
+        overrides["max_tasks_per_night"] = args.max_tasks
+    target_skill_path = getattr(args, "target_skill_path", "")
+    if not target_skill_path and task_meta:
+        target_skill_path = str(task_meta.get("target_skill_path") or "")
+    if target_skill_path:
+        path = os.path.expanduser(target_skill_path)
+        if args.project and not os.path.isabs(path):
+            path = os.path.join(os.path.abspath(args.project), path)
+        overrides["target_skill_path"] = os.path.abspath(path)
+    if getattr(args, "progress", False):
+        overrides["progress"] = True
     if getattr(args, "auto_adopt", False):
         overrides["auto_adopt"] = True
     return load_config(**overrides)
 
 
 def cmd_run(args, dry: bool = False) -> int:
-    cfg = _cfg_from_args(args)
-    outcome = run_sleep_cycle(cfg, dry_run=dry)
+    task_meta: Dict[str, Any] = {}
+    tasks = None
+    if getattr(args, "tasks_file", ""):
+        # Load once before config so target_skill_path can default from metadata.
+        tasks, task_meta = load_tasks_file(args.tasks_file)
+    cfg = _cfg_from_args(args, task_meta=task_meta)
+    if getattr(args, "tasks_file", ""):
+        tasks, task_meta = load_tasks_file(
+            args.tasks_file,
+            holdout_fraction=cfg.get("holdout_fraction", 0.34),
+            seed=cfg.get("seed", 42),
+        )
+        if cfg.get("backend", "mock") != "mock" and task_meta.get("reviewed") is not True:
+            print(
+                "[sleep] refusing real-backend replay from an unreviewed tasks file; "
+                "inspect/redact it and set \"reviewed\": true first",
+                file=sys.stderr,
+            )
+            return 2
+    outcome = run_sleep_cycle(cfg, seed_tasks=tasks, dry_run=dry)
     rep = outcome.report
     if args.json:
-        print(json.dumps({
-            "night": rep.night, "accepted": rep.accepted,
-            "gate_action": rep.gate_action,
-            "baseline": rep.baseline_score, "candidate": rep.candidate_score,
-            "n_tasks": rep.n_tasks, "n_sessions": rep.n_sessions,
-            "edits": [e.__dict__ for e in rep.edits],
-            "staging_dir": outcome.staging_dir, "adopted": outcome.adopted,
-        }, ensure_ascii=False, indent=2))
+        payload = _report_payload(rep, outcome)
+        if task_meta:
+            payload["tasks_file"] = task_meta.get("tasks_file", "")
+            payload["tasks_reviewed"] = task_meta.get("reviewed", False)
+        print(json.dumps(payload, ensure_ascii=False, indent=2))
     else:
         print(f"[sleep] night {rep.night}: {rep.n_sessions} sessions -> {rep.n_tasks} tasks")
         print(f"[sleep] held-out {rep.baseline_score:.3f} -> {rep.candidate_score:.3f} "
               f"=> {rep.gate_action} (accepted={rep.accepted})")
         for e in rep.edits:
             print(f"   + [{e.target}/{e.op}] {e.content}")
+        if rep.rejected_edits:
+            print("[sleep] rejected by gate:")
+            for e in rep.rejected_edits:
+                print(f"   - [{e.target}/{e.op}] {e.content}")
         if outcome.staging_dir:
             print(f"[sleep] staged: {outcome.staging_dir}")
             if not outcome.adopted:
@@ -152,16 +228,42 @@ def cmd_adopt(args) -> int:
 
 def cmd_harvest(args) -> int:
     cfg = _cfg_from_args(args)
-    digests = harvest_for_config(cfg, limit=cfg.get("max_tasks_per_night", 40) * 3)
-    tasks = mine(digests, max_tasks=cfg.get("max_tasks_per_night", 40),
-                 holdout_fraction=cfg.get("holdout_fraction", 0.34), seed=cfg.get("seed", 42))
+    session_limit = cfg.get("max_sessions_per_night", 0) or cfg.get("max_tasks_per_night", 40) * 3
+    target_skill_path = cfg.managed_skill_path() if cfg.get("target_skill_path", "") else ""
+    target_skill_text = _read_text(target_skill_path) if target_skill_path else ""
+    max_tasks = cfg.get("max_tasks_per_night", 40)
+    candidate_limit = max_tasks
+    if cfg.get("target_task_filter", True) and target_skill_text:
+        candidate_limit = max(max_tasks, max_tasks * 3)
+    digests = harvest_for_config(cfg, limit=session_limit)
+    tasks = mine(
+        digests,
+        max_tasks=max_tasks,
+        candidate_limit=candidate_limit,
+        holdout_fraction=cfg.get("holdout_fraction", 0.34),
+        seed=cfg.get("seed", 42),
+        target_skill_text=target_skill_text,
+        target_skill_path=target_skill_path,
+    )
+    payload = make_tasks_payload(
+        tasks,
+        project=cfg.get("invoked_project") or os.getcwd(),
+        transcript_source=cfg.get("transcript_source", ""),
+        n_sessions=len(digests),
+        target_skill_path=target_skill_path,
+    )
+    output_path = ""
+    if getattr(args, "output", ""):
+        output_path = write_tasks_file(args.output, payload)
     if args.json:
-        print(json.dumps({
-            "n_sessions": len(digests),
-            "tasks": [t.to_dict() for t in tasks],
-        }, ensure_ascii=False, indent=2))
+        json_payload = dict(payload)
+        if output_path:
+            json_payload["output"] = output_path
+        print(json.dumps(json_payload, ensure_ascii=False, indent=2))
     else:
         print(f"[sleep] {len(digests)} sessions -> {len(tasks)} tasks")
+        if output_path:
+            print(f"[sleep] wrote reviewed-task draft: {output_path}")
         for t in tasks:
             print(f"  [{t.split}/{t.outcome}] {t.intent[:90]}")
     return 0
@@ -207,6 +309,7 @@ def main(argv=None) -> int:
     p_adopt.add_argument("--staging", default="", help="specific staging dir")
     p_harvest = sub.add_parser("harvest", help="debug: show mined tasks")
     _add_common(p_harvest)
+    p_harvest.add_argument("--output", default="", help="write mined tasks JSON for review")
     p_sched = sub.add_parser("schedule", help="install a nightly cron entry for this project")
     _add_common(p_sched)
     p_sched.add_argument("--hour", type=int, default=3)

skillopt_sleep/backend.py

@@ -316,6 +316,8 @@ class CliBackend(Backend):
         self.timeout = timeout
         self._tokens = 0
         self._cache: Dict[str, str] = {}
+        self.last_call_error = ""
+        self.last_reflect_raw = ""
 
     # subclasses override --------------------------------------------------
     def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
@@ -692,15 +694,25 @@ class CodexCliBackend(CliBackend):
 
     name = "codex"
 
-    def __init__(self, model: str = "", codex_path: str = "", timeout: int = 240,
-                 sandbox: str = "read-only") -> None:
+    def __init__(
+        self,
+        model: str = "",
+        codex_path: str = "",
+        timeout: int = 240,
+        sandbox: str = "read-only",
+        project_dir: str = "",
+    ) -> 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
+        self.project_dir = (
+            os.path.abspath(os.path.expanduser(project_dir)) if project_dir else ""
+        )
 
     def _call(self, prompt: str, *, max_tokens: int = 1024) -> str:
         import tempfile
+        self.last_call_error = ""
         out_path = tempfile.NamedTemporaryFile(
             prefix="codex_last_", suffix=".txt", delete=False
         ).name
@@ -709,18 +721,39 @@ class CodexCliBackend(CliBackend):
             "--color", "never", "--sandbox", self.sandbox,
             "-o", out_path,
         ]
+        if self.project_dir:
+            cmd[3:3] = ["-C", self.project_dir]
         if self.model:
             cmd += ["-m", self.model]
         cmd += ["--", prompt]
+        proc = None
         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 ""
+            try:
+                proc = subprocess.run(
+                    cmd,
+                    capture_output=True,
+                    text=True,
+                    timeout=self.timeout,
+                    cwd=self.project_dir or None,
+                )
+            except subprocess.TimeoutExpired:
+                self.last_call_error = f"codex exec timed out after {self.timeout}s"
+                return ""
+            except Exception as exc:
+                self.last_call_error = f"codex exec failed: {exc}"
+                return ""
+            try:
+                with open(out_path, encoding="utf-8") as f:
+                    out = f.read().strip()
+                if out:
+                    return out
+            except Exception as exc:
+                self.last_call_error = f"could not read codex output file: {exc}"
+            stdout = (proc.stdout or "").strip() if proc is not None else ""
+            stderr = (proc.stderr or "").strip() if proc is not None else ""
+            if proc is not None and proc.returncode != 0 and not self.last_call_error:
+                self.last_call_error = f"codex exec exited {proc.returncode}: {stderr[:500]}"
+            return stdout or stderr
         finally:
             try:
                 os.unlink(out_path)
@@ -1238,12 +1271,13 @@ def get_backend(
     claude_path: str = "claude",
     codex_path: str = "",
     azure_endpoint: str = "",
+    project_dir: 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 CodexCliBackend(model=model, codex_path=codex_path, project_dir=project_dir)
     if n in {"azure", "azure_openai", "aoai"}:
         return AzureOpenAIBackend(deployment=model, endpoint=azure_endpoint)
     if n in {"azure-responses", "azure_responses", "aoai-responses", "responses"}:
@@ -1265,6 +1299,7 @@ def build_backend(
     codex_path: str = "",
     azure_endpoint: str = "",
     preferences: str = "",
+    project_dir: str = "",
 ) -> Backend:
     """Build a single or dual backend.
 
@@ -1275,13 +1310,21 @@ def build_backend(
     """
     has_split = any([optimizer_backend, optimizer_model, target_backend, target_model])
     if not has_split:
-        be = get_backend(backend, model=model, codex_path=codex_path, azure_endpoint=azure_endpoint)
+        be = get_backend(
+            backend,
+            model=model,
+            codex_path=codex_path,
+            azure_endpoint=azure_endpoint,
+            project_dir=project_dir,
+        )
         be.preferences = preferences
         return be
     tgt = get_backend(target_backend or backend, model=target_model or model,
-                      codex_path=codex_path, azure_endpoint=azure_endpoint)
+                      codex_path=codex_path, azure_endpoint=azure_endpoint,
+                      project_dir=project_dir)
     opt = get_backend(optimizer_backend or backend, model=optimizer_model or model,
-                      codex_path=codex_path, azure_endpoint=azure_endpoint)
+                      codex_path=codex_path, azure_endpoint=azure_endpoint,
+                      project_dir=project_dir)
     opt.preferences = preferences  # reflect runs on the optimizer
     dual = DualBackend(target=tgt, optimizer=opt)
     dual.preferences = preferences

skillopt_sleep/config.py

@@ -51,6 +51,9 @@ DEFAULTS: Dict[str, Any] = {
     "evolve_memory": True,        # consolidate CLAUDE.md
     "evolve_skill": True,         # consolidate the managed SKILL.md
     "llm_mine": True,             # use the backend to mine checkable tasks (real backends)
+    "target_skill_path": "",      # explicit SKILL.md target for repo-scoped agents
+    "target_task_filter": True,   # prefer mined tasks matching target_skill_path/text
+    "progress": False,            # print phase progress to stderr
     # ── adoption / safety ──────────────────────────────────────────────────
     "auto_adopt": False,          # default: stage + require explicit `adopt`
     "managed_skill_name": "skillopt-sleep-learned",
@@ -113,6 +116,13 @@ class SleepConfig:
         return os.path.join(self.data["claude_home"], "skills")
 
     def managed_skill_path(self) -> str:
+        target = self.data.get("target_skill_path") or ""
+        if target:
+            target = os.path.expanduser(str(target))
+            if not os.path.isabs(target):
+                base = self.data.get("invoked_project") or os.getcwd()
+                target = os.path.join(base, target)
+            return os.path.abspath(target)
         return os.path.join(
             self.skills_dir, self.data["managed_skill_name"], "SKILL.md"
         )

skillopt_sleep/cycle.py

@@ -10,6 +10,7 @@ CI use. With backend="anthropic" it spends the user's budget for real lift.
 from __future__ import annotations
 
 import os
+import sys
 from dataclasses import dataclass
 from typing import List, Optional
 
@@ -49,6 +50,11 @@ def _read(path: str) -> str:
         return ""
 
 
+def _progress(cfg: SleepConfig, message: str) -> None:
+    if cfg.get("progress", False):
+        print(f"[sleep] {message}", file=sys.stderr, flush=True)
+
+
 def _render_report_md(report: SleepReport, cfg: SleepConfig) -> str:
     lines = [
         f"# SkillOpt-Sleep — night {report.night} report",
@@ -108,6 +114,26 @@ def run_sleep_cycle(
         cfg.get("backend", "mock"),
         model=cfg.get("model", ""),
         codex_path=cfg.get("codex_path", ""),
+        project_dir=project,
+    )
+    _progress(cfg, f"night {night}: project={project} backend={backend.name}")
+
+    # ── live skill/memory docs ───────────────────────────────────────────
+    live_memory_path = os.path.join(project, "CLAUDE.md")
+    live_skill_path = cfg.managed_skill_path()
+    _progress(cfg, f"live skill: {live_skill_path}")
+    raw_skill = _read(live_skill_path)
+    skill = raw_skill
+    memory = _read(live_memory_path)
+    if not skill:
+        skill = ensure_skill_scaffold(
+            "", name=cfg.get("managed_skill_name", "skillopt-sleep-learned"),
+            description="Preferences and procedures learned from past local agent sessions.",
+        )
+    target_filter = bool(
+        cfg.get("target_task_filter", True)
+        and cfg.get("target_skill_path", "")
+        and raw_skill
     )
 
     # ── 1+2. harvest + mine (unless seed_tasks injected) ─────────────────
@@ -115,14 +141,25 @@ def run_sleep_cycle(
     if seed_tasks is not None:
         tasks = seed_tasks
         n_sessions = 0
+        _progress(cfg, f"using {len(tasks)} seeded tasks")
     else:
         since = state.last_harvest_for(project)
+        max_tasks = cfg.get("max_tasks_per_night", 40)
+        max_sessions = cfg.get("max_sessions_per_night", 0) or max_tasks * 3
+        candidate_limit = max_tasks
+        if target_filter:
+            candidate_limit = max(max_tasks, max_tasks * 3)
+        _progress(
+            cfg,
+            f"harvest start: source={cfg.get('transcript_source')} max_sessions={max_sessions}",
+        )
         digests = harvest_for_config(
             cfg,
             since_iso=since,
-            limit=cfg.get("max_tasks_per_night", 40) * 3,
+            limit=max_sessions,
         )
         n_sessions = len(digests)
+        _progress(cfg, f"harvest done: sessions={n_sessions}")
         # When a real backend is configured, use it to mine checkable tasks from
         # the transcripts (rubric/rule judges); otherwise fall back to the
         # heuristic miner (no API, no checkable reference).
@@ -130,27 +167,29 @@ def run_sleep_cycle(
         if cfg.get("backend", "mock") != "mock" and cfg.get("llm_mine", True):
             try:
                 from skillopt_sleep.llm_miner import make_llm_miner
-                llm_miner = make_llm_miner(backend, max_tasks=cfg.get("max_tasks_per_night", 40))
+                llm_miner = make_llm_miner(
+                    backend,
+                    max_sessions=max_sessions,
+                    max_tasks=candidate_limit,
+                )
             except Exception:
                 llm_miner = None
+        _progress(
+            cfg,
+            f"mine start: max_tasks={max_tasks} candidate_limit={candidate_limit} "
+            f"llm_mine={llm_miner is not None} target_filter={target_filter}",
+        )
         tasks = mine(
             digests,
-            max_tasks=cfg.get("max_tasks_per_night", 40),
+            max_tasks=max_tasks,
+            candidate_limit=candidate_limit,
             holdout_fraction=cfg.get("holdout_fraction", 0.34),
             seed=cfg.get("seed", 42),
             llm_miner=llm_miner,
+            target_skill_text=raw_skill if target_filter else "",
+            target_skill_path=live_skill_path if target_filter else "",
         )
-
-    # ── live skill/memory docs ───────────────────────────────────────────
-    live_memory_path = os.path.join(project, "CLAUDE.md")
-    live_skill_path = cfg.managed_skill_path()
-    skill = _read(live_skill_path)
-    memory = _read(live_memory_path)
-    if not skill:
-        skill = ensure_skill_scaffold(
-            "", name=cfg.get("managed_skill_name", "skillopt-sleep-learned"),
-            description="Preferences and procedures learned from past local agent sessions.",
-        )
+        _progress(cfg, f"mine done: tasks={len(tasks)}")
 
     report = SleepReport(
         night=night, project=project, started_at=started,
@@ -172,6 +211,7 @@ def run_sleep_cycle(
     # / dream_factor enrich the training signal. With the defaults (recall_k=0,
     # dream_rollouts=1, dream_factor=0) this is exactly the prior single-shot
     # consolidate — behavior is unchanged unless the user opts in.
+    _progress(cfg, "consolidate start")
     recall_k = int(cfg.get("recall_k", 0) or 0)
     history_tasks = []
     if recall_k > 0:
@@ -192,12 +232,18 @@ def run_sleep_cycle(
     )
     # archive tonight's real (non-dream) tasks so future nights can recall them
     state.add_to_archive([t.to_dict() for t in tasks if t.origin != "dream"])
+    _progress(
+        cfg,
+        f"consolidate done: gate={result.gate_action} accepted={result.accepted} "
+        f"edits={len(result.applied_edits)} rejected={len(result.rejected_edits)}",
+    )
 
     report.n_replayed = len(tasks)
     report.baseline_score = result.baseline_score
     report.candidate_score = result.candidate_score
     report.accepted = result.accepted
     report.gate_action = result.gate_action
+    report.no_edits_reason = getattr(result, "no_edits_reason", "")
     report.edits = result.applied_edits
     report.rejected_edits = result.rejected_edits
     report.tokens_used = backend.tokens_used()
@@ -208,6 +254,7 @@ def run_sleep_cycle(
     adopted = False
     adopted_paths: List[str] = []
     if not dry_run:
+        _progress(cfg, "staging start")
         report_md = _render_report_md(report, cfg)
         proposed_skill = result.new_skill if (cfg.get("evolve_skill") and result.accepted) else None
         proposed_memory = result.new_memory if (cfg.get("evolve_memory") and result.accepted) else None

skillopt_sleep/mine.py

@@ -15,8 +15,10 @@ basis of the deterministic experiment.
 from __future__ import annotations
 
 import hashlib
+import os
 import re
-from typing import Any, Callable, List, Optional
+from collections import Counter
+from typing import Any, Callable, List, Optional, Set, Tuple
 
 from skillopt_sleep.types import SessionDigest, TaskRecord
 
@@ -39,6 +41,99 @@ def _looks_positive(signals: List[str]) -> bool:
     return any(s.startswith("pos:") for s in signals)
 
 
+_TARGET_STOPWORDS = {
+    "about", "after", "again", "agent", "agents", "all", "also", "always",
+    "and", "any", "are", "before", "being", "but", "can", "codex",
+    "current", "default", "docs", "does", "done", "each", "file", "files",
+    "for", "from", "have", "into", "keep", "must", "not", "only", "path",
+    "paths", "project", "read", "repo", "request", "requests", "rule",
+    "rules", "same", "should", "skill", "skills", "source", "start",
+    "task", "tasks", "that", "the", "their", "then", "this", "unless",
+    "update", "user", "users", "when", "with", "work", "workflow",
+}
+
+
+def _target_tokens(text: str) -> List[str]:
+    tokens: List[str] = []
+    for raw in re.findall(r"[\w][\w.-]*", (text or "").lower(), flags=re.UNICODE):
+        parts = [raw] + re.split(r"[\W_]+", raw, flags=re.UNICODE)
+        for part in parts:
+            if len(part) < 3 or part.isdigit() or part in _TARGET_STOPWORDS:
+                continue
+            tokens.append(part)
+    return tokens
+
+
+def _expand_target_keywords(keywords: Set[str]) -> None:
+    if "mcp" in keywords:
+        keywords.update({
+            "configure", "configuration", "connect", "connected", "enable",
+            "enabled", "install", "installed", "server", "servers",
+            "настрой", "настроить", "подключи", "подключить",
+        })
+    if {"conflict", "conflicts"} & keywords:
+        keywords.update({
+            "cherry", "conflict", "conflicts", "git", "merge", "rebase",
+            "unmerged", "конфликт", "конфликты",
+        })
+
+
+def target_task_keywords(
+    target_skill_text: str,
+    target_skill_path: str = "",
+    *,
+    limit: int = 180,
+) -> Tuple[Set[str], Set[str]]:
+    """Return (strong, weak) keywords that describe a target skill."""
+    path_text = (target_skill_path or "").replace(os.sep, " ")
+    headings = "\n".join(re.findall(r"(?m)^#+\s+(.+)$", target_skill_text or ""))
+    strong = set(_target_tokens(path_text + "\n" + headings))
+    weak = set(strong)
+    counts = Counter(_target_tokens(target_skill_text or ""))
+    for token, _count in counts.most_common(limit):
+        weak.add(token)
+    _expand_target_keywords(strong)
+    _expand_target_keywords(weak)
+    return strong, weak
+
+
+def _task_search_text(task: TaskRecord) -> str:
+    return "\n".join([
+        task.intent or "",
+        task.context_excerpt or "",
+        " ".join(task.tags or []),
+    ])
+
+
+def filter_tasks_for_target(
+    tasks: List[TaskRecord],
+    target_skill_text: str,
+    target_skill_path: str = "",
+) -> List[TaskRecord]:
+    """Prefer tasks whose language overlaps the explicit target skill.
+
+    If nothing matches, return the original list. This keeps a target run useful
+    even when transcripts are too sparse or the skill is too generic.
+    """
+    strong, weak = target_task_keywords(target_skill_text, target_skill_path)
+    if not tasks or not (strong or weak):
+        return tasks
+
+    ranked = []
+    for idx, task in enumerate(tasks):
+        tokens = set(_target_tokens(_task_search_text(task)))
+        strong_hits = tokens & strong
+        weak_hits = tokens & weak
+        if not strong_hits and len(weak_hits) < 2:
+            continue
+        score = len(strong_hits) * 3 + len(weak_hits)
+        ranked.append((score, idx, task))
+    if not ranked:
+        return tasks
+    ranked.sort(key=lambda item: (-item[0], item[1]))
+    return [task for _score, _idx, task in ranked]
+
+
 def heuristic_mine(
     digests: List[SessionDigest],
     *,
@@ -192,11 +287,15 @@ def mine(
     digests: List[SessionDigest],
     *,
     max_tasks: int = 40,
+    candidate_limit: int = 0,
     holdout_fraction: float = 0.34,
     seed: int = 42,
     llm_miner: Optional[Callable[[List[SessionDigest]], List[TaskRecord]]] = None,
+    target_skill_text: str = "",
+    target_skill_path: str = "",
 ) -> List[TaskRecord]:
     """Top-level miner. Uses ``llm_miner`` if provided, else heuristic."""
+    candidate_limit = candidate_limit or max_tasks
     tasks: List[TaskRecord] = []
     if llm_miner is not None:
         try:
@@ -204,7 +303,10 @@ def mine(
         except Exception:
             tasks = []
     if not tasks:
-        tasks = heuristic_mine(digests, max_tasks=max_tasks)
+        tasks = heuristic_mine(digests, max_tasks=candidate_limit)
     tasks = dedup_tasks(tasks)
+    if target_skill_text or target_skill_path:
+        tasks = filter_tasks_for_target(tasks, target_skill_text, target_skill_path)
+    tasks = tasks[:max_tasks]
     tasks = assign_splits(tasks, holdout_fraction=holdout_fraction, seed=seed)
     return tasks

skillopt_sleep/tasks_file.py

@@ -0,0 +1,81 @@
+"""Reviewed task-file helpers for privacy-safe SkillOpt-Sleep runs."""
+from __future__ import annotations
+
+import json
+import os
+from typing import Any, Dict, List, Tuple
+
+from skillopt_sleep.mine import assign_splits, normalize_legacy_split
+from skillopt_sleep.types import TaskRecord
+
+
+def make_tasks_payload(
+    tasks: List[TaskRecord],
+    *,
+    project: str,
+    transcript_source: str = "",
+    n_sessions: int = 0,
+    target_skill_path: str = "",
+) -> Dict[str, Any]:
+    return {
+        "format": "skillopt_sleep.tasks.v1",
+        "project": project,
+        "transcript_source": transcript_source,
+        "n_sessions": n_sessions,
+        "target_skill_path": target_skill_path,
+        "reviewed": False,
+        "tasks": [t.to_dict() for t in tasks],
+    }
+
+
+def write_tasks_file(path: str, payload: Dict[str, Any]) -> str:
+    out = os.path.abspath(os.path.expanduser(path))
+    parent = os.path.dirname(out)
+    if parent:
+        os.makedirs(parent, exist_ok=True)
+    with open(out, "w", encoding="utf-8") as f:
+        json.dump(payload, f, ensure_ascii=False, indent=2)
+        f.write("\n")
+    return out
+
+
+def _normalize_tasks(
+    tasks: List[TaskRecord],
+    *,
+    holdout_fraction: float,
+    seed: int,
+) -> List[TaskRecord]:
+    for task in tasks:
+        task.split = normalize_legacy_split(task.split or "train")
+    if len(tasks) >= 2 and not any(task.split in {"val", "test"} for task in tasks):
+        tasks = assign_splits(tasks, holdout_fraction=holdout_fraction, seed=seed)
+    return tasks
+
+
+def load_tasks_file(
+    path: str,
+    *,
+    holdout_fraction: float = 0.34,
+    seed: int = 42,
+) -> Tuple[List[TaskRecord], Dict[str, Any]]:
+    source = os.path.abspath(os.path.expanduser(path))
+    with open(source, encoding="utf-8") as f:
+        payload = json.load(f)
+    if isinstance(payload, list):
+        meta: Dict[str, Any] = {"format": "skillopt_sleep.tasks.v1", "tasks_file": source}
+        raw_tasks = payload
+    elif isinstance(payload, dict):
+        meta = {k: v for k, v in payload.items() if k != "tasks"}
+        meta["tasks_file"] = source
+        raw_tasks = payload.get("tasks", [])
+    else:
+        raise ValueError("tasks file must contain a JSON object with tasks or a JSON task array")
+    if not isinstance(raw_tasks, list):
+        raise ValueError("tasks file field 'tasks' must be an array")
+
+    tasks: List[TaskRecord] = []
+    for item in raw_tasks:
+        if not isinstance(item, dict):
+            raise ValueError("each task entry must be a JSON object")
+        tasks.append(TaskRecord.from_dict(item))
+    return _normalize_tasks(tasks, holdout_fraction=holdout_fraction, seed=seed), meta

skillopt_sleep/types.py

@@ -135,6 +135,7 @@ class SleepReport:
     candidate_score: float = 0.0
     accepted: bool = False
     gate_action: str = ""
+    no_edits_reason: str = ""
     edits: List[EditRecord] = field(default_factory=list)
     rejected_edits: List[EditRecord] = field(default_factory=list)
     tokens_used: int = 0