Coverage for little_loops/workflow_sequence_analyzer.py: 0%

1"""ll-workflows: Identify multi-step workflow patterns from user message history.

2

3 Identifies multi-step workflows and cross-session patterns using:

4- Entity-based clustering

5- Time-gap weighted boundaries

6- Semantic similarity scoring

7- Workflow template matching

8

9 Usage as CLI:

10 ll-workflows analyze --input messages.jsonl --patterns step1.yaml

11 ll-workflows analyze -i messages.jsonl -p patterns.yaml -o output.yaml

12

13 Usage as library:

14 from little_loops.workflow_sequence_analyzer import analyze_workflows

15

16 result = analyze_workflows(

17 messages_file=Path("user-messages.jsonl"),

18 patterns_file=Path("step1-patterns.yaml"),

19 output_file=Path("step2-workflows.yaml"),

20 )

21"""

22

23 from __future__ import annotations

24

25 import json

26 import re

27 import sys

28 from dataclasses import dataclass, field

29 from datetime import datetime

30 from pathlib import Path

31 from typing import Any

32

33 import yaml

34

35 __all__ = [

36 "analyze_workflows",

37 "SessionLink",

38 "EntityCluster",

39 "WorkflowBoundary",

40 "Workflow",

41 "WorkflowAnalysis",

42 "extract_entities",

43 "calculate_boundary_weight",

44 "entity_overlap",

45 "get_verb_class",

46 "semantic_similarity",

47]

48

49# Module-level compiled regex patterns

50 FILE_PATTERN = re.compile(r"[\w./-]+\.(?:md|py|json|yaml|yml|js|ts|tsx|jsx|sh|toml)", re.IGNORECASE)

51 PHASE_PATTERN = re.compile(r"phase[- ]?\d+", re.IGNORECASE)

52 MODULE_PATTERN = re.compile(r"module[- ]?\d+", re.IGNORECASE)

53 COMMAND_PATTERN = re.compile(r"/[\w:-]+")

54 ISSUE_PATTERN = re.compile(r"(?:BUG|FEAT|ENH)-\d+", re.IGNORECASE)

55

56# Verb class taxonomy for semantic similarity

57 VERB_CLASSES: dict[str, set[str]] = {

58 "deletion": {"remove", "delete", "drop", "eliminate", "clear", "clean"},

59 "modification": {"update", "change", "modify", "edit", "fix", "adjust", "revise"},

60 "creation": {"create", "add", "generate", "write", "make", "build"},

61 "search": {"find", "search", "locate", "where", "what", "which", "list"},

62 "verification": {"check", "verify", "validate", "confirm", "review", "ensure"},

63 "execution": {"run", "execute", "launch", "start", "invoke", "call"},

64}

65

66# Workflow templates: category sequences that indicate common patterns

67 WORKFLOW_TEMPLATES: dict[str, list[str]] = {

68 "explore → modify → verify": ["file_search", "code_modification", "testing"],

69 "create → refine → finalize": ["file_write", "code_modification", "git_operation"],

70 "review → fix → commit": ["code_review", "code_modification", "git_operation"],

71 "plan → implement → verify": ["planning", "code_modification", "testing"],

72 "debug → fix → test": ["debugging", "code_modification", "testing"],

73}

74

75# Maps content keywords to workflow category labels used by WORKFLOW_TEMPLATES

76 _CONTENT_CATEGORY_MAP: dict[str, list[str]] = {

77 "file_search": ["search", "find", "glob", "grep", "locate"],

78 "code_modification": ["edit", "write", "fix", "refactor", "update", "implement"],

79 "testing": ["test", "pytest", "assert", "verify", "check"],

80 "git_operation": ["commit", "push", "branch", "pr", "merge", "pull"],

81 "planning": ["plan", "design", "architect", "outline", "draft"],

82 "debugging": ["debug", "trace", "breakpoint", "error", "exception", "bug"],

83 "code_review": ["review", "inspect", "audit", "read", "examine"],

84 "file_write": ["create", "generate", "scaffold", "write", "add"],

85}

86

87

88# -----------------------------------------------------------------------------

89# Data Classes

90# -----------------------------------------------------------------------------

91

92

93@dataclass

94 class SessionLink:

95 """Link between related sessions."""

96

97 link_id: str

98 sessions: list[dict[str, Any]] # session_id, position, link_evidence

99 unified_workflow: dict[str, Any] # name, total_messages, span_hours

100 confidence: float

101

102 def to_dict(self) -> dict[str, Any]:

103 """Convert to dictionary for YAML serialization."""

104 return {

105 "link_id": self.link_id,

106 "sessions": self.sessions,

107 "unified_workflow": self.unified_workflow,

108 "confidence": self.confidence,

109 }

110

111

112@dataclass

113 class EntityCluster:

114 """Cluster of messages sharing entities."""

115

116 cluster_id: str

117 primary_entities: list[str]

118 all_entities: set[str] = field(default_factory=set)

119 messages: list[dict[str, Any]] = field(default_factory=list)

120 span: dict[str, Any] | None = None

121 inferred_workflow: str | None = None

122 cohesion_score: float = 0.0

123

124 def to_dict(self) -> dict[str, Any]:

125 """Convert to dictionary for YAML serialization."""

126 return {

127 "cluster_id": self.cluster_id,

128 "primary_entities": self.primary_entities,

129 "all_entities": sorted(self.all_entities),

130 "messages": self.messages,

131 "span": self.span,

132 "inferred_workflow": self.inferred_workflow,

133 "cohesion_score": round(self.cohesion_score, 2),

134 }

135

136

137@dataclass

138 class WorkflowBoundary:

139 """Boundary between workflows based on time gaps and entity overlap."""

140

141 msg_a: str

142 msg_b: str

143 time_gap_seconds: int

144 time_gap_weight: float

145 entity_overlap: float

146 final_boundary_score: float

147 is_boundary: bool

148

149 def to_dict(self) -> dict[str, Any]:

150 """Convert to dictionary for YAML serialization."""

151 return {

152 "between": {"msg_a": self.msg_a, "msg_b": self.msg_b},

153 "time_gap_seconds": self.time_gap_seconds,

154 "time_gap_weight": round(self.time_gap_weight, 2),

155 "entity_overlap": round(self.entity_overlap, 2),

156 "final_boundary_score": round(self.final_boundary_score, 2),

157 "is_boundary": self.is_boundary,

158 }

159

160

161@dataclass

162 class Workflow:

163 """Identified multi-step workflow."""

164

165 workflow_id: str

166 name: str

167 pattern: str

168 pattern_confidence: float

169 messages: list[dict[str, Any]]

170 session_span: list[str]

171 entity_cluster: str | None = None

172 semantic_cluster: str | None = None

173 duration_minutes: int = 0

174 handoff_points: list[dict[str, Any]] = field(default_factory=list)

175

176 def to_dict(self) -> dict[str, Any]:

177 """Convert to dictionary for YAML serialization."""

178 return {

179 "workflow_id": self.workflow_id,

180 "name": self.name,

181 "pattern": self.pattern,

182 "pattern_confidence": round(self.pattern_confidence, 2),

183 "messages": self.messages,

184 "session_span": self.session_span,

185 "entity_cluster": self.entity_cluster,

186 "semantic_cluster": self.semantic_cluster,

187 "duration_minutes": self.duration_minutes,

188 "handoff_points": self.handoff_points,

189 }

190

191

192@dataclass

193 class WorkflowAnalysis:

194 """Complete workflow analysis output."""

195

196 metadata: dict[str, Any]

197 session_links: list[SessionLink] = field(default_factory=list)

198 entity_clusters: list[EntityCluster] = field(default_factory=list)

199 workflow_boundaries: list[WorkflowBoundary] = field(default_factory=list)

200 workflows: list[Workflow] = field(default_factory=list)

201 handoff_analysis: dict[str, Any] | None = None

202

203 def to_dict(self) -> dict[str, Any]:

204 """Convert to dictionary for YAML serialization."""

205 return {

206 "analysis_metadata": self.metadata,

207 "session_links": [s.to_dict() for s in self.session_links],

208 "entity_clusters": [c.to_dict() for c in self.entity_clusters],

209 "workflow_boundaries": [b.to_dict() for b in self.workflow_boundaries],

210 "workflows": [w.to_dict() for w in self.workflows],

211 "handoff_analysis": self.handoff_analysis,

212 }

213

214

215# -----------------------------------------------------------------------------

216# Core Analysis Functions

217# -----------------------------------------------------------------------------

218

219

220 def extract_entities(content: str) -> set[str]:

221 """Extract file paths, commands, and concepts from message content.

222

223 Args:

224 content: Message text content

225

226 Returns:

227 Set of extracted entities (file paths, commands, issue IDs, etc.)

228 """

229 entities: set[str] = set()

230

231 # File paths

232 entities.update(FILE_PATTERN.findall(content))

233

234 # Phase/module references

235 entities.update(PHASE_PATTERN.findall(content.lower()))

236 entities.update(MODULE_PATTERN.findall(content.lower()))

237

238 # Slash commands

239 entities.update(COMMAND_PATTERN.findall(content))

240

241 # Issue IDs (normalize to uppercase)

242 entities.update(match.upper() for match in ISSUE_PATTERN.findall(content))

243

244 return entities

245

246

247 def calculate_boundary_weight(gap_seconds: int) -> float:

248 """Calculate workflow boundary weight based on time gap.

249

250 Args:

251 gap_seconds: Time gap between messages in seconds

252

253 Returns:

254 Boundary weight from 0.0 to 0.95

255 """

256 if gap_seconds < 30:

257 return 0.0

258 elif gap_seconds < 120:

259 return 0.1

260 elif gap_seconds < 300:

261 return 0.3

262 elif gap_seconds < 900:

263 return 0.5

264 elif gap_seconds < 1800:

265 return 0.7

266 elif gap_seconds < 7200:

267 return 0.85

268 else:

269 return 0.95

270

271

272 def entity_overlap(entities_a: set[str], entities_b: set[str]) -> float:

273 """Calculate Jaccard similarity between two entity sets.

274

275 Args:

276 entities_a: First set of entities

277 entities_b: Second set of entities

278

279 Returns:

280 Jaccard similarity coefficient (0.0 to 1.0)

281 """

282 if not entities_a or not entities_b:

283 return 0.0

284 intersection = len(entities_a & entities_b)

285 union = len(entities_a | entities_b)

286 return intersection / union if union > 0 else 0.0

287

288

289 def get_verb_class(content: str) -> str | None:

290 """Extract verb class from message content.

291

292 Args:

293 content: Message text content

294

295 Returns:

296 Verb class name or None if no match

297 """

298 content_lower = content.lower()

299 words = set(re.findall(r"\b\w+\b", content_lower))

300

301 for verb_class, verbs in VERB_CLASSES.items():

302 if words & verbs:

303 return verb_class

304 return None

305

306

307 def semantic_similarity(

308 content_a: str,

309 content_b: str,

310 entities_a: set[str],

311 entities_b: set[str],

312 category_a: str | None,

313 category_b: str | None,

314) -> float:

315 """Calculate semantic similarity between two messages.

316

317 Uses weighted combination of:

318 - Keyword overlap (0.3)

319 - Verb class match (0.3)

320 - Entity overlap (0.3)

321 - Category match (0.1)

322

323 Args:

324 content_a: First message content

325 content_b: Second message content

326 entities_a: Entities from first message

327 entities_b: Entities from second message

328 category_a: Category of first message

329 category_b: Category of second message

330

331 Returns:

332 Similarity score (0.0 to 1.0)

333 """

334 # Keyword overlap (simple word-level Jaccard)

335 words_a = set(re.findall(r"\b[a-z]{3,}\b", content_a.lower()))

336 words_b = set(re.findall(r"\b[a-z]{3,}\b", content_b.lower()))

337 keyword_sim = len(words_a & words_b) / len(words_a | words_b) if words_a | words_b else 0.0

338

339 # Verb class similarity

340 verb_a = get_verb_class(content_a)

341 verb_b = get_verb_class(content_b)

342 verb_sim = 1.0 if verb_a and verb_a == verb_b else 0.0

343

344 # Entity overlap

345 entity_sim = entity_overlap(entities_a, entities_b)

346

347 # Category match

348 category_sim = 1.0 if category_a and category_a == category_b else 0.0

349

350 # Weighted combination

351 return keyword_sim * 0.3 + verb_sim * 0.3 + entity_sim * 0.3 + category_sim * 0.1

352

353

354# -----------------------------------------------------------------------------

355# Internal Analysis Functions

356# -----------------------------------------------------------------------------

357

358

359 def _load_messages(messages_file: Path) -> list[dict[str, Any]]:

360 """Load messages from JSONL file."""

361 messages = []

362 skipped = 0

363 with open(messages_file, encoding="utf-8") as f:

364 for line_num, raw_line in enumerate(f, 1):

365 line = raw_line.strip()

366 if not line:

367 continue

368 try:

369 messages.append(json.loads(line))

370 except json.JSONDecodeError as e:

371 skipped += 1

372 print(f"Warning: skipping malformed line {line_num}: {e}", file=sys.stderr)

373 if skipped:

374 print(f"Warning: skipped {skipped} malformed line(s) in {messages_file}", file=sys.stderr)

375 return messages

376

377

378 def _load_patterns(patterns_file: Path) -> dict[str, Any]:

379 """Load patterns from Step 1 YAML output."""

380 with open(patterns_file, encoding="utf-8") as f:

381 return yaml.safe_load(f) or {}

382

383

384 def _group_by_session(messages: list[dict[str, Any]]) -> dict[str, list[dict[str, Any]]]:

385 """Group messages by session_id."""

386 sessions: dict[str, list[dict[str, Any]]] = {}

387 for msg in messages:

388 session_id = msg.get("session_id", "unknown")

389 if session_id not in sessions:

390 sessions[session_id] = []

391 sessions[session_id].append(msg)

392 return sessions

393

394

395 def _detect_handoff(content: str) -> bool:

396 """Check if message indicates a session handoff."""

397 handoff_markers = [

398 "/ll:handoff",

399 "continue in new session",

400 "pick up in next session",

401 "resuming from",

402 "continuation of",

403 ]

404 content_lower = content.lower()

405 return any(marker in content_lower for marker in handoff_markers)

406

407

408 def _parse_timestamps(messages: list[dict[str, Any]]) -> list[datetime]:

409 """Parse valid ISO timestamps from a list of messages, stripping timezone info."""

410 timestamps = []

411 for msg in messages:

412 ts_str = msg.get("timestamp", "")

413 if ts_str:

414 try:

415 ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))

416 if ts.tzinfo is not None:

417 ts = ts.replace(tzinfo=None)

418 timestamps.append(ts)

419 except (ValueError, AttributeError, TypeError):

420 pass

421 return timestamps

422

423

424 def _link_sessions(sessions: dict[str, list[dict[str, Any]]]) -> list[SessionLink]:

425 """Identify sessions that are part of the same workflow."""

426 links: list[SessionLink] = []

427 session_ids = list(sessions.keys())

428 link_counter = 0

429

430 for i, session_a_id in enumerate(session_ids):

431 session_a = sessions[session_a_id]

432 if not session_a:

433 continue

434

435 # Extract session metadata

436 last_msg_a = session_a[-1] if session_a else {}

437 entities_a: set[str] = set()

438 for msg in session_a:

439 entities_a.update(extract_entities(msg.get("content", "")))

440 branch_a = last_msg_a.get("git_branch")

441

442 for session_b_id in session_ids[i + 1 :]:

443 session_b = sessions[session_b_id]

444 if not session_b:

445 continue

446

447 first_msg_b = session_b[0] if session_b else {}

448 entities_b: set[str] = set()

449 for msg in session_b:

450 entities_b.update(extract_entities(msg.get("content", "")))

451 branch_b = first_msg_b.get("git_branch")

452

453 # Calculate link score

454 score = 0.0

455 evidence: list[str] = []

456

457 # Same git branch (HIGH weight)

458 if branch_a and branch_a == branch_b:

459 score += 0.4

460 evidence.append("shared_branch")

461

462 # Explicit handoff marker (HIGH weight)

463 if any(_detect_handoff(msg.get("content", "")) for msg in session_a):

464 score += 0.4

465 evidence.append("handoff_detected")

466

467 # Shared entities (MEDIUM weight)

468 overlap = entity_overlap(entities_a, entities_b)

469 if overlap > 0.5:

470 score += 0.2

471 evidence.append("entity_overlap")

472 elif overlap > 0.3:

473 score += 0.1

474 evidence.append("partial_entity_overlap")

475

476 if score > 0.3:

477 link_counter += 1

478

479 # Calculate span

480 timestamps = _parse_timestamps(session_a + session_b)

481

482 span_hours = 0.0

483 if len(timestamps) >= 2:

484 try:

485 span_hours = (max(timestamps) - min(timestamps)).total_seconds() / 3600

486 except TypeError:

487 span_hours = 0.0

488

489 links.append(

490 SessionLink(

491 link_id=f"link-{link_counter:03d}",

492 sessions=[

493 {

494 "session_id": session_a_id,

495 "position": 1,

496 "link_evidence": evidence[0] if evidence else "score",

497 },

498 {

499 "session_id": session_b_id,

500 "position": 2,

501 "link_evidence": evidence[-1] if evidence else "score",

502 },

503 ],

504 unified_workflow={

505 "name": f"Linked workflow {link_counter}",

506 "total_messages": len(session_a) + len(session_b),

507 "span_hours": round(span_hours, 1),

508 "evidence": evidence,

509 },

510 confidence=min(score, 1.0),

511 )

512 )

513

514 return links

515

516

517 def _cluster_by_entities(

518 messages: list[dict[str, Any]], overlap_threshold: float = 0.3

519) -> list[EntityCluster]:

520 """Cluster messages with significant entity overlap."""

521 clusters: list[EntityCluster] = []

522 cluster_counter = 0

523

524 for msg in messages:

525 content = msg.get("content", "")

526 msg_entities = extract_entities(content)

527

528 if not msg_entities:

529 continue

530

531 # Find matching cluster

532 matched_cluster = None

533 best_overlap = overlap_threshold

534

535 for cluster in clusters:

536 overlap = entity_overlap(msg_entities, cluster.all_entities)

537 if overlap > best_overlap:

538 best_overlap = overlap

539 matched_cluster = cluster

540

541 if matched_cluster:

542 entities_matched = sorted(msg_entities & matched_cluster.all_entities)

543 matched_cluster.all_entities.update(msg_entities)

544 matched_cluster.messages.append(

545 {

546 "uuid": msg.get("uuid", ""),

547 "content": content[:80] + "..." if len(content) > 80 else content,

548 "entities_matched": entities_matched,

549 "timestamp": msg.get("timestamp"),

550 }

551 )

552 # Update cohesion score (average overlap of messages)

553 matched_cluster.cohesion_score = (

554 matched_cluster.cohesion_score * (len(matched_cluster.messages) - 1) + best_overlap

555 ) / len(matched_cluster.messages)

556 else:

557 cluster_counter += 1

558 # Create new cluster

559 primary = sorted(msg_entities)[:3] # Top 3 entities

560 cluster = EntityCluster(

561 cluster_id=f"cluster-{cluster_counter:03d}",

562 primary_entities=primary,

563 all_entities=msg_entities.copy(),

564 messages=[

565 {

566 "uuid": msg.get("uuid", ""),

567 "content": content[:80] + "..." if len(content) > 80 else content,

568 "entities_matched": sorted(msg_entities),

569 "timestamp": msg.get("timestamp"),

570 }

571 ],

572 cohesion_score=1.0,

573 )

574 clusters.append(cluster)

575

576 # Populate span and inferred_workflow for each multi-message cluster

577 for cluster in clusters:

578 # Compute span from timestamps

579 timestamps = _parse_timestamps(cluster.messages)

580 if len(timestamps) >= 2:

581 cluster.span = {

582 "start": min(timestamps).isoformat(),

583 "end": max(timestamps).isoformat(),

584 }

585

586 # Infer workflow by matching cluster message content against WORKFLOW_TEMPLATES

587 cluster_categories: set[str] = set()

588 for m in cluster.messages:

589 lower = m.get("content", "").lower()

590 for category, keywords in _CONTENT_CATEGORY_MAP.items():

591 if any(kw in lower for kw in keywords):

592 cluster_categories.add(category)

593

594 best_name: str | None = None

595 best_score = 0.0

596 for template_name, template_cats in WORKFLOW_TEMPLATES.items():

597 template_set = set(template_cats)

598 if template_set:

599 overlap = len(cluster_categories & template_set) / len(template_set)

600 if overlap > best_score:

601 best_score = overlap

602 best_name = template_name

603 if best_score >= 0.3:

604 cluster.inferred_workflow = best_name

605

606 # Filter out single-message clusters

607 return [c for c in clusters if len(c.messages) >= 2]

608

609

610 def _compute_boundaries(

611 messages: list[dict[str, Any]], boundary_threshold: float = 0.6

612) -> list[WorkflowBoundary]:

613 """Compute workflow boundaries between consecutive messages."""

614 boundaries: list[WorkflowBoundary] = []

615

616 # Sort by timestamp

617 sorted_msgs = sorted(messages, key=lambda m: m.get("timestamp", ""))

618

619 for i in range(len(sorted_msgs) - 1):

620 msg_a = sorted_msgs[i]

621 msg_b = sorted_msgs[i + 1]

622

623 # Parse timestamps

624 pair_timestamps = _parse_timestamps([msg_a, msg_b])

625 if len(pair_timestamps) == 2:

626 gap_seconds = int((pair_timestamps[1] - pair_timestamps[0]).total_seconds())

627 else:

628 gap_seconds = 0

629

630 # Calculate time gap weight

631 time_weight = calculate_boundary_weight(gap_seconds)

632

633 # Calculate entity overlap

634 entities_a = extract_entities(msg_a.get("content", ""))

635 entities_b = extract_entities(msg_b.get("content", ""))

636 overlap = entity_overlap(entities_a, entities_b)

637

638 # Adjust for entity overlap (reduce boundary weight if same topic)

639 final_score = time_weight

640 if overlap > 0.5:

641 final_score = max(0.0, time_weight - 0.3)

642 elif overlap > 0.3:

643 final_score = max(0.0, time_weight - 0.15)

644

645 is_boundary = final_score >= boundary_threshold

646

647 boundaries.append(

648 WorkflowBoundary(

649 msg_a=msg_a.get("uuid", ""),

650 msg_b=msg_b.get("uuid", ""),

651 time_gap_seconds=gap_seconds,

652 time_gap_weight=time_weight,

653 entity_overlap=overlap,

654 final_boundary_score=final_score,

655 is_boundary=is_boundary,

656 )

657 )

658

659 return boundaries

660

661

662 def _get_message_category(msg_uuid: str, patterns: dict[str, Any]) -> str | None:

663 """Look up message category from Step 1 patterns."""

664 for category_info in patterns.get("category_distribution", []):

665 for example in category_info.get("example_messages", []):

666 if example.get("uuid") == msg_uuid:

667 category = category_info.get("category")

668 return category if isinstance(category, str) else None

669 return None

670

671

672 def _detect_workflows(

673 messages: list[dict[str, Any]],

674 boundaries: list[WorkflowBoundary],

675 patterns: dict[str, Any],

676) -> list[Workflow]:

677 """Detect multi-step workflows using template matching."""

678 workflows: list[Workflow] = []

679 workflow_counter = 0

680

681 # Sort messages by timestamp

682 sorted_msgs = sorted(messages, key=lambda m: m.get("timestamp", ""))

683

684 # Build boundary index (msg_b uuid -> is_boundary)

685 boundary_before: dict[str, bool] = {}

686 for b in boundaries:

687 boundary_before[b.msg_b] = b.is_boundary

688

689 # Segment messages by boundaries

690 segments: list[list[dict[str, Any]]] = []

691 current_segment: list[dict[str, Any]] = []

692

693 for msg in sorted_msgs:

694 uuid = msg.get("uuid", "")

695 if boundary_before.get(uuid, False) and current_segment:

696 segments.append(current_segment)

697 current_segment = []

698 current_segment.append(msg)

699

700 if current_segment:

701 segments.append(current_segment)

702

703 # Match each segment against workflow templates

704 for segment in segments:

705 if len(segment) < 2:

706 continue

707

708 # Get categories for segment messages (from patterns)

709 segment_categories: list[str] = []

710 for msg in segment:

711 cat = _get_message_category(msg.get("uuid", ""), patterns)

712 if cat:

713 segment_categories.append(cat)

714

715 if len(segment_categories) < 2:

716 continue

717

718 # Find best matching template

719 best_match: tuple[str, float] | None = None

720

721 for template_name, template_cats in WORKFLOW_TEMPLATES.items():

722 # Check if template categories appear in sequence (allowing gaps)

723 template_idx = 0

724 matches = 0

725

726 for cat in segment_categories:

727 if template_idx < len(template_cats) and cat == template_cats[template_idx]:

728 matches += 1

729 template_idx += 1

730

731 if matches >= 2: # At least 2 template steps matched

732 confidence = matches / len(template_cats)

733 if best_match is None or confidence > best_match[1]:

734 best_match = (template_name, confidence)

735

736 if best_match:

737 workflow_counter += 1

738

739 # Calculate duration

740 timestamps = _parse_timestamps(segment)

741

742 duration_minutes = 0

743 if len(timestamps) >= 2:

744 try:

745 duration_minutes = int((max(timestamps) - min(timestamps)).total_seconds() / 60)

746 except TypeError:

747 duration_minutes = 0

748

749 # Get sessions

750 session_ids = list({msg.get("session_id", "") for msg in segment})

751

752 workflows.append(

753 Workflow(

754 workflow_id=f"wf-{workflow_counter:03d}",

755 name=f"Detected: {best_match[0]}",

756 pattern=best_match[0],

757 pattern_confidence=best_match[1],

758 messages=[

759 {

760 "uuid": msg.get("uuid", ""),

761 "category": _get_message_category(msg.get("uuid", ""), patterns),

762 "step": i + 1,

763 }

764 for i, msg in enumerate(segment)

765 ],

766 session_span=session_ids,

767 duration_minutes=duration_minutes,

768 )

769 )

770

771 return workflows

772

773

774# -----------------------------------------------------------------------------

775# Main API

776# -----------------------------------------------------------------------------

777

778

779 def analyze_workflows(

780 messages_file: Path,

781 patterns_file: Path,

782 output_file: Path | None = None,

783 overlap_threshold: float = 0.3,

784 boundary_threshold: float = 0.6,

785 verbose: bool = False,

786 output_format: str = "yaml",

787) -> WorkflowAnalysis:

788 """Main entry point: analyze workflows from messages and patterns.

789

790 Args:

791 messages_file: Path to JSONL file with extracted user messages

792 patterns_file: Path to YAML file from Step 1 (workflow-pattern-analyzer)

793 output_file: Output path for step2-workflows.yaml (optional)

794 overlap_threshold: Minimum entity overlap to cluster messages together (default: 0.3)

795 boundary_threshold: Minimum boundary score to split workflow segments (default: 0.6)

796 verbose: Emit per-stage progress to stderr (default: False)

797 output_format: Output serialization format, "yaml" or "json" (default: "yaml")

798

799 Returns:

800 WorkflowAnalysis with all analysis results

801 """

802 # Load inputs

803 messages = _load_messages(messages_file)

804 patterns = _load_patterns(patterns_file)

805

806 # Build metadata

807 metadata = {

808 "source_file": messages_file.name,

809 "patterns_file": patterns_file.name,

810 "message_count": len(messages),

811 "analysis_timestamp": datetime.now().isoformat(),

812 "module": "workflow-sequence-analyzer",

813 "version": "1.0",

814 }

815

816 # Run analysis pipeline

817 sessions = _group_by_session(messages)

818 if verbose:

819 print(f"[1/4] Linking sessions across {len(sessions)} session(s)...", file=sys.stderr)

820 session_links = _link_sessions(sessions)

821 if verbose:

822 print(f" → {len(session_links)} link(s) found", file=sys.stderr)

823 if verbose:

824 print("[2/4] Clustering by entities...", file=sys.stderr)

825 entity_clusters = _cluster_by_entities(messages, overlap_threshold=overlap_threshold)

826 if verbose:

827 print(f" → {len(entity_clusters)} cluster(s) found", file=sys.stderr)

828 if verbose:

829 print("[3/4] Computing workflow boundaries...", file=sys.stderr)

830 boundaries = _compute_boundaries(messages, boundary_threshold=boundary_threshold)

831 if verbose:

832 print(f" → {len(boundaries)} boundary/boundaries found", file=sys.stderr)

833 if verbose:

834 print("[4/4] Detecting workflows...", file=sys.stderr)

835 workflows = _detect_workflows(messages, boundaries, patterns)

836 if verbose:

837 print(f" → {len(workflows)} workflow(s) detected", file=sys.stderr)

838

839 # Cross-reference: link workflows to entity clusters and populate handoff_points

840 uuid_to_cluster: dict[str, str] = {}

841 for cluster in entity_clusters:

842 for msg in cluster.messages:

843 uuid = msg.get("uuid", "")

844 if uuid:

845 uuid_to_cluster[uuid] = cluster.cluster_id

846

847 uuid_to_content: dict[str, str] = {

848 m.get("uuid", ""): m.get("content", "") for m in messages if m.get("uuid", "")

849 }

850

851 for workflow in workflows:

852 cluster_votes: dict[str, int] = {}

853 for msg in workflow.messages:

854 cluster_id = uuid_to_cluster.get(msg.get("uuid", ""))

855 if cluster_id:

856 cluster_votes[cluster_id] = cluster_votes.get(cluster_id, 0) + 1

857 if cluster_votes:

858 workflow.entity_cluster = max(cluster_votes, key=cluster_votes.__getitem__)

859

860 for msg in workflow.messages:

861 uuid = msg.get("uuid", "")

862 if uuid and _detect_handoff(uuid_to_content.get(uuid, "")):

863 workflow.handoff_points.append({"uuid": uuid, "type": "explicit_handoff"})

864

865 # Compute handoff analysis

866 handoff_count = sum(

867 1

868 for link in session_links

869 if "handoff_detected" in link.unified_workflow.get("evidence", [])

870 )

871

872 handoff_analysis: dict[str, Any] = {

873 "total_handoffs": handoff_count,

874 "handoff_patterns": [

875 {"pattern": "explicit_handoff", "count": handoff_count},

876 {"pattern": "session_timeout", "count": len(session_links) - handoff_count},

877 ],

878 "recommendations": [],

879 }

880

881 if len(session_links) > handoff_count:

882 handoff_analysis["recommendations"].append(

883 "Consider using /ll:handoff for cleaner session transitions"

884 )

885

886 # Build result

887 analysis = WorkflowAnalysis(

888 metadata=metadata,

889 session_links=session_links,

890 entity_clusters=entity_clusters,

891 workflow_boundaries=boundaries,

892 workflows=workflows,

893 handoff_analysis=handoff_analysis,

894 )

895

896 # Write output if path provided

897 if output_file:

898 output_file = Path(output_file)

899 output_file.parent.mkdir(parents=True, exist_ok=True)

900 with open(output_file, "w", encoding="utf-8") as f:

901 if output_format == "json":

902 json.dump(analysis.to_dict(), f, indent=2, default=str)

903 else:

904 yaml.dump(analysis.to_dict(), f, default_flow_style=False, sort_keys=False)

905

906 return analysis

907

908

909# -----------------------------------------------------------------------------

910# CLI

911# -----------------------------------------------------------------------------

912

913

914 def main() -> int:

915 """Entry point for ll-workflows command.

916

917 Analyze workflows from user messages and Step 1 patterns.

918

919 Returns:

920 Exit code (0 = success, 1 = failure)

921 """

922 import argparse

923 import sys

924

925 parser = argparse.ArgumentParser(

926 description="Identify multi-step workflow patterns from user message history",

927 formatter_class=argparse.RawDescriptionHelpFormatter,

928 epilog="""

929 Examples:

930 %(prog)s analyze --input messages.jsonl --patterns step1.yaml

931 %(prog)s analyze -i messages.jsonl -p patterns.yaml -o output.yaml

932 %(prog)s analyze --input .claude/user-messages.jsonl \\

933 --patterns .claude/workflow-analysis/step1-patterns.yaml

934""",

935 )

936

937 subparsers = parser.add_subparsers(dest="command", help="Available commands")

938

939 # analyze subcommand

940 analyze_parser = subparsers.add_parser(

941 "analyze",

942 help="Analyze workflows from messages and patterns",

943 )

944 analyze_parser.add_argument(

945 "-i",

946 "--input",

947 type=Path,

948 required=True,

949 help="Input JSONL file with user messages",

950 )

951 analyze_parser.add_argument(

952 "-p",

953 "--patterns",

954 type=Path,

955 required=True,

956 help="Input YAML file from Step 1 (workflow-pattern-analyzer)",

957 )

958 analyze_parser.add_argument(

959 "-o",

960 "--output",

961 type=Path,

962 default=None,

963 help="Output file (default: .claude/workflow-analysis/step2-workflows.yaml or .json)",

964 )

965 analyze_parser.add_argument(

966 "-f",

967 "--format",

968 choices=["yaml", "json"],

969 default="yaml",

970 help="Output format (default: yaml)",

971 )

972 analyze_parser.add_argument(

973 "-v",

974 "--verbose",

975 action="store_true",

976 help="Print verbose progress information",

977 )

978 analyze_parser.add_argument(

979 "--overlap-threshold",

980 type=float,

981 default=0.3,

982 metavar="FLOAT",

983 help="Minimum entity overlap to cluster messages together (default: 0.3)",

984 )

985 analyze_parser.add_argument(

986 "--boundary-threshold",

987 type=float,

988 default=0.6,

989 metavar="FLOAT",

990 help="Minimum boundary score to split workflow segments (default: 0.6)",

991 )

992

993 args = parser.parse_args()

994

995 if args.command is None:

996 parser.print_help()

997 return 1

998

999 if args.command == "analyze":

1000 # Validate input files

1001 if not args.input.exists():

1002 print(f"Error: Input file not found: {args.input}", file=sys.stderr)

1003 return 1

1004

1005 if not args.patterns.exists():

1006 print(f"Error: Patterns file not found: {args.patterns}", file=sys.stderr)

1007 return 1

1008

1009 # Validate threshold ranges

1010 if not (0.0 <= args.overlap_threshold <= 1.0):

1011 print(

1012 f"Error: --overlap-threshold must be in [0.0, 1.0], got {args.overlap_threshold}",

1013 file=sys.stderr,

1014 )

1015 return 1

1016

1017 if not (0.0 <= args.boundary_threshold <= 1.0):

1018 print(

1019 f"Error: --boundary-threshold must be in [0.0, 1.0], got {args.boundary_threshold}",

1020 file=sys.stderr,

1021 )

1022 return 1

1023

1024 # Set default output path

1025 output_path = args.output

1026 if output_path is None:

1027 if args.format == "json":

1028 output_path = Path(".claude/workflow-analysis/step2-workflows.json")

1029 else:

1030 output_path = Path(".claude/workflow-analysis/step2-workflows.yaml")

1031

1032 if args.verbose:

1033 print(f"Input: {args.input}")

1034 print(f"Patterns: {args.patterns}")

1035 print(f"Output: {output_path}")

1036

1037 try:

1038 analysis = analyze_workflows(

1039 messages_file=args.input,

1040 patterns_file=args.patterns,

1041 output_file=output_path,

1042 overlap_threshold=args.overlap_threshold,

1043 boundary_threshold=args.boundary_threshold,

1044 verbose=args.verbose,

1045 output_format=args.format,

1046 )

1047

1048 if args.verbose:

1049 print(f"Analyzed {analysis.metadata['message_count']} messages")

1050 print(f"Found {len(analysis.session_links)} session links")

1051 print(f"Found {len(analysis.entity_clusters)} entity clusters")

1052 print(f"Detected {len(analysis.workflows)} workflows")

1053 print(f"Output written to: {output_path}")

1054

1055 return 0

1056

1057 except Exception as e:

1058 print(f"Error: {e}", file=sys.stderr)

1059 return 1

1060

1061 return 1

1062

1063

1064 if __name__ == "__main__":

1065 raise SystemExit(main())