204e071b74
Add 3-verdict system (PASS/FAIL/ESCALATE) with priority handling across simple and phased pipelines. Senior reviewers can now escalate issues requiring human intervention, immediately breaking the review loop. - ESCALATE verdict extraction with highest priority over PASS/FAIL - Issue Tracker tables (ISS-NNN) carried across iterations - Auto-escalate heuristic using (file, keyword) composite fingerprints - Report restructuring: executive view first (verdict → tracker → metrics) - Onboarding: `doctor`, `demo`, `init --guided` commands - Exit codes: PASS=0, FAIL=1, ESCALATE=2 - 87 tests passing (54 config + 25 onboarding + 8 integration) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
121 lines
3.3 KiB
Python
121 lines
3.3 KiB
Python
"""Data models for cross-eval pipeline."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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@dataclass
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class AgentConfig:
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"""Definition of a single agent."""
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name: str
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command: str
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args: list[str] = field(default_factory=list)
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system_prompt: Optional[str] = None
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reasoning_effort: Optional[str] = None
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stdin_mode: bool = False
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@dataclass
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class StepConfig:
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"""One step in the pipeline."""
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name: str
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agent: str # reference to agents key
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role: str # "coding" or "review"
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prompt_template: str # "default:<role>" or file path
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output_key: str
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verdict: bool = False
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verdict_pattern: str = r"VERDICT:\s*PASS"
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context_override: dict[str, str] = field(default_factory=dict)
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parallel: bool = False # Can run concurrently with adjacent parallel steps
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@dataclass
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class PhaseConfig:
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"""One phase in a multi-phase pipeline (e.g. review-fix)."""
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name: str
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steps: list[StepConfig] = field(default_factory=list)
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max_iterations: int = 10
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consecutive_pass: int = 1 # stop after N consecutive PASSes
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@dataclass
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class PipelineConfig:
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"""Full cross-eval configuration."""
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output_dir: Path = field(default_factory=lambda: Path("output"))
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max_iterations: int = 3
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min_iterations: int = 1
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verbose: bool = False
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language: str = "en" # "en" or "ko"
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inputs: dict[str, Path | str] = field(default_factory=dict)
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agents: dict[str, AgentConfig] = field(default_factory=dict)
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coders: list[str] = field(default_factory=list)
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reviewers: list[str] = field(default_factory=list)
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seniors: list[str] = field(default_factory=list)
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pipeline: list[StepConfig] = field(default_factory=list)
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phases: list[PhaseConfig] = field(default_factory=list)
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preset_name: str = "custom"
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_config_path: Optional[Path] = field(default=None, repr=False)
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_config_mtime: Optional[float] = field(default=None, repr=False)
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@dataclass
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class AgentResult:
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"""Result from an agent invocation."""
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output: str
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exit_code: int
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agent_name: str
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step_name: str
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duration_seconds: float
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@dataclass
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class ReviewMetrics:
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"""Parsed metrics from a single review output."""
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# Severity counts
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critical: int = 0
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major: int = 0
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minor: int = 0
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# Category counts
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over_engineering: int = 0
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omission: int = 0
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# Assessment counts
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confirmed: int = 0
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dismissed: int = 0
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@dataclass
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class IterationResult:
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"""Results from a single iteration."""
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iteration: int
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step_results: dict[str, AgentResult] = field(default_factory=dict)
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step_outputs: dict[str, str] = field(default_factory=dict)
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verdict: Optional[str] = None
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feedback: Optional[str] = None
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phase_name: Optional[str] = None
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repeated_aggregate_warning: Optional[str] = None
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review_metrics: Optional[ReviewMetrics] = None
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escalated_issues: Optional[str] = None
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@dataclass
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class PipelineResult:
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"""Results from the entire pipeline run."""
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iterations: list[IterationResult] = field(default_factory=list)
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final_verdict: str = "MAX_ITERATIONS_REACHED"
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total_duration: float = 0.0
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run_dir: Optional[Path] = None
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repeated_aggregate_warnings: list[str] = field(default_factory=list)
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escalated_issues: list[str] = field(default_factory=list)
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