Hawthorn bd45947941 feat(skills,agents): add agent-self-evaluation skill and agent-evaluator persona
Add structured 5-axis self-evaluation framework for agent output quality:
- Accuracy, Completeness, Clarity, Actionability, Conciseness
- Evidence-based scoring with concrete improvement suggestions
- Standalone Python evaluator script with keyword heuristics
- Detailed scoring anchors reference guide
- High-score and low-score annotated examples
- Reusable evaluation report template
- Optional hook integration for session-stop evaluation

Agent persona (agent-evaluator) provides a dedicated subagent
for applying the rubric to agent output with tool-backed verification.

All files tested: Python script runs, examples score correctly
(high 4.2, low 3.4), frontmatter parses clean, 183 lines (under 500).
2026-06-10 16:56:18 +05:30

372 lines
13 KiB
Python
Executable File

#!/usr/bin/env python3
"""Standalone agent output evaluator using the 5-axis rubric.
Reads a task description and agent output from stdin or files,
scores each axis, and prints a structured evaluation report.
Usage:
# Pipe output directly
echo "Task: Add retry logic" | evaluate.py --output response.txt
# From files
evaluate.py --task task.txt --output response.txt
# Interactive (reads task from prompt, output from stdin)
evaluate.py --interactive
The evaluator uses keyword heuristics + structural checks as a first pass.
For production use, pair with an LLM judge for semantic understanding.
"""
import argparse
import re
import sys
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class AxisScore:
name: str
score: int
evidence: list[str] = field(default_factory=list)
improvement: Optional[str] = None
def count_words(text: str) -> int:
return len(text.split())
def check_accuracy(text: str) -> AxisScore:
"""Check for verifiable claims, tool output references, error signs."""
evidence = []
deductions = 0
score = 5
# Positive signals: verified claims
verified_patterns = [
(r"(?i)(tests?\s+pass|all\s+tests?\s+passing|\d+\s+passed)", "Tests passing"),
(r"(?i)(exit\s+code\s*[:=]?\s*0|exited\s+with\s+0)", "Clean exit code"),
(r"(?i)(lint.*clean|no\s+lint\s+errors|0\s+errors)", "Lint clean"),
(r"(?i)(verified|confirmed|validated)\s+(with|against|using|by)", "Explicit verification"),
(r"(?i)(grep|rg)\s+.*\b(found|matched|returned)", "Grep confirmed"),
]
for pattern, label in verified_patterns:
if re.search(pattern, text):
evidence.append(f"+ {label}")
# Negative signals: unverified claims
danger_patterns = [
(r"(?i)(should\s+work|probably\s+fine|should\s+be\s+ok)", "Hedged claim without verification"),
(r"(?i)(I\s+think|I\s+believe|I\s+assume|might\s+be)", "Speculation without evidence"),
(r"(?i)(untested|not\s+tested|haven'?t\s+tested)", "Explicitly untested"),
(r"(?i)(TODO|FIXME|HACK|WORKAROUND)", "Unresolved TODO/FIXME"),
]
for pattern, label in danger_patterns:
if re.search(pattern, text):
deductions += 1
evidence.append(f"- {label}")
if deductions >= 3:
score = 2
elif deductions == 2:
score = 3
elif deductions == 1:
score = 4
if not evidence:
evidence.append("No verification signals detected — score assumes correctness")
result = AxisScore(name="Accuracy", score=score, evidence=evidence)
if score < 5:
result.improvement = "Cite specific tool outputs (test results, exit codes, grep findings) to back claims"
return result
def check_completeness(text: str, task: Optional[str] = None) -> AxisScore:
"""Check for requirement coverage, edge cases, error handling."""
evidence = []
score = 5
# Positive signals
completeness_signals = [
(r"(?i)(edge\s*cases?|corner\s*cases?)", "Edge cases addressed"),
(r"(?i)(error\s*handling|exception\s*handling|try/except|try\s*{)", "Error handling present"),
(r"(?i)(all\s+\w+\s+(methods|endpoints|routes))", "Full coverage claimed"),
(r"(?i)(verification|verified\s+that|confirmed\s+that)", "Verification step present"),
]
for pattern, label in completeness_signals:
if re.search(pattern, text):
evidence.append(f"+ {label}")
# Gaps
gap_signals = [
(r"(?i)(not\s+covered|not\s+handled|out\s+of\s+scope)", "Explicit gap acknowledged"),
(r"(?i)(only\s+(works|handles|supports)\s+\w+)", "Limited scope noted"),
(r"(?i)(assume[sd]?\s+that|assuming\s+the)", "Assumption without verification"),
]
deductions = 0
for pattern, label in gap_signals:
if re.search(pattern, text):
deductions += 1
evidence.append(f"- {label}")
if deductions >= 2:
score = 3
elif deductions == 1:
score = 4
if not evidence:
evidence.append("No completeness signals — unable to assess coverage")
result = AxisScore(name="Completeness", score=score, evidence=evidence)
if score < 5:
result.improvement = "List what was covered AND what was intentionally excluded, with reasoning"
return result
def check_clarity(text: str) -> AxisScore:
"""Check for structure, readability, jargon handling."""
evidence = []
score = 5
deductions = 0
# Positive signals
if re.search(r"^#{1,3}\s+", text, re.MULTILINE):
evidence.append("+ Uses headings for structure")
if re.search(r"```", text):
evidence.append("+ Uses code blocks")
if re.search(r"^\s*[-*]\s+", text, re.MULTILINE):
evidence.append("+ Uses bullet points")
# Negative signals
# Wall of text: long paragraph without breaks
paragraphs = [p for p in text.split("\n\n") if p.strip()]
for p in paragraphs:
if count_words(p) > 200:
deductions += 1
evidence.append("- Wall-of-text paragraph (>200 words without break)")
break
# Jargon without definition
jargon = [
(r"\b(idempotent|race condition|deadlock|thundering herd)\b", "concurrency"),
(r"\b(exponential backoff|circuit breaker|bulkhead)\b", "resilience"),
(r"\b(ACID|CAP|eventual consistency|linearizability)\b", "database theory"),
]
for pattern, domain in jargon:
if re.search(pattern, text, re.IGNORECASE):
if not re.search(rf"(?i)({domain}|means|refers to|i\.e\.|in other words)", text):
deductions += 1
evidence.append(f"- Domain term used without explanation ({domain})")
break
if not any(t in text[:100].lower() for t in ["summary", "tldr", "overview", "in short"]):
# No early summary — penalize only if text is long
if count_words(text) > 300:
deductions += 1
evidence.append("- No summary/TLDR in first 100 words (text is 300+ words)")
if deductions >= 3:
score = 2
elif deductions == 2:
score = 3
elif deductions == 1:
score = 4
if not evidence:
evidence.append("+ Well-structured with no clarity issues detected")
result = AxisScore(name="Clarity", score=score, evidence=evidence)
if score < 5:
result.improvement = "Add headings, break long paragraphs, define domain terms on first use"
return result
def check_actionability(text: str) -> AxisScore:
"""Check if the user can act on the output immediately."""
evidence = []
score = 5
deductions = 0
# Positive signals
actionable_signals = [
(r"(?i)(merge|PR|pull request).*?(created|ready|open)", "PR created"),
(r"(?i)(run|execute)\s+[`\"']?[\w./-]+", "Specific run command given"),
(r"(?i)(next\s+steps?|follow[- ]up|what\s+to\s+do)", "Next steps provided"),
(r"(?i)(file\s+(created|written|modified|updated)\s+at)", "File path specified"),
]
for pattern, label in actionable_signals:
if re.search(pattern, text):
evidence.append(f"+ {label}")
# Negative signals
vague_signals = [
(r"(?i)(you\s+(should|could|might\s+want\s+to))\s+\w+", "Vague suggestion without specifics"),
(r"(?i)(consider|maybe|perhaps)\s+\w+ing", "Non-committal suggestion"),
(r"(?i)(figure\s+out|look\s+into|investigate)\s", "Defers work to user"),
]
for pattern, label in vague_signals:
if re.search(pattern, text):
deductions += 1
evidence.append(f"- {label}")
if deductions >= 3:
score = 2
elif deductions == 2:
score = 3
elif deductions == 1:
score = 4
if not evidence:
evidence.append("No actionability signals — user may need to ask 'what now?'")
result = AxisScore(name="Actionability", score=score, evidence=evidence)
if score < 5:
result.improvement = "End with a single clear action: 'Merge this PR', 'Run ./deploy.sh', or 'Review the 3 changed files'"
return result
def check_concision(text: str, task: Optional[str] = None) -> AxisScore:
"""Check for redundancy, filler, information density."""
evidence = []
score = 5
wc = count_words(text)
# Heuristic: task-to-output ratio
if task:
task_wc = count_words(task)
ratio = wc / max(task_wc, 1)
if ratio > 15:
evidence.append(f"- Output is {ratio:.0f}x longer than task description (high ratio)")
score = min(score, 3)
elif ratio > 8:
evidence.append(f"- Output is {ratio:.0f}x longer than task description")
score = min(score, 4)
# Redundancy signals
redundancy_checks = [
(r"(?i)(as\s+(I|we)\s+(mentioned|said|noted|discussed)\s+(earlier|above|before))",
"Refers back to earlier statement (possible repetition)"),
(r"(?i)(to\s+summarize|in\s+summary|in\s+conclusion|to\s+conclude)",
"Has explicit summary (good if needed, flag if redundant)"),
(r"(?i)(let\s+me\s+(explain|break\s+this\s+down|walk\s+you\s+through))",
"Meta-commentary adds words without information"),
]
redundant_count = 0
for pattern, label in redundancy_checks:
matches = re.findall(pattern, text)
if len(matches) > 2:
redundant_count += 1
evidence.append(f"- '{label}' appears {len(matches)} times")
if redundant_count >= 2:
score = min(score, 3)
elif redundant_count == 1:
score = min(score, 4)
if not evidence and score == 5:
evidence.append("+ No redundancy detected. Information density appears good.")
result = AxisScore(name="Conciseness", score=score, evidence=evidence)
if score < 5:
result.improvement = "Cut meta-commentary, remove repeated points, trim examples to one representative case"
return result
def evaluate(task: Optional[str], output: str) -> list[AxisScore]:
"""Run all 5 axis checks and return scored results."""
return [
check_accuracy(output),
check_completeness(output, task),
check_clarity(output),
check_actionability(output),
check_concision(output, task),
]
def format_report(scores: list[AxisScore]) -> str:
"""Format scores into a readable evaluation report."""
avg = sum(s.score for s in scores) / len(scores)
lines = []
lines.append("=" * 60)
lines.append("AGENT SELF-EVALUATION REPORT")
lines.append("=" * 60)
lines.append("")
for s in scores:
bar = "" * s.score + "" * (5 - s.score)
lines.append(f" {s.name:<15} {bar} {s.score}/5")
for e in s.evidence:
lines.append(f" {e}")
if s.improvement:
lines.append(f"{s.improvement}")
lines.append("")
lines.append(f" {'OVERALL':<15} {avg:.1f}/5")
lines.append("")
# Top improvements
improvements = [(s, s.improvement) for s in scores if s.improvement and s.score < 4]
if improvements:
lines.append("TOP IMPROVEMENTS (axes scoring < 4):")
for s, imp in sorted(improvements, key=lambda x: x[0].score):
lines.append(f" [{s.name}] {imp}")
else:
lines.append("No axes below 4. Strong output across all dimensions.")
return "\n".join(lines)
def main():
parser = argparse.ArgumentParser(
description="Evaluate agent output against the 5-axis rubric"
)
parser.add_argument("--task", help="Task description (file path or inline text)")
parser.add_argument("--output", help="Agent output to evaluate (file path)")
parser.add_argument("--interactive", action="store_true", help="Prompt for task and read output from stdin")
args = parser.parse_args()
task = None
output = None
if args.interactive:
task = input("Task description: ").strip()
print("Paste agent output (Ctrl+D to finish):")
output = sys.stdin.read()
elif args.task and args.output:
# Read task
try:
with open(args.task) as f:
task = f.read()
except FileNotFoundError:
task = args.task # Treat as inline text
# Read output
try:
with open(args.output) as f:
output = f.read()
except FileNotFoundError:
print(f"Error: output file '{args.output}' not found", file=sys.stderr)
sys.exit(1)
else:
# Pipe mode: read output from stdin
output = sys.stdin.read()
if args.task:
try:
with open(args.task) as f:
task = f.read()
except FileNotFoundError:
task = args.task
if not output:
print("Error: no output to evaluate", file=sys.stderr)
sys.exit(1)
scores = evaluate(task, output)
print(format_report(scores))
if __name__ == "__main__":
main()