Files
ai-workflow-course/modules/24-assistive-agents/lab/triage.py
T
claude f925fd9645 fix(M7-27+capstone): apply AI-drives-git reframe, lesson=theory, de-slop course-wide
Phase 2 sweep — all modules are post-pivot, so the learner directs the AI agent
(Claude Code as the worked example) to do the git/setup work and verifies, instead
of typing commands by hand; no re-teaching basics. Lesson sections are theory with
example output; all execution lives in the labs. De-slopped ("prose" etc. gone
course-wide, em-dash density thinned). /path/to placeholders -> ~/ai-workflow-course.

Every deliberate teaching device verified intact: M10 ai-change.patch trap,
M12 bad-clear-snippet, M13/M27 planted pending_count bug, M15 secret+typosquat+MD5,
M18 BREAK=1, M21 absent-.gitignore, M22 poisoned skill, M24 no-op patch, M25 --simulate.
Labs compile/parse (py/sh/yaml/json); no junk.

Closes #83
Closes #86
Closes #89

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
2026-06-22 21:58:17 -04:00

128 lines
4.8 KiB
Python

"""Assistive issue-triage agent — local simulation of a triage bot.
Stands in for a forge-native triage agent (triggered when an issue opens) without a hosted account.
It assembles the prompt, then validates and renders the AI's suggestion — and stops at a human
confirm. The agent proposes labels and a route; it does not apply them.
python triage.py prompt # taxonomy + issue -> prompt for the agent
python triage.py apply ai-triage.sample.json # validate + render + confirm gate
The validation step matters: the agent may only use labels that exist in label-taxonomy.md. A
hallucinated label is rejected. Stdlib only — no pip install.
"""
import argparse
import json
import re
import sys
from pathlib import Path
HERE = Path(__file__).parent
PROMPT_HEADER = """\
You are an assistive issue-triage agent. Using ONLY the taxonomy below, propose labels, a route,
and a rationale for the issue that follows. Return ONLY the JSON object the taxonomy specifies.
================ LABEL TAXONOMY ===============
{taxonomy}
================ INCOMING ISSUE ===============
{issue}
"""
# Allowed labels are the backticked `prefix:value` tokens in the taxonomy file. Keeping the source
# of truth in the committed markdown — not hardcoded here — is the point.
LABEL_RE = re.compile(r"`([a-z]+:[a-z0-9-]+)`")
def allowed_labels(taxonomy_text: str) -> set[str]:
return set(LABEL_RE.findall(taxonomy_text))
def load_json_response(path: Path):
"""Parse the JSON the AI returned.
Chat assistants very often wrap their output in a ```json ... ``` code fence (or add a stray
line of text) even when told to "return only the JSON", so a strict json.loads on the raw paste
fails on the most likely real output. Try a strict parse first; if that fails, fall back to the
outermost { ... } block, which survives a code fence or surrounding text. Stdlib only."""
raw = path.read_text()
try:
return json.loads(raw)
except json.JSONDecodeError:
start, end = raw.find("{"), raw.rfind("}")
if start != -1 and end > start:
return json.loads(raw[start : end + 1])
raise
def cmd_prompt(args: argparse.Namespace) -> int:
taxonomy = Path(args.taxonomy).read_text()
issue = Path(args.issue).read_text()
print(PROMPT_HEADER.format(taxonomy=taxonomy, issue=issue))
return 0
def cmd_apply(args: argparse.Namespace) -> int:
allowed = allowed_labels(Path(args.taxonomy).read_text())
try:
sug = load_json_response(Path(args.response))
except (json.JSONDecodeError, FileNotFoundError) as exc:
print(f"error: could not read a JSON suggestion from {args.response}: {exc}")
return 1
labels = sug.get("labels", [])
bogus = [l for l in labels if l not in allowed]
if bogus:
print("=" * 70)
print("REJECTED — the agent suggested labels that aren't in the taxonomy:")
for l in bogus:
print(f" - {l}")
print(
"\nThis is the guardrail working. The agent can only use labels you've committed to\n"
"label-taxonomy.md. Fix the prompt or the taxonomy and re-run; do not apply this.\n"
)
return 1
print("=" * 70)
print("TRIAGE AGENT — suggestion (advisory only)")
print("=" * 70)
print(f"\n Labels: {', '.join(labels) or '(none)'}")
print(f" Route to: {sug.get('assignee_type', '?')}")
print(f" Confidence: {sug.get('confidence', '?')}")
print(f" Rationale: {sug.get('rationale', '')}\n")
print("-" * 70)
print(
"Human confirm gate. The agent did NOT apply these labels or assign anyone.\n"
"You decide:\n"
" - confirm apply the labels and route as proposed\n"
" - edit change a label or the route, then apply\n"
" - reject the triage is wrong; do it yourself\n"
"\nA wrong label here costs one glance and one click to fix — which is exactly why\n"
"triage is the safe place to let an agent in first.\n"
)
return 0
def main(argv: list[str]) -> int:
parser = argparse.ArgumentParser(description=__doc__)
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("prompt", help="assemble the triage prompt for the agent to act on")
p.add_argument("--taxonomy", default=str(HERE / "label-taxonomy.md"))
p.add_argument("--issue", default=str(HERE / "sample-issue.md"))
p.set_defaults(func=cmd_prompt)
a = sub.add_parser("apply", help="validate + render the AI's suggestion, then gate it")
a.add_argument("response", help="path to the JSON the AI returned")
a.add_argument("--taxonomy", default=str(HERE / "label-taxonomy.md"))
a.set_defaults(func=cmd_apply)
args = parser.parse_args(argv)
return args.func(args)
if __name__ == "__main__":
raise SystemExit(main(sys.argv[1:]))