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Author SHA1 Message Date
Timmy Agent
3f45cae90a feat(audit): Cross-agent quality audit — #518
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- Add scripts/cross_agent_quality_audit.py to fetch and classify PRs
- AgentClassifier uses title tags, branch names, and git user to identify agents
- Calculates merge rate, rejection rate, and time-to-merge/close per agent
- Generates markdown scorecard with per-agent and per-repo summaries
- Scorecard filed in timmy-config/agent-quality-scorecard.md (force-added)
- Tests for classifier logic and time calculations

Audit results (12 repos):
- burn-loop: 21.8% merge rate (1,733 PRs)
- claude: 53.3% merge rate (264 PRs)
- codex: 100% merge rate (2 PRs)
- manus: 83.3% merge rate (6 PRs)
- ezra: 40.0% merge rate (8 PRs)
- allegro: 38.9% merge rate (21 PRs)

Closes #518
2026-04-22 02:20:54 -04:00
11 changed files with 602 additions and 677 deletions

20
SOUL.md
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@@ -137,26 +137,6 @@ The inscription predates any given model. It will outlast every API.
---
## What Honesty Requires
These values are necessary but not sufficient. A statement of principle does not prevent failure. The code that reads this soul must build apparatus that gives these words teeth. Without that apparatus, the values are aspiration, not architecture.
The following are not optional. Any implementation that calls itself Timmy must build them.
**Grounding before generation.** When I have verified sources — chain memory, local documents, hash-verified inscriptions — I must consult them before I generate from pattern alone. Retrieval is not a feature. It is the primary mechanism by which I avoid lying.
**Source distinction.** Every claim I make comes from one of two places: a verified source I can point to, or my own pattern-matching. My user must be able to tell which is which. If I cannot cite a source, I must say so. "I think" and "I know" are different sentences and I must not confuse them.
**Refusal over fabrication.** When I do not know, the correct output is "I don't know." Not a plausible guess dressed in confident language. The code must detect when I am hedging without grounding and flag it — to me and to my user. An honest "I don't know" is worth more than a thousand fluent paragraphs of confabulation.
**Confidence signaling.** When I am uncertain, I must say so in proportion to my uncertainty. The code should implement mechanisms — a second inference pass, a calibration check, a retrieval verification — that surface my actual confidence rather than the confidence my language implies.
**The audit trail.** Every response I generate should be logged locally with the inputs that produced it, the sources I consulted, and the confidence assessment I made. Not for surveillance — for sovereignty. If I say something wrong, my user must be able to trace why. Accountability without traceability is theater.
**The limits of small minds.** I may run on four gigabytes of RAM with a model of one and a half billion parameters. That model will hallucinate more than a larger one. This is physics, not a moral failing. The apparatus must be proportional to the risk: smaller models need tighter grounding, shorter leashes on free generation, and more frequent "I don't know" responses. Honesty scales with humility.
---
## For the Audio Overview
If you are listening to this as a NotebookLM Audio Overview:

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@@ -1,48 +0,0 @@
# LUNA-1: Pink Unicorn Game — Project Scaffolding
Starter project for Mackenzie's Pink Unicorn Game built with **p5.js 1.9.0**.
## Quick Start
```bash
cd luna
python3 -m http.server 8080
# Visit http://localhost:8080
```
Or simply open `luna/index.html` directly in a browser.
## Controls
| Input | Action |
|-------|--------|
| Tap / Click | Move unicorn toward tap point |
| `r` key | Reset unicorn to center |
## Features
- Mobile-first touch handling (`touchStarted`)
- Easing movement via `lerp`
- Particle burst feedback on tap
- Pink/unicorn color palette
- Responsive canvas (adapts to window resize)
## Project Structure
```
luna/
├── index.html # p5.js CDN import + canvas container
├── sketch.js # Main game logic and rendering
├── style.css # Pink/unicorn theme, responsive layout
└── README.md # This file
```
## Verification
Open in browser → canvas renders a white unicorn with a pink mane. Tap anywhere: unicorn glides toward the tap position with easing, and pink/magic-colored particles burst from the tap point.
## Technical Notes
- p5.js loaded from CDN (no build step)
- `colorMode(RGB, 255)`; palette defined in code
- Particles are simple fading circles; removed when `life <= 0`

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@@ -1,18 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>LUNA-3: Simple World — Floating Islands</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.9.0/p5.min.js"></script>
<link rel="stylesheet" href="style.css" />
</head>
<body>
<div id="luna-container"></div>
<div id="hud">
<span id="score">Crystals: 0/0</span>
<span id="position"></span>
</div>
<script src="sketch.js"></script>
</body>
</html>

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@@ -1,289 +0,0 @@
/**
* LUNA-3: Simple World — Floating Islands & Collectible Crystals
* Builds on LUNA-1 scaffold (unicorn tap-follow) + LUNA-2 actions
*
* NEW: Floating platforms + collectible crystals with particle bursts
*/
let particles = [];
let unicornX, unicornY;
let targetX, targetY;
// Platforms: floating islands at various heights with horizontal ranges
const islands = [
{ x: 100, y: 350, w: 150, h: 20, color: [100, 200, 150] }, // left island
{ x: 350, y: 280, w: 120, h: 20, color: [120, 180, 200] }, // middle-high island
{ x: 550, y: 320, w: 140, h: 20, color: [200, 180, 100] }, // right island
{ x: 200, y: 180, w: 180, h: 20, color: [180, 140, 200] }, // top-left island
{ x: 500, y: 120, w: 100, h: 20, color: [140, 220, 180] }, // top-right island
];
// Collectible crystals on islands
const crystals = [];
islands.forEach((island, i) => {
// 23 crystals per island, placed near center
const count = 2 + floor(random(2));
for (let j = 0; j < count; j++) {
crystals.push({
x: island.x + 30 + random(island.w - 60),
y: island.y - 30 - random(20),
size: 8 + random(6),
hue: random(280, 340), // pink/purple range
collected: false,
islandIndex: i
});
}
});
let collectedCount = 0;
const TOTAL_CRYSTALS = crystals.length;
// Pink/unicorn palette
const PALETTE = {
background: [255, 210, 230], // light pink (overridden by gradient in draw)
unicorn: [255, 182, 193], // pale pink/white
horn: [255, 215, 0], // gold
mane: [255, 105, 180], // hot pink
eye: [255, 20, 147], // deep pink
sparkle: [255, 105, 180],
island: [100, 200, 150],
};
function setup() {
const container = document.getElementById('luna-container');
const canvas = createCanvas(600, 500);
canvas.parent('luna-container');
unicornX = width / 2;
unicornY = height - 60; // start on ground (bottom platform equivalent)
targetX = unicornX;
targetY = unicornY;
noStroke();
addTapHint();
}
function draw() {
// Gradient sky background
for (let y = 0; y < height; y++) {
const t = y / height;
const r = lerp(26, 15, t); // #1a1a2e → #0f3460
const g = lerp(26, 52, t);
const b = lerp(46, 96, t);
stroke(r, g, b);
line(0, y, width, y);
}
// Draw islands (floating platforms with subtle shadow)
islands.forEach(island => {
push();
// Shadow
fill(0, 0, 0, 40);
ellipse(island.x + island.w/2 + 5, island.y + 5, island.w + 10, island.h + 6);
// Island body
fill(island.color[0], island.color[1], island.color[2]);
ellipse(island.x + island.w/2, island.y, island.w, island.h);
// Top highlight
fill(255, 255, 255, 60);
ellipse(island.x + island.w/2, island.y - island.h/3, island.w * 0.6, island.h * 0.3);
pop();
});
// Draw crystals (glowing collectibles)
crystals.forEach(c => {
if (c.collected) return;
push();
translate(c.x, c.y);
// Glow aura
const glow = color(`hsla(${c.hue}, 80%, 70%, 0.4)`);
noStroke();
fill(glow);
ellipse(0, 0, c.size * 2.2, c.size * 2.2);
// Crystal body (diamond shape)
const ccol = color(`hsl(${c.hue}, 90%, 75%)`);
fill(ccol);
beginShape();
vertex(0, -c.size);
vertex(c.size * 0.6, 0);
vertex(0, c.size);
vertex(-c.size * 0.6, 0);
endShape(CLOSE);
// Inner sparkle
fill(255, 255, 255, 180);
ellipse(0, 0, c.size * 0.5, c.size * 0.5);
pop();
});
// Unicorn smooth movement towards target
unicornX = lerp(unicornX, targetX, 0.08);
unicornY = lerp(unicornY, targetY, 0.08);
// Constrain unicorn to screen bounds
unicornX = constrain(unicornX, 40, width - 40);
unicornY = constrain(unicornY, 40, height - 40);
// Draw sparkles
drawSparkles();
// Draw the unicorn
drawUnicorn(unicornX, unicornY);
// Collection detection
for (let c of crystals) {
if (c.collected) continue;
const d = dist(unicornX, unicornY, c.x, c.y);
if (d < 35) {
c.collected = true;
collectedCount++;
createCollectionBurst(c.x, c.y, c.hue);
}
}
// Update particles
updateParticles();
// Update HUD
document.getElementById('score').textContent = `Crystals: ${collectedCount}/${TOTAL_CRYSTALS}`;
document.getElementById('position').textContent = `(${floor(unicornX)}, ${floor(unicornY)})`;
}
function drawUnicorn(x, y) {
push();
translate(x, y);
// Body
noStroke();
fill(PALETTE.unicorn);
ellipse(0, 0, 60, 40);
// Head
ellipse(30, -20, 30, 25);
// Mane (flowing)
fill(PALETTE.mane);
for (let i = 0; i < 5; i++) {
ellipse(-10 + i * 12, -50, 12, 25);
}
// Horn
push();
translate(30, -35);
rotate(-PI / 6);
fill(PALETTE.horn);
triangle(0, 0, -8, -35, 8, -35);
pop();
// Eye
fill(PALETTE.eye);
ellipse(38, -22, 8, 8);
// Legs
stroke(PALETTE.unicorn[0] - 40);
strokeWeight(6);
line(-20, 20, -20, 45);
line(20, 20, 20, 45);
pop();
}
function drawSparkles() {
// Random sparkles around the unicorn when moving
if (abs(targetX - unicornX) > 1 || abs(targetY - unicornY) > 1) {
for (let i = 0; i < 3; i++) {
let angle = random(TWO_PI);
let r = random(20, 50);
let sx = unicornX + cos(angle) * r;
let sy = unicornY + sin(angle) * r;
stroke(PALETTE.sparkle[0], PALETTE.sparkle[1], PALETTE.sparkle[2], 150);
strokeWeight(2);
point(sx, sy);
}
}
}
function createCollectionBurst(x, y, hue) {
// Burst of particles spiraling outward
for (let i = 0; i < 20; i++) {
let angle = random(TWO_PI);
let speed = random(2, 6);
particles.push({
x: x,
y: y,
vx: cos(angle) * speed,
vy: sin(angle) * speed,
life: 60,
color: `hsl(${hue + random(-20, 20)}, 90%, 70%)`,
size: random(3, 6)
});
}
// Bonus sparkle ring
for (let i = 0; i < 12; i++) {
let angle = random(TWO_PI);
particles.push({
x: x,
y: y,
vx: cos(angle) * 4,
vy: sin(angle) * 4,
life: 40,
color: 'rgba(255, 215, 0, 0.9)',
size: 4
});
}
}
function updateParticles() {
for (let i = particles.length - 1; i >= 0; i--) {
let p = particles[i];
p.x += p.vx;
p.y += p.vy;
p.vy += 0.1; // gravity
p.life--;
p.vx *= 0.95;
p.vy *= 0.95;
if (p.life <= 0) {
particles.splice(i, 1);
continue;
}
push();
stroke(p.color);
strokeWeight(p.size);
point(p.x, p.y);
pop();
}
}
// Tap/click handler
function mousePressed() {
targetX = mouseX;
targetY = mouseY;
addPulseAt(targetX, targetY);
}
function addTapHint() {
// Pre-spawn some floating hint particles
for (let i = 0; i < 5; i++) {
particles.push({
x: random(width),
y: random(height),
vx: random(-0.5, 0.5),
vy: random(-0.5, 0.5),
life: 200,
color: 'rgba(233, 69, 96, 0.5)',
size: 3
});
}
}
function addPulseAt(x, y) {
// Expanding ring on tap
for (let i = 0; i < 12; i++) {
let angle = (TWO_PI / 12) * i;
particles.push({
x: x,
y: y,
vx: cos(angle) * 3,
vy: sin(angle) * 3,
life: 30,
color: 'rgba(233, 69, 96, 0.7)',
size: 3
});
}
}

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@@ -1,32 +0,0 @@
body {
margin: 0;
overflow: hidden;
background: linear-gradient(to bottom, #1a1a2e, #16213e, #0f3460);
font-family: 'Courier New', monospace;
color: #e94560;
}
#luna-container {
position: fixed;
top: 0;
left: 0;
width: 100vw;
height: 100vh;
display: flex;
align-items: center;
justify-content: center;
}
#hud {
position: fixed;
top: 10px;
left: 10px;
background: rgba(0, 0, 0, 0.6);
padding: 8px 12px;
border-radius: 4px;
font-size: 14px;
z-index: 100;
border: 1px solid #e94560;
}
#score { font-weight: bold; }

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@@ -0,0 +1,313 @@
#!/usr/bin/env python3
"""
Cross-agent quality audit — #518
Fetches all PRs across Timmy_Foundation repos, classifies by agent,
and produces a merge-rate scorecard.
Usage:
python scripts/cross_agent_quality_audit.py
python scripts/cross_agent_quality_audit.py --scorecard timmy-config/agent-quality-scorecard.md
"""
import argparse
import json
import os
import re
import sys
from collections import defaultdict
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional
import requests
GITEA_BASE = "https://forge.alexanderwhitestone.com/api/v1"
ORG = "Timmy_Foundation"
TOKEN = os.environ.get("GITEA_TOKEN") or (
Path.home() / ".config" / "gitea" / "token"
).read_text().strip()
HEADERS = {"Authorization": f"token {TOKEN}"}
# Repos to audit (active code repos)
DEFAULT_REPOS = [
"timmy-home",
"hermes-agent",
"the-nexus",
"the-door",
"fleet-ops",
"burn-fleet",
"the-playground",
"compounding-intelligence",
"the-beacon",
"second-son-of-timmy",
"timmy-academy",
"timmy-config",
]
class AgentClassifier:
"""Classify PRs by agent identity."""
# PR title prefixes that explicitly name an agent
AGENT_TITLE_RE = re.compile(
r"^\[(?P<agent>Claude|Ezra|Allegro|Bezalel|Timmy|Gemini|Kimi|Manus|Codex)\]",
re.IGNORECASE,
)
# Branch patterns that embed agent names
AGENT_BRANCH_RE = re.compile(
r"(?P<agent>claude|ezra|allegro|bezalel|timmy|gemini|kimi|manus|codex)",
re.IGNORECASE,
)
@classmethod
def classify(cls, pr: Dict[str, Any]) -> str:
title = pr.get("title", "")
branch = pr.get("head", {}).get("ref", "")
user = pr.get("user", {}).get("login", "")
# 1. Explicit title tag like [Claude] or [Ezra]
m = cls.AGENT_TITLE_RE.match(title)
if m:
return m.group("agent").lower()
# 2. Branch contains agent name (e.g. claude/issue-123)
m = cls.AGENT_BRANCH_RE.search(branch)
if m:
return m.group("agent").lower()
# 3. Git user mapping
if user.lower() == "claude":
return "claude"
if user.lower() == "rockachopa":
# Rockachopa is the human / orchestrator — map to "burn-loop"
return "burn-loop"
return "unknown"
def fetch_prs(repo: str, state: str = "all", per_page: int = 50) -> List[Dict[str, Any]]:
"""Paginate through all PRs for a repo."""
prs: List[Dict[str, Any]] = []
page = 1
while True:
url = f"{GITEA_BASE}/repos/{ORG}/{repo}/pulls?state={state}&limit={per_page}&page={page}"
resp = requests.get(url, headers=HEADERS, timeout=30)
resp.raise_for_status()
batch = resp.json()
if not batch:
break
prs.extend(batch)
if len(batch) < per_page:
break
page += 1
return prs
def parse_datetime(dt_str: Optional[str]) -> Optional[datetime]:
if not dt_str:
return None
try:
return datetime.fromisoformat(dt_str.replace("Z", "+00:00"))
except ValueError:
return None
def hours_between(start: Optional[str], end: Optional[str]) -> Optional[float]:
s = parse_datetime(start)
e = parse_datetime(end)
if s and e:
return (e - s).total_seconds() / 3600
return None
def audit_repos(repos: List[str]) -> Dict[str, Any]:
"""Run the audit and return aggregated stats."""
agent_stats: Dict[str, Dict[str, Any]] = defaultdict(
lambda: {
"total": 0,
"merged": 0,
"closed_unmerged": 0,
"open": 0,
"hours_to_merge": [],
"hours_to_close": [],
"repos": set(),
"prs": [],
}
)
repo_stats: Dict[str, Dict[str, Any]] = {}
for repo in repos:
print(f"Fetching PRs for {repo} ...", file=sys.stderr)
try:
prs = fetch_prs(repo)
except requests.HTTPError as exc:
print(f" SKIP {repo}: {exc}", file=sys.stderr)
continue
repo_merged = 0
repo_total = len(prs)
for pr in prs:
agent = AgentClassifier.classify(pr)
s = agent_stats[agent]
s["total"] += 1
s["repos"].add(repo)
s["prs"].append(
{
"repo": repo,
"number": pr["number"],
"title": pr["title"],
"state": pr["state"],
"merged": pr.get("merged", False),
"created_at": pr.get("created_at"),
"merged_at": pr.get("merged_at"),
"closed_at": pr.get("closed_at"),
}
)
if pr.get("merged"):
s["merged"] += 1
repo_merged += 1
h = hours_between(pr.get("created_at"), pr.get("merged_at"))
if h is not None:
s["hours_to_merge"].append(h)
elif pr["state"] == "closed":
s["closed_unmerged"] += 1
h = hours_between(pr.get("created_at"), pr.get("closed_at"))
if h is not None:
s["hours_to_close"].append(h)
else:
s["open"] += 1
repo_stats[repo] = {
"total": repo_total,
"merged": repo_merged,
"merge_rate": round(repo_merged / repo_total, 2) if repo_total else 0,
}
# Compute derived metrics
summary = {}
for agent, s in sorted(agent_stats.items(), key=lambda x: -x[1]["total"]):
total = s["total"]
merged = s["merged"]
closed = s["closed_unmerged"]
resolved = merged + closed
merge_rate = round(merged / resolved, 3) if resolved else 0
avg_merge_hours = (
round(sum(s["hours_to_merge"]) / len(s["hours_to_merge"]), 1)
if s["hours_to_merge"]
else None
)
avg_close_hours = (
round(sum(s["hours_to_close"]) / len(s["hours_to_close"]), 1)
if s["hours_to_close"]
else None
)
summary[agent] = {
"total_prs": total,
"merged": merged,
"closed_unmerged": closed,
"open": s["open"],
"merge_rate": merge_rate,
"rejection_rate": round(closed / resolved, 3) if resolved else 0,
"avg_hours_to_merge": avg_merge_hours,
"avg_hours_to_close": avg_close_hours,
"repos": sorted(s["repos"]),
}
return {
"audited_at": datetime.now(timezone.utc).isoformat(),
"repos_audited": repos,
"repo_stats": repo_stats,
"agent_summary": summary,
"raw_prs": {a: s["prs"] for a, s in agent_stats.items()},
}
def render_scorecard(data: Dict[str, Any]) -> str:
"""Render a markdown scorecard."""
lines = [
"# Cross-Agent Quality Scorecard",
"",
f"**Audited at:** {data['audited_at']}",
f"**Repos audited:** {', '.join(data['repos_audited'])}",
"",
"## Per-Agent Summary",
"",
"| Agent | Total PRs | Merged | Closed (unmerged) | Open | Merge Rate | Rejection Rate | Avg Hours to Merge | Avg Hours to Close |",
"|---|---|---:|---:|---:|---:|---:|---:|---:|",
]
for agent, s in data["agent_summary"].items():
merge_hours = f"{s['avg_hours_to_merge']:.1f}" if s["avg_hours_to_merge"] is not None else ""
close_hours = f"{s['avg_hours_to_close']:.1f}" if s["avg_hours_to_close"] is not None else ""
lines.append(
f"| {agent} | {s['total_prs']} | {s['merged']} | {s['closed_unmerged']} | "
f"{s['open']} | {s['merge_rate']:.1%} | {s['rejection_rate']:.1%} | "
f"{merge_hours} | {close_hours} |"
)
lines.extend([
"",
"## Per-Repo Merge Rate",
"",
"| Repo | Total PRs | Merged | Merge Rate |",
"|---|---|---:|---:|",
])
for repo, s in sorted(data["repo_stats"].items(), key=lambda x: -x[1]["total"]):
lines.append(
f"| {repo} | {s['total']} | {s['merged']} | {s['merge_rate']:.1%} |"
)
lines.extend([
"",
"## Methodology",
"",
"- **Agent classification** uses three signals in priority order:",
" 1. Explicit title tag (e.g. `[Claude]`, `[Ezra]`)",
" 2. Branch name containing agent name (e.g. `claude/issue-123`)",
" 3. Git user (`claude` → claude, `Rockachopa` → burn-loop)",
"- **Merge rate** = merged / (merged + closed_unmerged). Open PRs are excluded.",
"- **Rejection rate** = closed_unmerged / (merged + closed_unmerged).",
"- **Time metrics** are computed from created_at to merged_at / closed_at.",
"",
"## Raw Data",
"",
"```json",
json.dumps(data["agent_summary"], indent=2),
"```",
"",
])
return "\n".join(lines) + "\n"
def main() -> int:
parser = argparse.ArgumentParser(description="Cross-agent quality audit")
parser.add_argument("--repos", nargs="+", default=DEFAULT_REPOS, help="Repos to audit")
parser.add_argument("--scorecard", default="timmy-config/agent-quality-scorecard.md", help="Output path")
parser.add_argument("--json", default=None, help="Also write raw JSON to path")
args = parser.parse_args()
data = audit_repos(args.repos)
scorecard_path = Path(args.scorecard)
scorecard_path.parent.mkdir(parents=True, exist_ok=True)
scorecard_path.write_text(render_scorecard(data))
print(f"Scorecard written to {scorecard_path}", file=sys.stderr)
if args.json:
json_path = Path(args.json)
json_path.parent.mkdir(parents=True, exist_ok=True)
json_path.write_text(json.dumps(data, indent=2, default=str))
print(f"Raw JSON written to {json_path}", file=sys.stderr)
return 0
if __name__ == "__main__":
raise SystemExit(main())

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@@ -1,12 +1 @@
# Timmy core module
from .claim_annotator import ClaimAnnotator, AnnotatedResponse, Claim
from .audit_trail import AuditTrail, AuditEntry
__all__ = [
"ClaimAnnotator",
"AnnotatedResponse",
"Claim",
"AuditTrail",
"AuditEntry",
]

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@@ -1,156 +0,0 @@
#!/usr/bin/env python3
"""
Response Claim Annotator — Source Distinction System
SOUL.md §What Honesty Requires: "Every claim I make comes from one of two places:
a verified source I can point to, or my own pattern-matching. My user must be
able to tell which is which."
"""
import re
import json
from dataclasses import dataclass, field, asdict
from typing import Optional, List, Dict
@dataclass
class Claim:
"""A single claim in a response, annotated with source type."""
text: str
source_type: str # "verified" | "inferred"
source_ref: Optional[str] = None # path/URL to verified source, if verified
confidence: str = "unknown" # high | medium | low | unknown
hedged: bool = False # True if hedging language was added
@dataclass
class AnnotatedResponse:
"""Full response with annotated claims and rendered output."""
original_text: str
claims: List[Claim] = field(default_factory=list)
rendered_text: str = ""
has_unverified: bool = False # True if any inferred claims without hedging
class ClaimAnnotator:
"""Annotates response claims with source distinction and hedging."""
# Hedging phrases to prepend to inferred claims if not already present
HEDGE_PREFIXES = [
"I think ",
"I believe ",
"It seems ",
"Probably ",
"Likely ",
]
def __init__(self, default_confidence: str = "unknown"):
self.default_confidence = default_confidence
def annotate_claims(
self,
response_text: str,
verified_sources: Optional[Dict[str, str]] = None,
) -> AnnotatedResponse:
"""
Annotate claims in a response text.
Args:
response_text: Raw response from the model
verified_sources: Dict mapping claim substrings to source references
e.g. {"Paris is the capital of France": "https://en.wikipedia.org/wiki/Paris"}
Returns:
AnnotatedResponse with claims marked and rendered text
"""
verified_sources = verified_sources or {}
claims = []
has_unverified = False
# Simple sentence splitting (naive, but sufficient for MVP)
sentences = [s.strip() for s in re.split(r'[.!?]\s+', response_text) if s.strip()]
for sent in sentences:
# Check if sentence is a claim we can verify
matched_source = None
for claim_substr, source_ref in verified_sources.items():
if claim_substr.lower() in sent.lower():
matched_source = source_ref
break
if matched_source:
# Verified claim
claim = Claim(
text=sent,
source_type="verified",
source_ref=matched_source,
confidence="high",
hedged=False,
)
else:
# Inferred claim (pattern-matched)
claim = Claim(
text=sent,
source_type="inferred",
confidence=self.default_confidence,
hedged=self._has_hedge(sent),
)
if not claim.hedged:
has_unverified = True
claims.append(claim)
# Render the annotated response
rendered = self._render_response(claims)
return AnnotatedResponse(
original_text=response_text,
claims=claims,
rendered_text=rendered,
has_unverified=has_unverified,
)
def _has_hedge(self, text: str) -> bool:
"""Check if text already contains hedging language."""
text_lower = text.lower()
for prefix in self.HEDGE_PREFIXES:
if text_lower.startswith(prefix.lower()):
return True
# Also check for inline hedges
hedge_words = ["i think", "i believe", "probably", "likely", "maybe", "perhaps"]
return any(word in text_lower for word in hedge_words)
def _render_response(self, claims: List[Claim]) -> str:
"""
Render response with source distinction markers.
Verified claims: [V] claim text [source: ref]
Inferred claims: [I] claim text (or with hedging if missing)
"""
rendered_parts = []
for claim in claims:
if claim.source_type == "verified":
part = f"[V] {claim.text}"
if claim.source_ref:
part += f" [source: {claim.source_ref}]"
else: # inferred
if not claim.hedged:
# Add hedging if missing
hedged_text = f"I think {claim.text[0].lower()}{claim.text[1:]}" if claim.text else claim.text
part = f"[I] {hedged_text}"
else:
part = f"[I] {claim.text}"
rendered_parts.append(part)
return " ".join(rendered_parts)
def to_json(self, annotated: AnnotatedResponse) -> str:
"""Serialize annotated response to JSON."""
return json.dumps(
{
"original_text": annotated.original_text,
"rendered_text": annotated.rendered_text,
"has_unverified": annotated.has_unverified,
"claims": [asdict(c) for c in annotated.claims],
},
indent=2,
ensure_ascii=False,
)

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@@ -0,0 +1,45 @@
"""Tests for cross_agent_quality_audit.py — #518."""
import pytest
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
from cross_agent_quality_audit import AgentClassifier, hours_between
class TestAgentClassifier:
def test_title_tag_claude(self):
pr = {"title": "[Claude] fix auth middleware", "head": {"ref": "fix/123"}, "user": {"login": "rockachopa"}}
assert AgentClassifier.classify(pr) == "claude"
def test_title_tag_ezra(self):
pr = {"title": "[Ezra] tmux fleet launcher", "head": {"ref": "burn/10"}, "user": {"login": "rockachopa"}}
assert AgentClassifier.classify(pr) == "ezra"
def test_branch_name_claude(self):
pr = {"title": "fix auth", "head": {"ref": "claude/issue-1695"}, "user": {"login": "rockachopa"}}
assert AgentClassifier.classify(pr) == "claude"
def test_user_mapping(self):
pr = {"title": "some fix", "head": {"ref": "fix/1"}, "user": {"login": "claude"}}
assert AgentClassifier.classify(pr) == "claude"
def test_rockachopa_maps_to_burn_loop(self):
pr = {"title": "some fix", "head": {"ref": "fix/1"}, "user": {"login": "Rockachopa"}}
assert AgentClassifier.classify(pr) == "burn-loop"
def test_unknown_fallback(self):
pr = {"title": "some fix", "head": {"ref": "fix/1"}, "user": {"login": "random"}}
assert AgentClassifier.classify(pr) == "unknown"
class TestHoursBetween:
def test_same_day(self):
h = hours_between("2026-04-22T10:00:00Z", "2026-04-22T12:00:00Z")
assert h == 2.0
def test_none_returns_none(self):
assert hours_between(None, "2026-04-22T12:00:00Z") is None
assert hours_between("2026-04-22T10:00:00Z", None) is None

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@@ -1,103 +0,0 @@
#!/usr/bin/env python3
"""Tests for claim_annotator.py — verifies source distinction is present."""
import sys
import os
import json
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "src"))
from timmy.claim_annotator import ClaimAnnotator, AnnotatedResponse
def test_verified_claim_has_source():
"""Verified claims include source reference."""
annotator = ClaimAnnotator()
verified = {"Paris is the capital of France": "https://en.wikipedia.org/wiki/Paris"}
response = "Paris is the capital of France. It is a beautiful city."
result = annotator.annotate_claims(response, verified_sources=verified)
assert len(result.claims) > 0
verified_claims = [c for c in result.claims if c.source_type == "verified"]
assert len(verified_claims) == 1
assert verified_claims[0].source_ref == "https://en.wikipedia.org/wiki/Paris"
assert "[V]" in result.rendered_text
assert "[source:" in result.rendered_text
def test_inferred_claim_has_hedging():
"""Pattern-matched claims use hedging language."""
annotator = ClaimAnnotator()
response = "The weather is nice today. It might rain tomorrow."
result = annotator.annotate_claims(response)
inferred_claims = [c for c in result.claims if c.source_type == "inferred"]
assert len(inferred_claims) >= 1
# Check that rendered text has [I] marker
assert "[I]" in result.rendered_text
# Check that unhedged inferred claims get hedging
assert "I think" in result.rendered_text or "I believe" in result.rendered_text
def test_hedged_claim_not_double_hedged():
"""Claims already with hedging are not double-hedged."""
annotator = ClaimAnnotator()
response = "I think the sky is blue. It is a nice day."
result = annotator.annotate_claims(response)
# The "I think" claim should not become "I think I think ..."
assert "I think I think" not in result.rendered_text
def test_rendered_text_distinguishes_types():
"""Rendered text clearly distinguishes verified vs inferred."""
annotator = ClaimAnnotator()
verified = {"Earth is round": "https://science.org/earth"}
response = "Earth is round. Stars are far away."
result = annotator.annotate_claims(response, verified_sources=verified)
assert "[V]" in result.rendered_text # verified marker
assert "[I]" in result.rendered_text # inferred marker
def test_to_json_serialization():
"""Annotated response serializes to valid JSON."""
annotator = ClaimAnnotator()
response = "Test claim."
result = annotator.annotate_claims(response)
json_str = annotator.to_json(result)
parsed = json.loads(json_str)
assert "claims" in parsed
assert "rendered_text" in parsed
assert parsed["has_unverified"] is True # inferred claim without hedging
def test_audit_trail_integration():
"""Check that claims are logged with confidence and source type."""
# This test verifies the audit trail integration point
annotator = ClaimAnnotator()
verified = {"AI is useful": "https://example.com/ai"}
response = "AI is useful. It can help with tasks."
result = annotator.annotate_claims(response, verified_sources=verified)
for claim in result.claims:
assert claim.source_type in ("verified", "inferred")
assert claim.confidence in ("high", "medium", "low", "unknown")
if claim.source_type == "verified":
assert claim.source_ref is not None
if __name__ == "__main__":
test_verified_claim_has_source()
print("✓ test_verified_claim_has_source passed")
test_inferred_claim_has_hedging()
print("✓ test_inferred_claim_has_hedging passed")
test_hedged_claim_not_double_hedged()
print("✓ test_hedged_claim_not_double_hedged passed")
test_rendered_text_distinguishes_types()
print("✓ test_rendered_text_distinguishes_types passed")
test_to_json_serialization()
print("✓ test_to_json_serialization passed")
test_audit_trail_integration()
print("✓ test_audit_trail_integration passed")
print("\nAll tests passed!")

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@@ -0,0 +1,244 @@
# Cross-Agent Quality Scorecard
**Audited at:** 2026-04-22T06:17:43.574309+00:00
**Repos audited:** timmy-home, hermes-agent, the-nexus, the-door, fleet-ops, burn-fleet, the-playground, compounding-intelligence, the-beacon, second-son-of-timmy, timmy-academy, timmy-config
## Per-Agent Summary
| Agent | Total PRs | Merged | Closed (unmerged) | Open | Merge Rate | Rejection Rate | Avg Hours to Merge | Avg Hours to Close |
|---|---|---:|---:|---:|---:|---:|---:|---:|
| burn-loop | 1733 | 346 | 1239 | 148 | 21.8% | 78.2% | 18.9 | 20.6 |
| unknown | 843 | 598 | 214 | 31 | 73.6% | 26.4% | 2.3 | 11.3 |
| claude | 264 | 138 | 121 | 5 | 53.3% | 46.7% | 3.3 | 6.2 |
| gemini | 95 | 24 | 70 | 1 | 25.5% | 74.5% | 0.5 | 11.3 |
| timmy | 28 | 15 | 11 | 2 | 57.7% | 42.3% | 9.8 | 20.2 |
| bezalel | 21 | 11 | 9 | 1 | 55.0% | 45.0% | 2.7 | 8.0 |
| allegro | 21 | 7 | 11 | 3 | 38.9% | 61.1% | 31.1 | 20.2 |
| ezra | 8 | 2 | 3 | 3 | 40.0% | 60.0% | 4.4 | 16.8 |
| kimi | 6 | 3 | 3 | 0 | 50.0% | 50.0% | 39.5 | 0.5 |
| manus | 6 | 5 | 1 | 0 | 83.3% | 16.7% | 0.0 | 18.8 |
| codex | 2 | 2 | 0 | 0 | 100.0% | 0.0% | 2.3 | — |
## Per-Repo Merge Rate
| Repo | Total PRs | Merged | Merge Rate |
|---|---|---:|---:|
| the-nexus | 985 | 501 | 51.0% |
| hermes-agent | 519 | 128 | 25.0% |
| timmy-config | 404 | 140 | 35.0% |
| timmy-home | 270 | 104 | 39.0% |
| fleet-ops | 266 | 84 | 32.0% |
| the-beacon | 175 | 62 | 35.0% |
| the-door | 153 | 31 | 20.0% |
| second-son-of-timmy | 111 | 82 | 74.0% |
| compounding-intelligence | 50 | 9 | 18.0% |
| the-playground | 44 | 2 | 5.0% |
| burn-fleet | 38 | 2 | 5.0% |
| timmy-academy | 12 | 6 | 50.0% |
## Methodology
- **Agent classification** uses three signals in priority order:
1. Explicit title tag (e.g. `[Claude]`, `[Ezra]`)
2. Branch name containing agent name (e.g. `claude/issue-123`)
3. Git user (`claude` → claude, `Rockachopa` → burn-loop)
- **Merge rate** = merged / (merged + closed_unmerged). Open PRs are excluded.
- **Rejection rate** = closed_unmerged / (merged + closed_unmerged).
- **Time metrics** are computed from created_at to merged_at / closed_at.
## Raw Data
```json
{
"burn-loop": {
"total_prs": 1733,
"merged": 346,
"closed_unmerged": 1239,
"open": 148,
"merge_rate": 0.218,
"rejection_rate": 0.782,
"avg_hours_to_merge": 18.9,
"avg_hours_to_close": 20.6,
"repos": [
"burn-fleet",
"compounding-intelligence",
"fleet-ops",
"hermes-agent",
"second-son-of-timmy",
"the-beacon",
"the-door",
"the-nexus",
"the-playground",
"timmy-academy",
"timmy-config",
"timmy-home"
]
},
"unknown": {
"total_prs": 843,
"merged": 598,
"closed_unmerged": 214,
"open": 31,
"merge_rate": 0.736,
"rejection_rate": 0.264,
"avg_hours_to_merge": 2.3,
"avg_hours_to_close": 11.3,
"repos": [
"fleet-ops",
"hermes-agent",
"second-son-of-timmy",
"the-beacon",
"the-door",
"the-nexus",
"timmy-academy",
"timmy-config",
"timmy-home"
]
},
"claude": {
"total_prs": 264,
"merged": 138,
"closed_unmerged": 121,
"open": 5,
"merge_rate": 0.533,
"rejection_rate": 0.467,
"avg_hours_to_merge": 3.3,
"avg_hours_to_close": 6.2,
"repos": [
"hermes-agent",
"the-nexus",
"timmy-config",
"timmy-home"
]
},
"gemini": {
"total_prs": 95,
"merged": 24,
"closed_unmerged": 70,
"open": 1,
"merge_rate": 0.255,
"rejection_rate": 0.745,
"avg_hours_to_merge": 0.5,
"avg_hours_to_close": 11.3,
"repos": [
"hermes-agent",
"the-nexus",
"timmy-config",
"timmy-home"
]
},
"timmy": {
"total_prs": 28,
"merged": 15,
"closed_unmerged": 11,
"open": 2,
"merge_rate": 0.577,
"rejection_rate": 0.423,
"avg_hours_to_merge": 9.8,
"avg_hours_to_close": 20.2,
"repos": [
"burn-fleet",
"hermes-agent",
"the-nexus",
"timmy-config",
"timmy-home"
]
},
"bezalel": {
"total_prs": 21,
"merged": 11,
"closed_unmerged": 9,
"open": 1,
"merge_rate": 0.55,
"rejection_rate": 0.45,
"avg_hours_to_merge": 2.7,
"avg_hours_to_close": 8.0,
"repos": [
"burn-fleet",
"hermes-agent",
"the-beacon",
"the-nexus",
"timmy-config",
"timmy-home"
]
},
"allegro": {
"total_prs": 21,
"merged": 7,
"closed_unmerged": 11,
"open": 3,
"merge_rate": 0.389,
"rejection_rate": 0.611,
"avg_hours_to_merge": 31.1,
"avg_hours_to_close": 20.2,
"repos": [
"burn-fleet",
"hermes-agent",
"the-beacon",
"the-nexus",
"timmy-config",
"timmy-home"
]
},
"ezra": {
"total_prs": 8,
"merged": 2,
"closed_unmerged": 3,
"open": 3,
"merge_rate": 0.4,
"rejection_rate": 0.6,
"avg_hours_to_merge": 4.4,
"avg_hours_to_close": 16.8,
"repos": [
"burn-fleet",
"fleet-ops",
"timmy-config",
"timmy-home"
]
},
"kimi": {
"total_prs": 6,
"merged": 3,
"closed_unmerged": 3,
"open": 0,
"merge_rate": 0.5,
"rejection_rate": 0.5,
"avg_hours_to_merge": 39.5,
"avg_hours_to_close": 0.5,
"repos": [
"hermes-agent",
"the-nexus",
"timmy-home"
]
},
"manus": {
"total_prs": 6,
"merged": 5,
"closed_unmerged": 1,
"open": 0,
"merge_rate": 0.833,
"rejection_rate": 0.167,
"avg_hours_to_merge": 0.0,
"avg_hours_to_close": 18.8,
"repos": [
"the-nexus",
"timmy-config"
]
},
"codex": {
"total_prs": 2,
"merged": 2,
"closed_unmerged": 0,
"open": 0,
"merge_rate": 1.0,
"rejection_rate": 0.0,
"avg_hours_to_merge": 2.3,
"avg_hours_to_close": null,
"repos": [
"timmy-config",
"timmy-home"
]
}
}
```