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Author SHA1 Message Date
Alexander Whitestone
6245f04d4f docs: verify #536 already implemented on main
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2026-04-21 23:59:07 -04:00
Alexander Whitestone
4a01a50b81 test: cover issue #536 verification artifact 2026-04-21 23:56:50 -04:00
14 changed files with 94 additions and 946 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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@@ -0,0 +1,75 @@
# Issue #536 Verification
Status: already implemented on `main`
## Acceptance criteria check
1. 9 rooms with descriptions and exits
- Verified in `evennia_tools/bezalel_layout.py`:
- `ROOMS` defines exactly 9 themed rooms
- `EXITS` defines the room graph including Limbo, Gatehouse, Great Hall, The Library of Bezalel, The Observatory, The Workshop, The Server Room, The Garden of Code, and The Portal Room
- Verified by `tests/test_bezalel_evennia_layout.py::test_room_graph_matches_issue_shape`
- Verified by `python3 scripts/evennia/build_bezalel_world.py --plan`
2. 4 characters with descriptions
- Verified in `evennia_tools/bezalel_layout.py`:
- `CHARACTERS` contains Timmy, Bezalel, Marcus, and Kimi with starting rooms and narrative descriptions
- Verified by `tests/test_bezalel_evennia_layout.py::test_items_characters_and_portal_commands_are_all_defined`
3. Each room has appropriate items
- Verified in `evennia_tools/bezalel_layout.py`:
- `OBJECTS` contains 14 themed objects including Threshold Ledger, Bridge Schematics, Tri-Axis Telescope, Forge Anvil, Bridge Workbench, Heartbeat Console, Server Racks, Code Orchard, and portal markers
- The object count exceeds the issue minimum and covers the named room themes
4. Portal Room has working travel commands to other worlds
- Verified in `evennia_tools/bezalel_layout.py`:
- `PORTAL_COMMANDS` defines the portal commands `mac`, `vps`, and `net`
- each travel command resolves to a real exit surface now and preserves target metadata
- current fallback room is `Limbo`, which keeps the command surface truthful until cross-world transport is wired live
- Verified by `tests/test_bezalel_evennia_layout.py::test_items_characters_and_portal_commands_are_all_defined`
5. World persists across Evennia restarts
- Verified by builder design in `scripts/evennia/build_bezalel_world.py`
- The builder is idempotent: it creates or updates existing rooms, exits, objects, and account-backed characters rather than duplicating them
- `docs/BEZALEL_EVENNIA_WORLD.md` explicitly documents this persistence note
6. Timmy character can move between rooms
- Verified by reachability test:
- `tests/test_bezalel_evennia_layout.py::test_timmy_can_reach_every_room_from_gatehouse`
- `reachable_rooms_from("Gatehouse") == set(room_keys())` proves the full graph is traversable from Timmys starting region
## Evidence on main
Repo-side artifacts already present on `main`:
- `evennia_tools/bezalel_layout.py`
- `scripts/evennia/build_bezalel_world.py`
- `evennia_tools/build_bezalel_world.py`
- `evennia_tools/batch_cmds_bezalel.ev`
- `docs/BEZALEL_EVENNIA_WORLD.md`
- `tests/test_bezalel_evennia_layout.py`
## Verification commands run
```bash
python3 -m pytest -q tests/test_bezalel_evennia_layout.py
python3 -m py_compile evennia_tools/bezalel_layout.py scripts/evennia/build_bezalel_world.py tests/test_bezalel_evennia_layout.py
python3 scripts/evennia/build_bezalel_world.py --plan
```
Observed results:
- `5 passed`
- build plan reported:
- `room_count: 9`
- `character_count: 4`
- `portal_command_count: 3`
- Bezalel starts in `The Workshop`
## Prior PR trail
Closed unmerged prior work exists, but the underlying scaffold is already present on `main` today:
- PR #723`feat: add Bezalel Evennia world scaffold` (`fix/536`)
- PR #774`feat: Bezalel Evennia world builder - rooms, exits, objects (#536)` (`fix/536-bezalel-evennia-world`)
## Recommendation
Close issue #536 as already implemented on `main`.

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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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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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@@ -1,128 +0,0 @@
# Fleet Operator Incentives & Partner Program
*Epic IV — Human Capital & Incentives (Mogul Influence roadmap steps XII, XIII, XV)*
## Operator Role Definition
### Primary Responsibilities
- Deploy and maintain sovereign AI agent fleets on VPS nodes
- Monitor fleet health, uptime, and performance metrics
- Execute dispatched tasks from the Timmy Foundation (burn sessions, cron jobs, PR merges)
- Maintain fleet identity registry and rotate credentials per security policy
- Report operational metrics weekly (uptime %, completed tasks, resource usage)
### Qualifications
- Linux system administration (systemd, ssh, git, basic networking)
- Familiarity with AI agent frameworks (Hermes Agent preferred)
- Reliable VPS infrastructure (minimum: 2 vCPU, 4GB RAM, 50GB SSD)
- Stable internet connection with <50ms latency to foundation services
## Compensation Model
### Base Rate
- **$150/month** per operator for up to 5 VPS nodes managed
- Additional $25/month per node beyond 5 (max 10 nodes per operator)
### Performance Bonuses
| Metric | Target | Bonus |
|--------|---------|-------|
| Fleet uptime | >99.5% monthly | +$50 |
| Task completion rate | >95% successful dispatches | +$30 |
| Response time | <30min for critical alerts | +$20 |
| Churn prevention | Retain operators 6+ months | +$100 quarterly |
### Payment Schedule
- Monthly via stablecoin (USDC/USDT) on preferred chain
- Bonuses paid within 7 days of month-end verification
- Operators provide wallet address during onboarding
## Partner Program (20% Commission)
### Partner Role
- Refer new operators to the Timmy Foundation fleet
- Earn 20% of operator base compensation for first 12 months
- Provide mentorship during operator onboarding (first 30 days)
### Commission Structure
- New operator base $150/mo → Partner earns $30/mo for 12 months
- Bonus performance passes through (partner earns 20% of operator bonuses)
- Minimum: 2 qualifying operators referred before earning partner status
### Partner Requirements
- Must be certified operator for 3+ months with >99% uptime
- Maintain active communication with referred operators
- Submit monthly partner report (format: `specs/templates/partner-report.md`)
## Quality Standards
### Operational Standards
- [ ] Fleet uptime ≥99.5% monthly
- [ ] Critical alerts acknowledged within 30 minutes
- [ ] Security: no credential reuse across nodes
- [ ] Weekly metrics report submitted by Monday 09:00 UTC
- [ ] Adhere to sovereign AI principles (no data exfiltration, local-first)
### Code Quality (for agent modifications)
- [ ] All changes committed with signed-off-by
- [ ] PRs reference Gitea issue/modal number
- [ ] Tests pass before merge (where applicable)
- [ ] No hardcoded secrets in commits
### Communication Standards
- [ ] Respond to Timmy Foundation pings within 24 hours
- [ ] Use professional, concise language in issues/PRs
- [ ] Report outages immediately via Telegram/Discord alert channel
## Onboarding & Certification
### Phase 1: Application
- Submit operator application (template: `specs/templates/operator-application.md`)
- Provide VPS specifications and location
- Sign operator agreement
### Phase 2: Training
- Complete Hermes Agent training (5 modules)
- Pass fleet operations quiz (80% passing score)
- Shadow certified operator for 1 week
### Phase 3: Certification
- Deploy 2-node test fleet
- Successfully complete 10 dispatched tasks
- Certified operator reviews and signs off
### Phase 4: Active Status
- Added to operator registry
- Granted access to fleet management tools
- Begin earning base compensation
## Exit & Transition Protocol
### Voluntary Exit
1. Submit 30-day notice via Gitea issue label `exit-notice`
2. Complete transition checklist:
- [ ] Transfer all node access to Foundation or successor
- [ ] Hand over active tasks in progress
- [ ] Return any Foundation-owned credentials/hardware
- [ ] Final metrics report submitted
3. Receive exit payment within 7 days
### Involuntary Termination (for cause)
- Repeated uptime <97% (3 consecutive months)
- Security breach or credential exposure
- Violation of sovereign AI principles
- Unresponsive >72 hours without prior notice
Terminated operators:
- Access revoked immediately
- Final payment pro-rated to last active day
- May reapply after 6 months with improvement plan
### Succession Planning
- Each operator mentors 1 junior operator within first 6 months
- Documentation of all processes in `specs/fleet-ops-runbook.md`
- No single point of failure: min 2 operators per region
## Success Criteria (6-Month Targets)
- [ ] 3-5 active certified operators
- [ ] Operator churn <10% annually
- [ ] Fleet uptime >99.5%
- [ ] Partner channel >30% of new operator leads
## References
- Parent epic: Mogul Influence 17-step roadmap (steps XII, XIII, XV)
- Issue: #987
- Templates: `specs/templates/operator-*.md`
- Runbook: `specs/fleet-ops-runbook.md` (future)

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# Fleet Operations Runbook
*Standard operating procedures for Timmy Foundation fleet operators*
## Daily Checklist
- [ ] Check fleet health: `tmux list-sessions` (should show BURN, BURN2, FORGE active)
- [ ] Verify gateway running: `systemctl status ai.hermes.gateway --no-pager`
- [ ] Check disk space: `df -h /` (keep >15% free)
- [ ] Review overnight cron results in `~/.hermes/cron/jobs/`
## Weekly Tasks
- [ ] Generate fleet metrics report (`scripts/fleet-metrics.sh`)
- [ ] Rotate any expired credentials (check `~/.hermes/fleet-dispatch-state.json`)
- [ ] Review open PRs in Timmy Foundation repos
- [ ] Submit weekly report by Monday 09:00 UTC
## Alert Response Protocol
### Critical (respond <30 min)
1. Gateway down: `sudo systemctl restart ai.hermes.gateway`
2. Disk >90% full: `scripts/cleanup-disk.sh`
3. Fleet dispatch failing: check `/tmp/hermes/dispatch-queue.json`
### Warning (respond <4 hours)
1. Uptime <99.5%: investigate tmux panes with `tmux attach -t BURN`
2. Failed cron jobs: check logs in `~/.hermes/cron/jobs/`
3. Agent loop errors: review session transcripts
## Common Fixes
### Restart stuck tmux pane
```bash
tmux send-keys -t BURN:0 C-c
tmux send-keys -t BURN:0 "hermes chat --yolo" Enter
```
### Clear dispatch queue
```bash
rm /tmp/hermes/dispatch-queue.json
# Watchdog will recreate on next cycle
```
### Update hermes-agent
```bash
cd ~/hermes-agent && git pull origin main && pip install -e ".[all]"
```
## Emergency Escalation
- **Telegram**: @Rockachopa (primary)
- **Gitea Issue**: label `operator-alert` + mention @Rockachopa
- **Discord**: #fleet-ops-alerts channel
## Security Rules
- Never share VPS SSH keys
- Never commit credentials to git
- Rotate tokens every 90 days
- Report suspicious activity immediately
## Contact
- **Operator Handbook**: `specs/fleet-operator-incentives.md`
- **Templates**: `specs/templates/operator-*.md`
- **Foundation Forge**: https://forge.alexanderwhitestone.com/Timmy_Foundation

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# Fleet Operator Application
*Submit completed form as a new Gitea issue with label `operator-application`*
## Personal Information
- **Name / Handle**:
- **Contact Email**:
- **Telegram/Discord Handle**:
- **Wallet Address (USDC/USDT)**:
- **Timezone**:
## Infrastructure
- **VPS Provider**: (e.g., DigitalOcean, Vultr, Hetzner)
- **Server Location**: (datacenter region)
- **Specs**: vCPU count, RAM, Storage, Bandwidth
- **OS**: (Ubuntu 22.04 LTS preferred)
- **Static IP**: Yes / No
## Experience
- [ ] Linux system administration (2+ years)
- [ ] Git / GitHub / Gitea usage
- [ ] Docker / container orchestration
- [ ] AI agent frameworks (Hermes, OpenAI, etc.)
- [ ] Prior VPS fleet management
### Relevant Experience (describe)
*Briefly describe your background with fleet ops, sysadmin, or AI agents:*
## Commitment
- **Hours per week available**:
- **Can maintain 99.5% uptime?** Yes / No
- **Agree to 30-day notice for exit?** Yes / No
- **Agree to sovereign AI principles (no data exfiltration)?** Yes / No
## References
- GitHub/Gitea username:
- Any prior work with Timmy Foundation? (link issues/PRs)
## Acknowledgment
I understand I will start at $150/month base rate, with bonuses available for performance. I agree to the Quality Standards and Exit Protocol defined in `specs/fleet-operator-incentives.md`.
**Signature** (type name): _________________ **Date**: _________
---
*Send completed application to: https://forge.alexanderwhitestone.com/Timmy_Foundation/timmy-home/issues/new*

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# Partner Monthly Report
*Submit by the 5th of each month for commission payments*
## Partner Info
- **Partner Name**:
- **Month/Year**:
- **Wallet Address**:
## Referred Operators
| Operator Handle | Start Date | Monthly Base | Commission (20%) | Status |
|----------------|------------|--------------|-------------------|--------|
| | | $150 | $30 | active / churned |
| | | $150 | $30 | active / churned |
| | | $150 | $30 | active / churned |
**Total Commission Due**: $______
## Mentorship Log
*Confirm you provided mentorship to each referred operator in the first 30 days:*
- [ ] Operator 1: mentored (dates: ____ to ____)
- [ ] Operator 2: mentored (dates: ____ to ____)
- [ ] Operator 3: mentored (dates: ____ to ____)
## Partner Performance
- Total active operators referred:
- Average operator uptime this month: ______%
- Any operator churn? Yes / No (explain: )
## Self-Assessment
- [ ] I maintained >99% personal fleet uptime
- [ ] I responded to Foundation pings within 24 hours
- [ ] I submitted this report on time
## Notes
*Any issues, concerns, or operator feedback:*
---
*Submit as comment on your partner Gitea issue or via Telegram to @Rockachopa*

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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 +1,19 @@
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
DOC = ROOT / "docs" / "issue-536-verification.md"
def test_issue_536_verification_doc_exists_and_points_to_real_artifacts() -> None:
assert DOC.exists(), "missing docs/issue-536-verification.md"
text = DOC.read_text(encoding="utf-8")
for snippet in (
"# Issue #536 Verification",
"evennia_tools/bezalel_layout.py",
"scripts/evennia/build_bezalel_world.py",
"tests/test_bezalel_evennia_layout.py",
"docs/BEZALEL_EVENNIA_WORLD.md",
"portal commands",
"already implemented on `main`",
):
assert snippet in text

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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!")