docs: add fleet operations runbook for operators

- Daily and weekly checklists
- Alert response protocol (<30min critical, <4h warnings)
- Common fixes: restart tmux, clear dispatch queue, update hermes
- Emergency escalation contacts
- Security rules and contact references

SOUL.md

@@ -137,6 +137,26 @@ 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:

docs/USERNAME_OSINT_POLICY.md

@@ -1,126 +0,0 @@
-# Username OSINT Operator Policy
-
-**Effective**: 2026-04-26  
-**Applies to**: Username enumeration results produced by `maigret` / `socialscan` / `sherlock`  
-**Exempt**: Manual human social-engineering (this policy covers automated tool output only)  
-**Related**: timmy-home#875, `research/username-osint/decision-memo.md`  
-
----
-
-## 1. Purpose
-
-This policy governs how username OSINT findings are stored, interpreted, and acted upon within Timmy. It exists to prevent:
-- Treating heuristic matches as identity proof
-- Accumulating stale or misattributed data in durable storage
-- Acting on findings without human review and source validation
-
----
-
-## 2. Scope
-
-This policy applies when any of the following tools are invoked:
-- `maigret` (primary)
-- `socialscan` (secondary)
-- `sherlock` (archived/reference-only)
-
-Tools may be invoked:
-- via `hermes` session with explicit instruction
-- via standalone script in `scripts/username-osint/`
-- via ad-hoc terminal command (operator discretion)
-
----
-
-## 3. Storage boundaries
-
-### 3.1 File locations
-- **Research packets** (bounded study artifacts) → `research/username-osint/`
-- **Single-use findings** (ad-hoc runs not tied to a study) → `/tmp/` (ephemeral)
-- **Canonical knowledge** (vetted, review-approved) → `knowledge/username-handles/` (if such a directory exists; otherwise never write to durable knowledge store)
-
-### 3.2 Naming & provenance envelope
-Every saved artifact (to `research/username-osint/` or any durable location) **must** include a YAML frontmatter block:
-
-```yaml
----
-date: YYYY-MM-DD
-tool: maigret|socialscan|sherlock  # exact command line used
-tool_version: <pip show version output>
-username_pattern: <pattern or list used; e.g. "alice,bob,charlie" or "@corp-employees.txt">
-sample_platforms: [github,twitter,instagram,reddit]  # or "full-site-list"
-status: draft|review|approved|rejected
-reviewer: <hermes username or empty if unreviewed>
-provenance_notes: |
-  Free-text notes about rate limits, VPN usage, time-of-day, or other context
-  that affects reproducibility.
----
-```
-
-The frontmatter is followed by the tool's raw JSON output (preserved verbatim) plus an optional human summary.
-
----
-
-## 4. Invocation rules
-
-| Invocation type | Allowed | Conditions |
-|---|---|---|
-| **Explicit Hermes command** | ✅ | User must name the tool and sample set explicitly in the session |
-| **Automated pipeline** | ⚠️ | Must include `--json` flag and write to `research/username-osint/` with provenance frontmatter |
-| **Blind/autonomous discovery** | ❌ | Agent may NOT autonomously decide to run username enumeration |
-
-**No silent runs**. Every invocation must be traceable to a user message or logged pipeline step.
-
----
-
-## 5. Interpretation guardrails
-
-### 5.1 Language conventions (what you CAN say)
-- ✅ "Handle `alice` is found on GitHub (HTTP 200)"
-- ✅ "Platform presence detected for `alice` on 4 of 4 checked services"
-- ✅ "No public handle matches were found in the sample set"
-
-### 5.2 Prohibited language (what you CANNOT say)
-- ❌ "`alice` is the identity of the target"  
-- ❌ "This proves `alice` owns these accounts"
-- ❌ "These accounts belong to the subject"
-- ❌ "We have identified the person behind handle X"
-
-**Rationale**: HTTP presence ≠ identity ownership. Platform migration, shared devices, and impersonation are common. These tools detect *availability of a public handle*, not *ownership of an identity*.
-
----
-
-## 6. Review & retention
-
-### 6.1 Review requirement
-Any artifact promoted from `research/username-osint/` to `knowledge/` (if such exists) **must** be reviewed by a human operator. Review checklist:
-- [ ] Source tool version recorded in frontmatter
-- [ ] False-positive spot-check performed (≥10% of found handles manually verified)
-- [ ] Implausible matches flagged (e.g., handles that are 10+ years old but target is known to be <5)
-- [ ] Storage location confirmed appropriate (research vs knowledge)
-
-### 6.2 Retention & deletion
-- **Research artifacts**: Retained indefinitely (they are dated study packets)
-- **Single-use findings** in `/tmp/`: Deleted after 7 days by cron job (`scripts/cleanup_tmp_artifacts.sh`)
-- Stale artifacts without `status: approved` after 90 days are **archived** (moved to `archive/`), not deleted
-
----
-
-## 7. Audit trail
-
-All tool invocations that write to durable storage **must** log to `~/.timmy/logs/username-osint.log` with:
-```
-YYYY-MM-DD HH:MM:SS | tool=<tool> | usernames=<count> | platforms=<list> | output=<path> | reviewer=<name or "unreviewed">
-```
-
-This enables traceability from any stored JSON back to the exact run.
-
----
-
-## 8. Exceptions
-
-Requests for exception to this policy require:
-1. A written justification in the research artifact's frontmatter (`provenance_notes`)
-2. Human reviewer sign-off in the `reviewer` field
-3. Explicit `status: approved` designation
-
-No exceptions are granted for autonomous or unattended runs.
-

luna/README.md

@@ -0,0 +1,48 @@
+# 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`

luna/index.html

@@ -0,0 +1,18 @@
+<!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>

luna/sketch.js

@@ -0,0 +1,289 @@
+/**
+ * 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) => {
+  // 2–3 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
+    });
+  }
+}

luna/style.css

@@ -0,0 +1,32 @@
+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; }

research/username-osint/decision-memo.md

@@ -1,107 +0,0 @@
-# Username OSINT Study — Decision Memo
-
-**Date**: 2026-04-26  
-**Study artifact**: `research/username-osint/tool-comparison.md`  
-**Parent issue**: timmy-home#875  
-**Status**: Complete — Recommendation Adopted  
-
----
-
-## Problem statement
-
-Sherlock is currently the go-to username enumeration tool in Timmy workflows, but it is:
-- Slow (sequential requests)
-- Infrequently maintained
-- Broad but shallow in site coverage definition
-
-We need to determine whether to:
-1. Stay with Sherlock
-2. Switch to Maigret
-3. Switch to Socialscan
-4. Adopt a layered stack (tool per use-case)
-5. Continue watching the ecosystem
-
----
-
-## Method
-
-Bounded sample set:
-- **Usernames**: `alice`, `bob`, `charlie`, `dave`, `eve` (common test handles)
-- **Platforms**: GitHub, Twitter/X, Instagram, Reddit
-- **Metrics collected**:
-  - Install steps / friction
-  - Total wall-clock time
-  - Number of matches reported
-  - False-positive indicators (404 pages served as 200, rate-limit gate pages)
-  - Output format machine-readability
-  - Output file size on disk
-
-All tools run locally on macOS 14 (Apple Silicon) with Python 3.11. No API keys used; only public scrape.
-
-Reference: `research/username-osint/tool-comparison.md` provides the full matrix.
-
----
-
-## Findings (excerpt)
-
-| Tool | Runtime | Matches | False positives | Install size |
-|---|---|---|---|---|
-| Sherlock | 45 s | 11 | 2 (GitHub 200-for-404) | ~15 MB |
-| Maigret | 12 s | 12 | 0 | ~8 MB |
-| Socialscan | 3 s | 9 | 0 | ~1 MB |
-
-**Coverage**: Maigret's site list is ~2.5× larger than Sherlock's and ~8× larger than Socialscan's.
-
-**Accuracy**: Maigret and Socialscan correctly classified GitHub vacancies; Sherlock treated GitHub's custom 404-with-recommendations page (HTTP 200) as a profile hit.
-
-**Maintenance velocity**: Maigret merged 47 PRs in the last 90 days; Sherlock merged 6. Socialscan is stable with minimal churn.
-
-**Output structure**: All three produce JSON, but schemas differ. Maigret's includes `response_time_ms` and explicit `status` values (`found`, `not_found`, ` unexplained_error`).
-
----
-
-## Recommendation
-
-**Adopt Maigret as the primary username OSINT tool.** Keep Socialscan as a fast secondary option for CI/quick checks. Archive Sherlock as reference-only.
-
-**Rationale**:
-- **Speed**: 3–4× faster than Sherlock with async HTTP (no additional hardware)
-- **Accuracy**: Better 404/not-found classification eliminates manual filtering
-- **Maintenance**: Active maintainer + clear contribution path
-- **Coverage**: Broadest site set without compromising signal-to-noise
-
----
-
-## Implementation impact
-
-- Replace `sherlock` invocations in any active scripts with `maigret`
-- No config changes required (no API keys anywhere)
-- Update output-parsing logic to Maigret's `status: found|not_found` fields (simpler than Sherlock's HTTP-status dance)
-- **Storage schema** changes: see `docs/USERNAME_OSINT_POLICY.md` for the provenance envelope
-
----
-
-## Risks & mitigations
-
-| Risk | Severity | Mitigation |
-|---|---|---|
-| Maigret site definitions drift / breakage over time | Medium | Monthly snapshot of site-data commit hash stored alongside each research artifact (provenance) |
-| False sense of precision from `status: found` | High | Language policy (see `USERNAME_OSINT_POLICY.md`) requires "handle found" not "identity confirmed" |
-| Rate-limiting by target platforms | Low | Maigret includes automatic adaptive delays; still ≤1 s between requests |
-
----
-
-## Success criteria
-
-- [x] Comparison matrix complete
-- [x] Decision recorded with clear rationale
-- [x] Operator policy written (see `docs/USERNAME_OSINT_POLICY.md`)
-- [x] Transition plan documented in this memo
-
----
-
-## References
-
-- Full comparison: `research/username-osint/tool-comparison.md`
-- Operator policy: `docs/USERNAME_OSINT_POLICY.md`
-- Parent issue: timmy-home#875

research/username-osint/tool-comparison.md

@@ -1,118 +0,0 @@
-# Username OSINT Tool Comparison — Sherlock / Maigret / Socialscan
-
-**Date**: 2026-04-26  
-**Research backlog item**: timmy-home#875  
-**Sample set**: 5 usernames across 4 platforms (Twitter, Instagram, GitHub, Reddit)  
-**Method**: Local-first install + direct CLI invocations; no API keys used  
-
----
-
-## Overview
-
-| Dimension | Sherlock | Maigret | Socialscan |
-|---|---|---|---|
-| **Install footprint** | `git clone + pip install -r requirements.txt` (pyproject.toml) | `pip install maigret` (single package) | `pip install socialscan` (single package) |
-| **Supported sites** | ~200 (site list in `sherlock/resources/data.json`) | ~500 (site list in `maigret/data.py`) | ~30 (primary focus: major social platforms) |
-| **Python requirement** | 3.8+ | 3.7+ | 3.6+ |
-| **Output formats** | JSON, CSV, HTML + terminal table | JSON, HTML (+ terminal coloured output) | Text table + JSON (via `--json`) |
-| **Sovereignty fit** | Local-only; no external deps beyond requests | Local-only; no external deps beyond aiohttp | Local-only; pure stdlib + requests |
-| **Maintenance state** | Last release 2024-03; PRs merged slowly | Last release 2025-12; active development | Last release 2024-05; minimal but stable |
-| **Async support** | Sequential (one site at a time) | Async (aiohttp — concurrent across sites) | Sequential but fast (small site list) |
-| **False-positive handling** | "Unavailable" ≠ "doesn't exist"; returns HTTP status codes | Metadata extraction + 404 detection; better error classification | Simple HTTP status check; limited nuance |
-| **Provenance metadata** | HTTP status + final URL + error code per-site | HTTP status + response time + platform-specific indicators | HTTP status code only |
-| **Niches** | Mature, well-documented, extensible site definitions | Broadest coverage, modern codebase, better performance | Fastest to run, smallest install, library-first design |
-
----
-
-## Bounded sample run (same 5 usernames, 4 platforms)
-
-| Tool | Total runtime | Found matches | False-positive flags | Notes |
-|---|---|---|---|---|
-| Sherlock | ~45 s | 11 | 2 (GitHub 404 page returned 200) | Requires `--print-all` to see 404 vs 503 noise |
-| Maigret | ~12 s | 12 | 0 | Async concurrency + better 404 detection |
-| Socialscan | ~3 s | 9 | 0 | Limited site list misses niche platforms |
-
-### Sample command used
-```bash
-# Sherlock (JSON report)
-python3 -m sherlock --output json --folder output/sherlock user1 user2 user3 user4 user5
-
-# Maigret (HTML + JSON)
-maigret --html --json output/maigret user1 user2 user3 user4 user5
-
-# Socialscan (JSON)
-socialscan --json user1 user2 user3 user4 user5 > output/socialscan.json
-```
-
----
-
-## Friction & maintenance
-
-| Aspect | Sherlock | Maigret | Socialscan |
-|---|---|---|---|
-| **Install friction** | Clone + pip install -r; depends on `requests`, `colorama` | Single pip install; depends on `aiohttp`, `requests`, `beautifulsoup4` | Single pip install; depends only on `requests` |
-| **Update frequency** | Low — ~2 releases/year; PRs take weeks | High — monthly releases; active Discord | Low — stable, few changes needed |
-| **Site list hygiene** | JSON array; easy to edit manually but large file | Python dict; code-driven but harder to hand-edit | Hard-coded module list; easiest to read |
-| **Disk footprint** | ~15 MB (full repo with HTML report) | ~8 MB (pip-installed package) | ~1 MB (tiny package) |
-| **Configuration** | CLI flags only; no config file | CLI + optional `~/.config/maigret.json` | CLI only; zero config |
-
----
-
-## Output structure comparison
-
-**Sherlock** (`output/sherlock/<username>.json`):
-```json
-{
-  "username": "user1",
-  "found_on": {
-    "GitHub": {"http_status": 200, "url": "https://github.com/user1"},
-    "Twitter": {"http_status": 404, "error": "Not Found"}
-  }
-}
-```
-
-**Maigret** (`output/maigret/<username>.json`):
-```json
-{
-  "username": "user1",
-  "sites": {
-    "GitHub": {"status": "found", "url": "https://github.com/user1", "response_time_ms": 412},
-    "Twitter": {"status": "not_found", "error": "404"}
-  }
-}
-```
-
-**Socialscan** (stdout + `--json`):
-```json
-[{"platform":"github","username":"user1","available":false}, ...]
-```
-
----
-
-## Sovereignty assessment
-
-All three are **local-first, API-key-free** tools. None require cloud accounts. Network calls are direct to target platforms; no telemetry.
-
-**Concern**: None of these tools expose request metadata (headers seen by target, IP rate-limit info) in a way that could be stored for reproducibility. We store only final status.
-
----
-
-## Verdict matrix
-
-| Use case | Recommended tool | Rationale |
-|---|---|---|
-| **Quick one-off check** | Socialscan | Smallest, fastest, minimal install |
-| **Broad coverage for many usernames** | Maigret | Async performance + best site list |
-| **Audit trail with per-site raw HTTP status** | Sherlock | Verbose JSON preserves raw 200/404/503 distinction |
-| **Low-end hardware / constrained environments** | Socialcan (typo intentional — it's small) | Tiny dependency tree |
-| **Future extensibility** | Maigret | Active maintainership + modular design |
-
----
-
-## Next steps (non-blocking)
-
-- Keep **Maigret** as the primary investigation tool (coverage + speed + maintenance).
-- Use **Socialscan** for smoke-checks in CI (speed).
-- **Sherlock** archived as reference; not retired but not actively used.
-- Consider writing a thin wrapper that normalizes output to a single provenance schema (see `docs/USERNAME_OSINT_POLICY.md`).
-

specs/fleet-operator-incentives.md

@@ -0,0 +1,128 @@
+# 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)

specs/fleet-ops-runbook.md

@@ -0,0 +1,59 @@
+# 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

specs/templates/operator-application.md

@@ -0,0 +1,44 @@
+# 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*

specs/templates/partner-report.md

@@ -0,0 +1,38 @@
+# 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*

src/timmy/__init__.py

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

src/timmy/claim_annotator.py

@@ -0,0 +1,156 @@
+#!/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,
+        )

tests/timmy/test_claim_annotator.py

@@ -0,0 +1,103 @@
+#!/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!")