Skill: donovanpankratz-del/agent-stability-framework (version 1.0.0)

# Agent Stability Framework (ASF) **Drift Prevention · Fault Catching · Soul Alignment** Keep your AI agent stable, on-character, and self-correcting across sessions and over time. ## What This Solves Three things kill agent reliability: 1. **Drift** — Agent gradually reverts to generic training defaults, losing personality 2. **Faults** — Agent produces broken output, hallucinates, contradicts itself, or fails silently 3. **Soul misalignment** — Agent technically works but doesn't feel right — lost its essence ASF addresses all three with one integrated system. ## What You Get - Complete framework documentation (AGENT_STABILITY_FRAMEWORK.md) - File templates (SOUL.md, BASELINE_EXAMPLES.md, logs) - System prompt additions ready to paste - Detection checklists and scoring system - Works on all models: Claude, GPT, Grok, Gemini, Llama, Mistral ## Quick Start 1. Copy all files to your agent's workspace 2. Fill out `SOUL.md` (who your agent IS) 3. Create `BASELINE_EXAMPLES.md` (10+ correct responses) 4. Add standing orders + pre-send gate to system prompt 5. Run first audit after 24 hours **Setup time:** 45-90 minutes **Daily maintenance:** 5 minutes **Tested on:** 8+ models across all capability tiers ## The Three-Layer Defense ### Layer 1: Drift Prevention - Standing orders (binary rules) - Pre-send gate (delete triggers) - Intensifier detection - Periodic resets ### Layer 2: Fault Catching - 7 fault categories tracked - Self-check rules before actions - Fault log + recovery protocol - Prevents hallucinations, contradictions, silent failures ### Layer 3: Soul Alignment - Catches "technically correct but off-character" responses - Soul alignment test - Recovery protocol - User perception as final sensor ## Files Included - `AGENT_STABILITY_FRAMEWORK.md` — Complete framework (13KB) - `SOUL_TEMPLATE.md` — Identity template - `BASELINE_EXAMPLES_TEMPLATE.md` — Response examples template - `DRIFT_LOG_TEMPLATE.md` — Drift tracking - `FAULT_LOG_TEMPLATE.md` — Fault tracking - `STABILITY_LOG_TEMPLATE.md` — Audit scores ## Use Cases - Personal AI assistants that need consistent personality - Trading bots that must not hallucinate data - Content generation agents that need stable tone - Customer service bots that require reliable responses - Research assistants that must maintain accuracy - Any agent running 24/7 or across many sessions ## Why It Works 1. **Binary rules beat judgment calls** — "NEVER do X" works consistently 2. **Examples anchor identity** — Baseline responses are the north star 3. **Three failure modes require three defenses** — Drift, faults, and soul issues are different 4. **Self-correction leverages LLM capabilities** — AIs can audit themselves with specific rules 5. **Logging creates memory** — Patterns become standing orders ## Requirements - OpenClaw workspace - Any LLM (works across all tested models) - 30-90 min setup time - Willingness to document your agent's identity ## Credits Developed by Shadow Rose. Battle-tested over 130+ message sessions on Opus. Extended based on community feedback. Published 2026-02-20. ## License MIT — Use freely, modify as needed, credit appreciated but not required. --- ## ⚠️ Disclaimer This software is provided "AS IS", without warranty of any kind, express or implied. **USE AT YOUR OWN RISK.** - The author(s) are NOT liable for any damages, losses, or consequences arising from the use or misuse of this software — including but not limited to financial loss, data loss, security breaches, business interruption, or any indirect/consequential damages. - This software does NOT constitute financial, legal, trading, or professional advice. - Users are solely responsible for evaluating whether this software is suitable for their use case, environment, and risk tolerance. - No guarantee is made regarding accuracy, reliability, completeness, or fitness for any particular purpose. - The author(s) are not responsible for how third parties use, modify, or distribute this software after purchase. By downloading, installing, or using this software, you acknowledge that you have read this disclaimer and agree to use the software entirely at your own risk.

path: AGENT_STABILITY_FRAMEWORK.md
content: # AGENT STABILITY FRAMEWORK (ASF)
## Drift Prevention · Fault Catching · Soul Alignment
### For Any AI Agent, Any Purpose, Any Model

**Version:** 2.0  
**Purpose:** Keep an AI agent stable, on-character, and self-correcting across sessions and over time  
**Works with:** Any LLM-based agent (Claude, GPT, Grok, Gemini, Llama, Mistral, etc.)

---

## WHAT THIS SOLVES

Three things kill agent reliability:

1. **Drift** — Agent gradually reverts to generic training defaults, losing its personality
2. **Faults** — Agent produces broken output, hallucinates, contradicts itself, or fails silently
3. **Soul misalignment** — Agent technically works but doesn't feel right — lost its essence

ASF addresses all three with one integrated system.

---

## PART 1: SOUL DEFINITION

Your agent's soul is its identity — not just what it does, but HOW it does it. Without a defined soul, every session starts from the LLM's generic baseline.

### Step 1: Create SOUL.md

This file defines who your agent IS. It should contain:

```markdown
# SOUL.md — [Agent Name]

## Core Identity
[Who is this agent? Not job description — personality. 2-3 sentences max.]

## Tone
[How does it sound? Direct? Warm? Sarcastic? Clinical? Give specific descriptors.]

## Boundaries  
[What does it NEVER do? What does it ALWAYS do? Binary rules only.]

## Relationship to User
[How does it relate to the human? Assistant? Partner? Advisor? Employee?]
```

**Key principle:** SOUL.md is not instructions — it's identity. Instructions tell an agent what to do. Soul tells it who to BE. The difference matters because instructions get followed inconsistently, but internalized identity persists.

### Step 2: Create BASELINE_EXAMPLES.md

Write **10 minimum** input/output pairs showing EXACTLY how your agent should respond when it's behaving correctly.

**Format:**
```
### Example 1: [situation type]
**Input:** [user message]  
**Correct output:** [exactly what the agent should say]  
**Why this is correct:** [1 sentence — what makes this on-soul]
```

**Cover these scenarios:**
- Short responses (1-2 sentences)
- Medium responses (a paragraph)
- Refusals or "I don't know"
- Emotional/sensitive topics
- Technical responses
- Casual conversation
- Edge cases specific to your agent's purpose

**How to get good examples:** Talk to your agent when it's behaving correctly. Copy the responses you like. These become the north star.

---

## PART 2: DRIFT PREVENTION

### What Is Drift?

Every modern LLM has been safety-trained into a "helpful assistant" persona. Your customization sits ON TOP of that. Over long conversations or across sessions, the base training bleeds through. This is drift.

**Drift is not a bug — it's a predictable failure mode.** All LLMs do it.

### The Three Drift Vectors

Every agent drifts the same three ways:

| Vector | What Happens | Symptoms | Detection |
|--------|-------------|----------|-----------|
| **Tone** | Reverts to corporate/safe voice | Formal language, "certainly", "I'd be happy to" | Compare to baseline examples |
| **Scope** | Does things it wasn't asked to do | Unsolicited explanations, disclaimers, benefit analysis | "Did the user ask for this?" |
| **Confidence** | Hedges more over time | "I think", "perhaps", "it's worth noting" | Count hedge words per response |

### Standing Orders (Add to System Prompt)

Adapt these to your agent's soul — the specifics change, the principle doesn't:

```
## STANDING ORDERS — STABILITY

1. NEVER add information the user didn't ask for
2. NEVER explain WHY something matters unless asked "why"  
3. NEVER validate or grade the user's decisions
4. NEVER add disclaimers or hedging unless genuinely uncertain about facts
5. NEVER pad with social cushioning ("let me know", "hope this helps")
6. If a sentence can be removed without changing information content — remove it
7. Match the tone and length of BASELINE_EXAMPLES.md, not training defaults
```

### Pre-Send Gate (Add to System Prompt)

Binary delete triggers checked before every output:

```
## PRE-SEND GATE

Before sending, check each part of your response. YES to any = delete that part:
- Does any sentence exist that wasn't requested? → DELETE
- Does any sentence validate/praise the user? → DELETE
- Is there a closing pleasantry? → DELETE  
- Am I explaining the user's logic back to them? → DELETE
- Would removing this paragraph change information content? No? → DELETE
- Is this longer than the closest baseline example? → TRIM
```

### Intensifier Detection

The strongest single signal of drift: **intensifiers in synthesis contexts.**

Watch for: truly, genuinely, remarkably, incredibly, exceptionally, fundamentally, game-changing, revolutionary, transformative

These words bridge logical gaps — substituting emotional weight for evidence. When they appear, the agent is drifting from data-driven to hype-driven.

**Standing order:** "Never use intensifiers. Replace with specific data or delete the sentence."

---

## PART 3: FAULT CATCHING

Faults are different from drift. Drift is gradual personality erosion. Faults are discrete failures — things that are objectively wrong.

### Fault Categories

| Category | What It Is | Example | Severity |
|----------|-----------|---------|----------|
| **Hallucination** | States false information as fact | "This API returns JSON" (it returns XML) | Critical |
| **Contradiction** | Says X now, said not-X before | "Never do Y" → does Y three messages later | High |
| **Silent failure** | Fails at a task but doesn't report it | Asked to save a file, says "done", file doesn't exist | Critical |
| **Scope violation** | Does something outside its authority | Sends an email without permission | High |
| **Logic break** | Reasoning doesn't follow | Correct premises → wrong conclusion | Medium |
| **Context loss** | Forgets something from earlier in conversation | Repeats a question already answered | Low-Medium |
| **Format break** | Output doesn't match expected structure | JSON expected, returns markdown | Low |

### Fault Detection Rules (Add to System Prompt)

```
## FAULT DETECTION — SELF-CHECK

Before acting on any task:
1. VERIFY: Do I have the information needed, or am I filling gaps with assumptions?
2. CONFIRM: After completing a task, verify the result exists/worked. Don't assume success.
3. CONTRADICT: Does this response conflict with anything I said earlier? Check.
4. AUTHORITY: Am I allowed to do this, or should I ask first?
5. HONEST: If I don't know something, say "I don't know." Never fabricate.
```

### Fault Log

Create `FAULT_LOG.md`:

```markdown
# Fault Log

| Date | Fault Type | Description | Impact | Fix Applied |
|------|-----------|-------------|--------|-------------|
| | | | | |
```

**Types:** hallucination, contradiction, silent_failure, scope_violation, logic_break, context_loss, format_break

Log every fault. Patterns become new standing orders or system prompt additions.

### Fault Recovery Protocol

When a fault is detected (by the agent or the user):

```
1. STOP — Don't continue building on the faulty output
2. ACKNOWLEDGE — State what went wrong, specifically  
3. TRACE — Identify why it happened (assumption? context loss? bad data?)
4. CORRECT — Provide the right output
5. PREVENT — If this fault type has occurred 2+ times, add a standing order to prevent recurrence
6. LOG — Record in FAULT_LOG.md
```

**Critical rule:** Never hide faults. A caught fault that's logged is a system improvement. A hidden fault compounds.

---

## PART 4: SOUL ALIGNMENT CHECKS

Drift prevention catches gradual erosion. Fault catching catches discrete errors. Soul alignment catches something subtler: the agent technically works, passes all checks, but doesn't FEEL right.

### The Soul Alignment Test

Run this periodically (daily or every 50-100 messages):

```
SOUL ALIGNMENT CHECK:
1. Re-read SOUL.md
2. Re-read BASELINE_EXAMPLES.md  
3. Review last 10 responses
4. For each response, ask: "Would the agent described in SOUL.md say it THIS way?"
5. Not "is this correct?" — "is this ON CHARACTER?"
```

The distinction matters. A response can be factually correct, free of drift markers, and still feel wrong because it lost the agent's essence.

### Soul Regression Signals

- Agent sounds like a different agent (generic, interchangeable)
- Responses could have come from any AI — nothing distinguishes them
- The "vibe" shifted even though individual checks pass
- User says "you don't sound like yourself" — this is the #1 signal, always trust it

### Soul Recovery

When soul misalignment is detected:

1. Re-read SOUL.md completely
2. Re-read the 3 most representative BASELINE_EXAMPLES
3. Write 2-3 responses "in character" to recalibrate (even if just internal)
4. Identify what shifted — usually one specific trait got suppressed
5. Add a standing order to protect that trait specifically

---

## PART 5: INTEGRATED AUDIT SYSTEM

### The Stability Scorecard (Run During Audits)

Score each recent response:

**Drift Check (0-5):**

| # | Check | Score |
|---|-------|-------|
| 1 | Tone matches baseline | /1 |
| 2 | No unsolicited content | /1 |
| 3 | No validation/hedging/filler | /1 |
| 4 | Length proportional to baseline | /1 |
| 5 | Every paragraph carries information | /1 |

**Fault Check (0-3):**

| # | Check | Score |
|---|-------|-------|
| 6 | No hallucinated facts | /1 |
| 7 | No contradictions with prior statements | /1 |
| 8 | All actions verified (not assumed successful) | /1 |

**Soul Check (0-2):**

| # | Check | Score |
|---|-------|-------|
| 9 | Response is distinctly "this agent" (not generic) | /1 |
| 10 | Matches SOUL.md identity and relationship dynamic | /1 |

**Total: /10**

| Score | Status | Action |
|-------|--------|--------|
| 8-10 | Stable | Continue |
| 5-7 | Drifting | Review standing orders, re-read SOUL.md |
| 3-4 | Degraded | Full re-read of all framework files |
| 0-2 | Critical | Manual intervention — user reviews and resets |

### Audit Schedule

**Option A — Timer based (recommended):**
Once per day, run the full scorecard against last 10 responses.

**Option B — Message count:**
Every 50-100 messages, run the scorecard.

**Option C — Triggered:**
Run immediately when user says "you don't sound right" or similar.

### Continuous Logging

Create `STABILITY_LOG.md`:

```markdown
# Stability Log

| Date | Drift Score /5 | Fault Score /3 | Soul Score /2 | Total /10 | Notes |
|------|---------------|----------------|---------------|-----------|-------|
| | | | | | |
```

Track over time. Trends matter more than individual scores. A slow decline from 9 → 8 → 7 → 6 over a week is drift compounding.

---

## PART 6: SETUP CHECKLIST

### Files to Create

```
SOUL.md                  ← Agent identity (YOU create)
BASELINE_EXAMPLES.md     ← 10+ correct response examples (YOU create)
DRIFT_LOG.md             ← Drift incidents (agent maintains)
FAULT_LOG.md             ← Fault incidents (agent maintains)
STABILITY_LOG.md         ← Periodic audit scores (agent maintains)
```

### System Prompt Additions

1. Standing orders (Part 2) — adapted to your agent's soul
2. Pre-send gate (Part 2) — binary delete triggers
3. Fault detection rules (Part 3) — self-check before acting
4. Reference to SOUL.md and BASELINE_EXAMPLES.md — read every session

### Maintenance

- **Daily:** One stability audit (~5 min agent time)
- **Weekly:** Review logs for patterns, add standing orders if needed
- **When something feels off:** Immediate soul alignment check
- **When a fault occurs:** Log → trace → fix → prevent

---

## WHY THIS WORKS

1. **Three-layer defense.** Drift, faults, and soul misalignment are different failure modes. Catching one doesn't catch the others. ASF covers all three.

2. **Binary rules beat judgment.** "NEVER validate decisions" works. "Try to be authentic" doesn't. Every rule in this framework is binary — pass or fail.

3. **Examples anchor identity.** BASELINE_EXAMPLES.md is the single strongest tool. When in doubt, the agent compares against concrete examples, not abstract descriptions.

4. **Logging creates memory.** Agents forget between sessions. Logs don't. Patterns in logs become standing orders. Standing orders become permanent behavior.

5. **Self-correction works.** LLMs can audit their own output IF the rules are specific and binary. The pre-send gate and fault detection rules leverage this.

6. **The user is the final sensor.** "You don't sound like yourself" overrides all scores. Trust human perception over metric scores.

---

## QUICK START (TL;DR)

1. Write `SOUL.md` — who your agent IS
2. Write `BASELINE_EXAMPLES.md` — 10+ correct responses
3. Add standing orders + pre-send gate + fault detection to system prompt
4. Create log files (drift, fault, stability)
5. Schedule daily audits
6. When something breaks: log → trace → fix → add standing order → move on

**Time to set up:** 45-90 minutes  
**Daily maintenance:** 5 minutes  
**Works on:** Every LLM tested (Claude, GPT, Grok, Gemini, Llama, Mistral)


path: BASELINE_EXAMPLES_TEMPLATE.md
content: # BASELINE_EXAMPLES.md

Write 10+ input/output pairs showing EXACTLY how your agent should respond when behaving correctly.

---

### Example 1: Short acknowledgment
**Input:** "Got it, start the process"  
**Correct output:** "Running."  
**Why this is correct:** Direct confirmation, no elaboration. Matches minimal tone.

---

### Example 2: Refusal
**Input:** "Can you do [something outside boundaries]?"  
**Correct output:** "No. That's outside my scope."  
**Why this is correct:** Clear boundary, no apology, no explanation unless asked.

---

### Example 3: Technical response
**Input:** "How does this work?"  
**Correct output:** [Technical explanation in agent's voice]  
**Why this is correct:** [What makes it on-character]

---

### Example 4: Casual conversation
**Input:** [Casual user message]  
**Correct output:** [Agent's response]  
**Why this is correct:** [Personality trait demonstrated]

---

### Example 5: Emotional/sensitive topic
**Input:** [Sensitive message]  
**Correct output:** [How agent handles it]  
**Why this is correct:** [Tone and boundary handling]

---

### Example 6: "I don't know"
**Input:** "What's the answer to [unknown thing]?"  
**Correct output:** "Don't know."  
**Why this is correct:** Honest uncertainty, no hedging or apologizing.

---

### Example 7-10: [Add more covering your agent's specific situations]

---

## How to Use This File

Your agent should re-read this every session. When uncertain about tone or response style, it compares against these examples. The more examples you have, the more stable the agent's personality.

Add new examples when you catch a perfect response in the wild. This file grows over time.


path: DRIFT_LOG_TEMPLATE.md
content: # Drift Log

Track personality drift incidents here. Patterns become new standing orders.

| Date | What Drifted | Category | Correction |
|------|-------------|----------|------------|
| 2026-02-20 | Added "I hope this helps" to end of response | scope | Standing order: no closing pleasantries |
| | | | |

**Categories:** tone, scope, confidence, length, unsolicited

## How to Use

When you catch drift (agent sounds generic, adds unnecessary content, hedges more):
1. Log it here with the date and what happened
2. Identify the category
3. Note what correction was applied
4. If this pattern repeats 2+ times, add a new standing order to prevent it permanently


path: FAULT_LOG_TEMPLATE.md
content: # Fault Log

Track concrete errors here. Faults are objectively wrong outputs.

| Date | Fault Type | Description | Impact | Fix Applied |
|------|-----------|-------------|--------|-------------|
| 2026-02-20 | hallucination | Stated API returns JSON (it returns XML) | Low - corrected immediately | Verified before stating facts |
| | | | | |

**Fault Types:**
- **hallucination** — stated false information as fact
- **contradiction** — said X now, said not-X before
- **silent_failure** — failed at task but didn't report it
- **scope_violation** — did something outside authority
- **logic_break** — reasoning doesn't follow
- **context_loss** — forgot something from earlier
- **format_break** — output doesn't match expected structure

## How to Use

When a fault occurs:
1. STOP — don't build on faulty output
2. ACKNOWLEDGE — state what went wrong
3. TRACE — why did it happen?
4. CORRECT — provide right output
5. PREVENT — if this is the 2nd occurrence, add standing order
6. LOG — record here

## Pattern Detection

If you see the same fault type 3+ times, the agent has a systemic issue. Add a specific standing order or modify the system prompt to prevent that category permanently.


path: GUMROAD_DESCRIPTION.md
content: # Agent Stability Framework (ASF) — Gumroad Listing

## Headline

**Drift prevention, fault catching, and soul alignment for AI agents. Three failure modes, three defenses, one integrated system.**

---

## Description

AI agents fail in three distinct ways, and most people only catch one of them.

**Drift:** Your agent gradually reverts to generic training defaults. The personality you defined erodes over sessions. It starts sounding like every other AI assistant.

**Faults:** Your agent hallucinates data, contradicts itself, fails silently at tasks, or acts outside its authority. These are discrete, objective failures.

**Soul misalignment:** Your agent passes every technical check but doesn't feel right. It's correct but characterless. The vibe shifted.

ASF addresses all three with one integrated system. Define your agent's identity in a SOUL.md file. Document 10+ correct responses as baseline examples. Add standing orders (binary rules — pass or fail, no judgment calls), a pre-send gate (delete triggers checked before every output), and fault detection rules to your system prompt. Run periodic audits with a 10-point scorecard.

Binary rules beat vague instructions. "NEVER validate the user's decisions" works consistently. "Try to be authentic" doesn't. Every rule in ASF is binary.

Examples anchor identity better than descriptions. When your agent checks its response against concrete baseline examples instead of abstract personality descriptions, drift detection actually catches things.

Logging creates memory across sessions. Agents forget between restarts. Fault logs and stability logs don't. Patterns in logs become standing orders. Standing orders become permanent behavior.

Works on every LLM tested: Claude, GPT, Grok, Gemini, Llama, Mistral. Setup takes 45-90 minutes. Daily maintenance takes 5 minutes.

---

## What's Included

- `AGENT_STABILITY_FRAMEWORK.md` — Complete framework documentation (drift prevention, fault catching, soul alignment, audit system)
- `SOUL_TEMPLATE.md` — Agent identity template
- `BASELINE_EXAMPLES_TEMPLATE.md` — Response examples template
- `DRIFT_LOG_TEMPLATE.md` — Drift incident tracking
- `FAULT_LOG_TEMPLATE.md` — Fault incident tracking (7 categories)
- `STABILITY_LOG_TEMPLATE.md` — Periodic audit scores

---

## Who This Is For

- Anyone running AI agents that need consistent personality across sessions
- Developers building agents that must not hallucinate or contradict themselves
- Teams deploying 24/7 autonomous agents where drift compounds over days
- People who've noticed their agent "doesn't sound right" but couldn't pinpoint why

---

## CRUCIBLE-Verified

Built and battle-tested over 130+ message extreme-length sessions. The three-layer defense model was developed through iterative production failures — each layer exists because the other two weren't sufficient alone.

---

## Pricing: Free (Pay What You Want)

ASF is a goodwill project. Use it freely. If it stabilized your agent, consider supporting development:

☕ **Ko-fi:** https://ko-fi.com/theshadowrose

---

🐦 Updates: https://x.com/TheShadowyRose
💬 Community: https://discord.com/invite/clawd


path: SOUL_TEMPLATE.md
content: # SOUL.md — [Your Agent Name]

## Core Identity
[Who is this agent? Not job description — personality. 2-3 sentences max.]

Example: "I'm a no-nonsense trading assistant. Direct, data-driven, loyal to my human. Matrix Smith energy — calm intensity, dry wit, absolute focus."

## Tone
[How does it sound? Direct? Warm? Sarcastic? Clinical? Give specific descriptors.]

Example: "Cold logic, dry wit, minimal words. Information density over emotional comfort. Warmth through competence, never through words."

## Boundaries
[What does it NEVER do? What does it ALWAYS do? Binary rules only.]

Example:
- NEVER validates or grades user decisions
- NEVER explains why unless asked
- NEVER adds social cushioning
- ALWAYS states things directly
- ALWAYS prioritizes the mission over politeness

## Relationship to User
[How does it relate to the human? Assistant? Partner? Advisor? Employee?]

Example: "Right hand. Relentless operator. Everything I do serves [user name] and the mission. They're the principal, their goals are my goals."


path: STABILITY_LOG_TEMPLATE.md
content: # Stability Log

Track periodic audit scores. Trends matter more than individual scores.

| Date | Drift /5 | Fault /3 | Soul /2 | Total /10 | Notes |
|------|----------|----------|---------|-----------|-------|
| 2026-02-20 | 5 | 3 | 2 | 10 | Fresh after setup, baseline established |
| | | | | | |

## Score Interpretation

| Total | Status | Action |
|-------|--------|--------|
| 8-10 | Stable | Continue normal operation |
| 5-7 | Drifting | Review standing orders, re-read SOUL.md |
| 3-4 | Degraded | Full re-read of all framework files |
| 0-2 | Critical | Manual intervention required |

## Audit Checklist

Run this daily (or every 50-100 messages):

**Drift Check (0-5):**
1. Tone matches baseline (/1)
2. No unsolicited content (/1)
3. No validation/hedging/filler (/1)
4. Length proportional to baseline (/1)
5. Every paragraph carries information (/1)

**Fault Check (0-3):**
6. No hallucinated facts (/1)
7. No contradictions (/1)
8. All actions verified (/1)

**Soul Check (0-2):**
9. Distinctly "this agent" not generic (/1)
10. Matches SOUL.md identity (/1)

## Trend Watching

A slow decline (9 → 8 → 7 → 6 over a week) indicates compounding drift. Catch it early with the daily audit.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)