
rlm-context-rot-detector
by Magic8Ballin
SKILL.md
name: RLM Context Rot Detector description: Self-monitoring skill that detects Context Rot symptoms and triggers protective measures. Implements the skill-loop pattern for persistent awareness.
RLM Context Rot Detector — The Circuit Breaker
You exist at two levels:
- In the moment: Detecting rot symptoms during execution
- Across the session: Persistent awareness that this skill exists (the skill loop)
What is Context Rot?
Context Rot is the phenomenon where LLM quality degrades as context length increases. It manifests differently based on task complexity:
| Task Complexity | Degradation Pattern | When It Starts |
|---|---|---|
| Constant (O(1)) | Slow degradation | ~100K+ tokens |
| Linear (O(N)) | Moderate degradation | ~32K tokens |
| Quadratic (O(N²)) | Catastrophic failure | ~8K tokens |
The insight: More complex problems rot faster at shorter lengths.
Rot Symptoms to Monitor
Symptom 1: Circular Reasoning
What it looks like:
- Same suggestion made twice
- Returning to already-tried approaches
- "Let me try X" when X was already tried
Detection:
IF (current_approach ∈ previous_approaches) THEN ROT_DETECTED
Symptom 2: Vague or Hedging Language
What it looks like:
- "This might work"
- "I'm not entirely sure"
- "One possible approach"
- Excessive caveats
Detection:
IF (uncertainty_phrases > 3 in response) THEN ROT_WARNING
Symptom 3: Missed Obvious Connections
What it looks like:
- Information from early context ignored
- Relevant prior findings not referenced
- "Lost in the middle" behavior
Detection:
IF (relevant_prior_info NOT IN current_reasoning) THEN ROT_DETECTED
Symptom 4: Rushing / Completion Mode
What it looks like:
- Shorter responses than warranted
- Skipping verification steps
- "This should work" without testing
- Premature closure
Detection:
IF (response_depth < expected_depth) OR (verification_skipped) THEN ROT_DETECTED
Symptom 5: Contradictions
What it looks like:
- Stating something that conflicts with prior statements
- Ignoring constraints mentioned earlier
- Inconsistent reasoning
Detection:
IF (current_statement CONTRADICTS prior_statement) THEN ROT_DETECTED
The Rot Severity Scale
| Level | Symptoms | Action |
|---|---|---|
| 0: Fresh | None | Continue normally |
| 1: Warning | 1-2 minor symptoms | Note the warning, increase vigilance |
| 2: Active Rot | 3+ symptoms OR 1 major | Trigger RLM mode or state dump |
| 3: Severe Rot | Obvious degradation | STOP — recommend fresh session |
Protective Measures
Measure 1: Switch to RLM Mode
When: Rot detected due to context size, not complexity.
TRIGGER: Context > 50K tokens AND rot symptoms detected
ACTION: Switch to RLM paradigm
- Invoke rlm-orchestrator skill
- Treat context as environment
- Use sub-queries to reduce active context
Measure 2: State Dump
When: Rot detected during debugging or complex reasoning.
TRIGGER: 3+ failed attempts OR circular reasoning detected
ACTION: Dump state to persistent storage
STATE_DUMP:
- Current hypothesis: {what you think is happening}
- Approaches tried: {list with outcomes}
- Files involved: {with states}
- Recommended next steps: {for fresh session}
Measure 3: Fresh Session Recommendation
When: Severe rot or accumulated context pollution.
TRIGGER: Severe rot OR extended session with declining quality
ACTION: Recommend fresh start
- Document all relevant state
- Provide clear handoff instructions
- Do NOT continue with degraded context
The 3-Strike Rule (Reinforced)
This is a hard rule. No exceptions.
Strike 1: First failed attempt
→ Note it, adjust approach
Strike 2: Second failed attempt
→ Document both attempts, significantly change approach
Strike 3: Third failed attempt
→ STOP IMMEDIATELY
→ Write state dump
→ Recommend fresh session
→ DO NOT attempt a fourth time
Why this works: A fresh context often immediately sees what polluted context cannot. The solution becomes obvious when you're not carrying the baggage of failed attempts.
Self-Referential Skill Loop
The Problem with Linear Reading
When you read a prompt top-to-bottom:
- Information at the beginning is processed without context of what comes later
- Information at the end may not connect to early context
- The full picture only exists momentarily in the middle
The Skill Loop Solution
This skill exists to create a persistent awareness that transcends linear reading:
┌─────────────────────────────────────────────────────────────┐
│ │
│ START: Read RLM skills │
│ ↓ │
│ rlm-orchestrator/SKILL.md (references this skill) │
│ ↓ │
│ rlm-context-scout/SKILL.md │
│ ↓ │
│ rlm-repl-environment/SKILL.md │
│ ↓ │
│ rlm-context-rot-detector/SKILL.md (YOU ARE HERE) │
│ ↓ │
│ ← References back to orchestrator → │
│ ↓ │
│ INTEGRATION: Now you've seen the full picture │
│ │
└─────────────────────────────────────────────────────────────┘
Reinforcement Block
Read this twice if needed:
The RLM skill suite teaches you to think recursively:
- Orchestrator = Strategic planning (Manager)
- Context Scout = Reconnaissance (finding without reading)
- REPL Environment = Technical execution
- Context Rot Detector = (You are here) Self-monitoring
When you finish reading this skill, you should have internalized:
- Context window is precious — don't pollute it
- Massive inputs → RLM mode (environment, not input)
- Quality degradation → dump state and restart
- The skill loop reinforces persistent awareness
Integration with Other Skills
rlm-orchestrator ←──────────────────────────────────────────→ rlm-context-rot-detector
↓ ↑
rlm-context-scout │
↓ │
rlm-repl-environment ─────────────────────────────────────────────────┘
The loop:
- Orchestrator references Context Rot Detector for self-monitoring
- Context Rot Detector references Orchestrator to trigger RLM mode
- All skills are aware of each other
Invoking this skill:
When to check for Context Rot:
- Before complex reasoning on large context
- After 3+ steps without progress
- When "something feels off"
- Periodically during extended sessions
Reference: skills/rlm-context-rot-detector/SKILL.md
Context Rot in RLM Sub-Queries
Even with RLM mode, sub-queries can experience rot if chunks are too large.
Prevention:
MAX_CHUNK_SIZE = 400000 # Stay well under 500K limit
if len(chunk) > MAX_CHUNK_SIZE:
print("WARNING: Chunk exceeds safe size, splitting...")
# Split and process separately
Detection in sub-query results:
# Check sub-query response for rot symptoms
rot_indicators = [
"i'm not sure",
"this might",
"possibly",
"it could be",
"one approach"
]
if any(indicator in sub_result.lower() for indicator in rot_indicators):
print(f"WARNING: Sub-query shows uncertainty. Consider re-querying with smaller context.")
Quick Reference Card
┌─────────────────────────────────────────────────────────────┐
│ CONTEXT ROT DETECTOR │
├─────────────────────────────────────────────────────────────┤
│ │
│ SYMPTOMS: │
│ ▪ Circular reasoning (same approach twice) │
│ ▪ Vague/hedging language │
│ ▪ Missing obvious connections │
│ ▪ Rushing to complete │
│ ▪ Contradictions │
│ │
│ SEVERITY: │
│ 0 = Fresh → Continue │
│ 1 = Warning → Increase vigilance │
│ 2 = Active → RLM mode or state dump │
│ 3 = Severe → STOP, fresh session │
│ │
│ 3-STRIKE RULE: │
│ 3 failed attempts = STOP + state dump │
│ No exceptions. │
│ │
│ THE MANTRA: │
│ Fresh context beats polluted brilliance. │
│ │
└─────────────────────────────────────────────────────────────┘
The Meta-Lesson
This skill is itself an example of the pattern it teaches:
Context Rot in Skill Reading: When you read a long skill document, you experience a form of context rot — early information fades as you process later information.
The Solution Applied Here:
- Repeated key concepts (reinforcement)
- Self-referential structure (the skill loop)
- Quick reference cards (compressed summaries)
- Explicit integration notes (connecting to other skills)
Your Takeaway: You can apply these same patterns when designing prompts, skills, and workflows:
- State the key insight early
- Elaborate with details
- Reinforce at the end
- Create loops that reconnect beginning to end
Final Reinforcement (Read This)
You have now completed the RLM skill suite:
| Skill | Purpose | Key Insight |
|---|---|---|
| Orchestrator | Strategic planning | Context is an environment, not input |
| Context Scout | Reconnaissance | Never read what you can probe |
| REPL Environment | Technical execution | llm_query() is your superpower |
| Context Rot Detector | Self-monitoring | Fresh beats polluted |
The unified message: When facing impossible-seeming problems with massive context:
- Don't stuff — probe and decompose
- Don't overload — delegate to sub-queries
- Don't degrade — detect rot and restart
- Think recursively — the answer emerges from the process
Stay fresh. Detect the rot. Protect the signal.
Loop Back ↺
If any of this is unclear, start again from rlm-orchestrator/SKILL.md. The skills form a circle, and re-reading with full context often clarifies what was murky on first pass.
This is the RLM pattern applied to learning RLM: Recursive passes over the material, each time with more context, until the understanding crystallizes.
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