111 lines
3.4 KiB
Markdown
111 lines
3.4 KiB
Markdown
# Reference: Manus Context Engineering Principles
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This skill is based on the context engineering principles from Manus, the AI agent company acquired by Meta for $2 billion in December 2025.
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## The 6 Manus Principles
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### 1. Filesystem as External Memory
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> "Markdown is my 'working memory' on disk."
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**Problem:** Context windows have limits. Stuffing everything in context degrades performance and increases costs.
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**Solution:** Treat the filesystem as unlimited memory:
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- Store large content in files
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- Keep only paths in context
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- Agent can "look up" information when needed
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- Compression must be REVERSIBLE
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### 2. Attention Manipulation Through Repetition
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**Problem:** After ~50 tool calls, models forget original goals ("lost in the middle" effect).
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**Solution:** Keep a `task_plan.md` file that gets RE-READ throughout execution:
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```
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Start of context: [Original goal - far away, forgotten]
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...many tool calls...
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End of context: [Recently read task_plan.md - gets ATTENTION!]
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```
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By reading the plan file before each decision, goals appear in the attention window.
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### 3. Keep Failure Traces
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> "Error recovery is one of the clearest signals of TRUE agentic behavior."
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**Problem:** Instinct says hide errors, retry silently. This wastes tokens and loses learning.
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**Solution:** KEEP failed actions in the plan file:
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```markdown
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## Errors Encountered
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- [2025-01-03] FileNotFoundError: config.json not found → Created default config
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- [2025-01-03] API timeout → Retried with exponential backoff, succeeded
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```
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The model updates its internal understanding when seeing failures.
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### 4. Avoid Few-Shot Overfitting
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> "Uniformity breeds fragility."
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**Problem:** Repetitive action-observation pairs cause drift and hallucination.
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**Solution:** Introduce controlled variation:
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- Vary phrasings slightly
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- Don't copy-paste patterns blindly
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- Recalibrate on repetitive tasks
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### 5. Stable Prefixes for Cache Optimization
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**Problem:** Agents are input-heavy (100:1 ratio). Every token costs money.
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**Solution:** Structure for cache hits:
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- Put static content FIRST
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- Append-only context (never modify history)
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- Consistent serialization
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### 6. Append-Only Context
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**Problem:** Modifying previous messages invalidates KV-cache.
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**Solution:** NEVER modify previous messages. Always append new information.
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## The Agent Loop
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Manus operates in a continuous loop:
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```
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1. Analyze → 2. Think → 3. Select Tool → 4. Execute → 5. Observe → 6. Iterate → 7. Deliver
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```
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### File Operations in the Loop:
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| Operation | When to Use |
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|-----------|-------------|
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| `write` | New files or complete rewrites |
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| `append` | Adding sections incrementally |
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| `edit` | Updating specific parts (checkboxes, status) |
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| `read` | Reviewing before decisions |
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## Manus Statistics
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| Metric | Value |
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|--------|-------|
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| Average tool calls per task | ~50 |
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| Input-to-output ratio | 100:1 |
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| Acquisition price | $2 billion |
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| Time to $100M revenue | 8 months |
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## Key Quotes
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> "If the model improvement is the rising tide, we want Manus to be the boat, not the piling stuck on the seafloor."
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> "For complex tasks, I save notes, code, and findings to files so I can reference them as I work."
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> "I used file.edit to update checkboxes in my plan as I progressed, rather than rewriting the whole file."
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## Source
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Based on Manus's official context engineering documentation:
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https://manus.im/de/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus
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