filesystem-context

What this skill does

# Filesystem-Based Context Engineering

The filesystem provides a single interface through which agents can flexibly store, retrieve, and update an effectively unlimited amount of context. This pattern addresses the fundamental constraint that context windows are limited while tasks often require more information than fits in a single window.

The core insight is that files enable dynamic context discovery: agents pull relevant context on demand rather than carrying everything in the context window. This contrasts with static context, which is always included regardless of relevance.

## When to Activate

Activate this skill when:
- Tool outputs are bloating the context window
- Agents need to persist state across long trajectories
- Sub-agents must share information without direct message passing
- Tasks require more context than fits in the window
- Building agents that learn and update their own instructions
- Implementing scratch pads for intermediate results
- Terminal outputs or logs need to be accessible to agents

## Core Concepts

Context engineering can fail in four predictable ways. First, when the context an agent needs is not in the total available context. Second, when retrieved context fails to encapsulate needed context. Third, when retrieved context far exceeds needed context, wasting tokens and degrading performance. Fourth, when agents cannot discover niche information buried in many files.

The filesystem addresses these failures by providing a persistent layer where agents write once and read selectively, offloading bulk content while preserving the ability to retrieve specific information through search tools.

## Detailed Topics

### The Static vs Dynamic Context Trade-off

**Static Context**
Static context is always included in the prompt: system instructions, tool definitions, and critical rules. Static context consumes tokens regardless of task relevance. As agents accumulate more capabilities (tools, skills, instructions), static context grows and crowds out space for dynamic information.

**Dynamic Context Discovery**
Dynamic context is loaded on-demand when relevant to the current task. The agent receives minimal static pointers (names, descriptions, file paths) and uses search tools to load full content when needed.

Dynamic discovery is more token-efficient because only necessary data enters the context window. It can also improve response quality by reducing potentially confusing or contradictory information.

The trade-off: dynamic discovery requires the model to correctly identify when to load additional context. This works well with current frontier models but may fail with less capable models that do not recognize when they need more information.

### Pattern 1: Filesystem as Scratch Pad

**The Problem**
Tool calls can return massive outputs. A web search may return 10k tokens of raw content. A database query may return hundreds of rows. If this content enters the message history, it remains for the entire conversation, inflating token costs and potentially degrading attention to more relevant information.

**The Solution**
Write large tool outputs to files instead of returning them directly to the context. The agent then uses targeted retrieval (grep, line-specific reads) to extract only the relevant portions.

**Implementation**
```python
def handle_tool_output(output: str, threshold: int = 2000) -> str:
    if len(output) < threshold:
        return output
    
    # Write to scratch pad
    file_path = f"scratch/{tool_name}_{timestamp}.txt"
    write_file(file_path, output)
    
    # Return reference instead of content
    key_summary = extract_summary(output, max_tokens=200)
    return f"[Output written to {file_path}. Summary: {key_summary}]"
```

The agent can then use `grep` to search for specific patterns or `read_file` with line ranges to retrieve targeted sections.

**Benefits**
- Reduces token accumulation over long conversations
- Preserves full output for later reference
- Enables targeted retrieval instead of carrying everything

### Pattern 2: Plan Persistence

**The Problem**
Long-horizon tasks require agents to make plans and follow them. But as conversations extend, plans can fall out of attention or be lost to summarization. The agent loses track of what it was supposed to do.

**The Solution**
Write plans to the filesystem. The agent can re-read its plan at any point, reminding itself of the current objective and progress. This is sometimes called "manipulating attention through recitation."

**Implementation**
Store plans in structured format:
```yaml
# scratch/current_plan.yaml
objective: "Refactor authentication module"
status: in_progress
steps:
  - id: 1
    description: "Audit current auth endpoints"
    status: completed
  - id: 2
    description: "Design new token validation flow"
    status: in_progress
  - id: 3
    description: "Implement and test changes"
    status: pending
```

The agent reads this file at the start of each turn or when it needs to re-orient.

### Pattern 3: Sub-Agent Communication via Filesystem

**The Problem**
In multi-agent systems, sub-agents typically report findings to a coordinator agent through message passing. This creates a "game of telephone" where information degrades through summarization at each hop.

**The Solution**
Sub-agents write their findings directly to the filesystem. The coordinator reads these files directly, bypassing intermediate message passing. This preserves fidelity and reduces context accumulation in the coordinator.

**Implementation**
```
workspace/
  agents/
    research_agent/
      findings.md        # Research agent writes here
      sources.jsonl      # Source tracking
    code_agent/
      changes.md         # Code agent writes here
      test_results.txt   # Test output
  coordinator/
    synthesis.md         # Coordinator reads agent outputs, writes synthesis
```

Each agent operates in relative isolation but shares state through the filesystem.

### Pattern 4: Dynamic Skill Loading

**The Problem**
Agents may have many skills or instruction sets, but most are irrelevant to any given task. Stuffing all instructions into the system prompt wastes tokens and can confuse the model with contradictory or irrelevant guidance.

**The Solution**
Store skills as files. Include only skill names and brief descriptions in static context. The agent uses search tools to load relevant skill content when the task requires it.

**Implementation**
Static context includes:
```
Available skills (load with read_file when relevant):
- database-optimization: Query tuning and indexing strategies
- api-design: REST/GraphQL best practices
- testing-strategies: Unit, integration, and e2e testing patterns
```

Agent loads `skills/database-optimization/SKILL.md` only when working on database tasks.

### Pattern 5: Terminal and Log Persistence

**The Problem**
Terminal output from long-running processes accumulates rapidly. Copying and pasting output into agent input is manual and inefficient.

**The Solution**
Sync terminal output to files automatically. The agent can then grep for relevant sections (error messages, specific commands) without loading entire terminal histories.

**Implementation**
Terminal sessions are persisted as files:
```
terminals/
  1.txt    # Terminal session 1 output
  2.txt    # Terminal session 2 output
```

Agents query with targeted grep:
```bash
grep -A 5 "error" terminals/1.txt
```

### Pattern 6: Learning Through Self-Modification

**The Problem**
Agents often lack context that users provide implicitly or explicitly during interactions. Traditionally, this requires manual system prompt updates between sessions.

**The Solution**
Agents write learned information to their own instruction files. Subsequent sessions load these files, incorporating learned context automatically.

**Implementation**
After user provides preference:
```python
def remember_preference(key: str, value: str):
    preferences_file = "agent/user_p