This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.
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name
filesystem-context
description
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.
Filesystem-Based Context Engineering
Use the filesystem as the primary overflow layer for agent context because context windows are limited while tasks often require more information than fits in a single window. Files let agents store, retrieve, and update an effectively unlimited amount of context through a single interface.
Prefer dynamic context discovery -- pulling relevant context on demand -- over static inclusion, because static context consumes tokens regardless of relevance and crowds out space for task-specific information.
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
Diagnose context failures against these four modes, because each requires a different filesystem remedy:
Missing context -- needed information is absent from the total available context. Fix by persisting tool outputs and intermediate results to files so nothing is lost.
Under-retrieved context -- retrieved content fails to encapsulate what the agent needs. Fix by structuring files for targeted retrieval (grep-friendly formats, clear section headers).
Over-retrieved context -- retrieved content far exceeds what is needed, wasting tokens and degrading attention. Fix by offloading bulk content to files and returning compact references.
Buried context -- niche information is hidden across many files. Fix by combining glob and grep for structural search alongside semantic search for conceptual queries.
Use the filesystem as the persistent layer that addresses all four: write once, store durably, retrieve selectively.
Detailed Topics
The Static vs Dynamic Context Trade-off
Treat static context (system instructions, tool definitions, critical rules) as expensive real estate -- it consumes tokens on every turn regardless of relevance. As agents accumulate capabilities, static context grows and crowds out dynamic information.
Use dynamic context discovery instead: include only minimal static pointers (names, one-line descriptions, file paths) and load full content with search tools when relevant. This is more token-efficient and often improves response quality by reducing contradictory or irrelevant information in the window.
Accept the trade-off: dynamic discovery requires the model to recognize when it needs more context. Current frontier models handle this well, but less capable models may fail to trigger loads. When in doubt, err toward including critical safety or correctness constraints statically.
Pattern 1: Filesystem as Scratch Pad
Redirect large tool outputs to files instead of returning them directly to context, because a single web search or database query can dump thousands of tokens into message history where they persist for the entire conversation.
Write the output to a scratch file, extract a compact summary, and return a file reference. The agent then uses targeted retrieval (grep for patterns, read with line ranges) to access only what it needs.
defhandle_tool_output(output: str, threshold: int = 2000) -> str:
iflen(output) < threshold:
return output
file_path = f"scratch/{tool_name}_{timestamp}.txt"
write_file(file_path, output)
key_summary = extract_summary(output, max_tokens=200)
returnf"[Output written to {file_path}. Summary: {key_summary}]"
Use grep to search the offloaded file and read_file with line ranges to retrieve targeted sections, because this preserves full output for later reference while keeping only ~100 tokens in the active context.
Pattern 2: Plan Persistence
Write plans to the filesystem because long-horizon tasks lose coherence when plans fall out of attention or get summarized away. The agent re-reads its plan at any point, restoring awareness of the objective and progress.
Store plans in structured format so they are both human-readable and machine-parseable:
# scratch/current_plan.yamlobjective:"Refactor authentication module"status:in_progresssteps:-id:1description:"Audit current auth endpoints"status:completed-id:2description:"Design new token validation flow"status:in_progress-id:3description:"Implement and test changes"status:pending
Re-read the plan at the start of each turn or after any context refresh to re-orient, because this acts as "manipulating attention through recitation."
Pattern 3: Sub-Agent Communication via Filesystem
Route sub-agent findings through the filesystem instead of message passing, because multi-hop message chains degrade information through summarization at each hop ("game of telephone").
Have each sub-agent write directly to its own workspace directory. The coordinator reads these files directly, preserving full fidelity:
Enforce per-agent directory isolation to prevent write conflicts and maintain clear ownership of each output artifact.
Pattern 4: Dynamic Skill Loading
Store skills as files and include only skill names with brief descriptions in static context, because stuffing all instructions into the system prompt wastes tokens and can confuse the model with contradictory guidance.
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
Load the full skill file (e.g., skills/database-optimization/SKILL.md) only when the current task requires it. This converts O(n) static token cost into O(1) per task.
Pattern 5: Terminal and Log Persistence
Persist terminal output to files automatically and use grep for selective retrieval, because terminal output from long-running processes accumulates rapidly and manual copy-paste is error-prone.
Query with targeted grep (grep -A 5 "error" terminals/1.txt) instead of loading entire terminal histories into context.
Pattern 6: Learning Through Self-Modification
Have agents write learned preferences and patterns to their own instruction files so subsequent sessions load this context automatically, instead of requiring manual system prompt updates.
Guard this pattern with validation because self-modification can accumulate incorrect or contradictory instructions over time. Treat it as experimental -- review persisted preferences periodically.
Filesystem Search Techniques
Combine ls/list_dir, glob, grep, and read_file with line ranges for context discovery, because models are specifically trained on filesystem traversal and this combination often outperforms semantic search for technical content where structural patterns are clear.
grep: Search file contents, returns matching lines with context
read_file with ranges: Read specific sections without loading entire files
Use filesystem search for structural and exact-match queries, and semantic search for conceptual queries. Combine both for comprehensive discovery.
Practical Guidance
When to Use Filesystem Context
Apply filesystem patterns when the situation matches these criteria, because they add I/O overhead that is only justified by token savings or persistence needs:
Use when:
Tool outputs exceed ~2000 tokens
Tasks span multiple conversation turns
Multiple agents need shared state
Skills or instructions exceed comfortable system prompt size
Logs or terminal output need selective querying
Avoid when:
Tasks complete in single turns (overhead not justified)
Context fits comfortably in window (no problem to solve)
Latency is critical (file I/O adds measurable delay)
Model lacks filesystem tool capabilities
File Organization
Structure files for agent discoverability, because agents navigate by listing and reading directory names:
project/
scratch/ # Temporary working files
tool_outputs/ # Large tool results
plans/ # Active plans and checklists
memory/ # Persistent learned information
preferences.yaml # User preferences
patterns.md # Learned patterns
skills/ # Loadable skill definitions
agents/ # Sub-agent workspaces
Use consistent naming conventions and include timestamps or IDs in scratch files for disambiguation.
Token Accounting
Measure where tokens originate before and after applying filesystem patterns, because optimizing without measurement leads to wasted effort:
Track static vs dynamic context ratio
Monitor tool output sizes before and after offloading
Measure how often dynamically-loaded context is actually used
Examples
Example 1: Tool Output Offloading
Input: Web search returns 8000 tokens
Before: 8000 tokens added to message history
After:
- Write to scratch/search_results_001.txt
- Return: "[Results in scratch/search_results_001.txt. Key finding: API rate limit is 1000 req/min]"
- Agent greps file when needing specific details
Result: ~100 tokens in context, 8000 tokens accessible on demand
Example 2: Dynamic Skill Loading
Input: User asks about database indexing
Static context: "database-optimization: Query tuning and indexing"
Agent action: read_file("skills/database-optimization/SKILL.md")
Result: Full skill loaded only when relevant
Example 3: Chat History as File Reference
Trigger: Context window limit reached, summarization required
Action:
1. Write full history to history/session_001.txt
2. Generate summary for new context window
3. Include reference: "Full history in history/session_001.txt"
Result: Agent can search history file to recover details lost in summarization
Guidelines
Write large outputs to files; return summaries and references to context
Store plans and state in structured files for re-reading
Use sub-agent file workspaces instead of message chains
Load skills dynamically rather than stuffing all into system prompt
Persist terminal and log output as searchable files
Combine grep/glob with semantic search for comprehensive discovery
Organize files for agent discoverability with clear naming
Measure token savings to validate filesystem patterns are effective
Implement cleanup for scratch files to prevent unbounded growth
Guard self-modification patterns with validation
Gotchas
Scratch directory unbounded growth: Agents create temp files without cleanup, eventually consuming disk and making directory listings noisy. Implement a retention policy (age-based or count-based) and run cleanup at session boundaries.
Race conditions in multi-agent file access: Concurrent writes to the same file corrupt state silently. Enforce per-agent directory isolation or use append-only files with agent-prefixed entries.
Stale file references after moves/renames: Agents hold paths from prior turns that no longer exist after refactors or file reorganization. Always verify file existence before reading a cached path; re-discover with glob if the check fails.
Glob pattern false matches: Overly broad patterns (e.g., **/*) pull irrelevant files into context, wasting tokens and confusing the model. Scope globs to specific directories and extensions.
File size assumptions: Reading a file without checking size can dump 100K+ tokens into context in a single tool call. Check file size before reading; use line-range reads for large files.
Missing file existence checks: Agents assume files exist from prior turns, but they may have been deleted or moved. Always guard reads with existence checks and handle missing-file errors gracefully.
Scratch pad format drift: Unstructured scratch pads become unparseable after many writes because format conventions erode over successive appends. Define and enforce a schema (YAML, JSON, or structured markdown) from the first write.
Hardcoded absolute paths: Break when repositories are checked out at different locations or when running in containers. Use relative paths from the project root or resolve paths dynamically.
Integration
This skill connects to:
context-optimization - Filesystem offloading is a form of observation masking
memory-systems - Filesystem-as-memory is a simple memory layer
tool-design - Tools should return file references for large outputs
References
Internal reference:
Implementation Patterns - Read when: implementing scratch pad, plan persistence, or tool output offloading and need concrete code beyond the inline examples
