This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

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 Do not activate this skill for adjacent work owned by other skills: Semantic cross session memory, entity tracking, or temporal knowledge graphs: memory systems . Conversation summarization, compaction, or durable handoff wording: context compression . Token efficiency tactics that do not require file backed storage: context optimization . Multi agent topology or handoff protocol design: multi agent patterns . Core Concepts Diagnose context failures against these four modes, because each requires a different filesystem remedy: 1. 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. 2. 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). 3. 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. 4. 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. 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: 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. 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. ls / list dir : Discover directory structure glob : Find files matching patterns (e.g., / .py ) 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: Use consistent naming conventions and include timestamps or IDs in scratch files for disambiguation. For autonomous research loops, store raw retrieved evidence under the run that consumed it, for example researcher/runs/<run id /sources/evidence/raw/ . Do not leave raw research dumps in the repository root; root level artifacts become hard to audit and easy to cite without provenance. 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 Example 2: Dynamic Skill Loading Example 3: Chat History as File Reference Guidelines 1. Write large outputs to files; return summaries and references to context 2. Store plans and state in structured files for re reading 3. Use sub agent file workspaces instead of message chains 4. Load skills dynamically rather than stuffing all into system prompt 5. Persist terminal and log output as searchable files 6. Combine grep/glob with semantic search for comprehensive discovery 7. Organize files for agent discoverability with clear naming 8. Measure token savings to validate filesystem patterns are effective 9. Implement cleanup for scratch files to prevent unbounded growth 10. Guard self modification patterns with validation 11. Keep raw evidence next to the run, evaluation, and proposal that used it Gotchas 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. 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 owns file backed context storage and retrieval. Adjacent skills own semantic memory, summarization, and topology: context optimization : filesystem offloading is one implementation of observation masking when full outputs remain retrievable. memory systems : use when file backed notes are no longer enough and semantic, entity, or temporal retrieval is required. multi agent patterns : sub agent file workspaces enable context isolation and direct handoff. context compression : file references can anchor summaries and preserve details omitted from compressed context. tool design : tools should return file references for large outputs and expose safe read/search operations. References Internal reference: [Implementation Patterns](./references/implementation patterns.md) Read when: implementing scratch pad, plan persistence, or tool output offloading and need concrete code beyond the inline ex

filesystem-context