Filesystem-Based Context Engineering workflow skill. Use this skill when the user needs Use for file-based context management, dynamic context discovery, and reducing context window bloat. Offload context to files for just-in-time loading and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Instrucciones de origen · Vista previa de solo lectura
name
filesystem-context
description
Filesystem-Based Context Engineering workflow skill. Use this skill when the user needs Use for file-based context management, dynamic context discovery, and reducing context window bloat. Offload context to files for just-in-time loading and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/filesystem-context from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Detailed Topics, Practical Guidance, Integration, Skill Metadata, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
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
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: 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.
Examples
Example 1: Ask for the upstream workflow directly
Use @filesystem-context to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @filesystem-context against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @filesystem-context for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @filesystem-context using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: 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
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
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
Imported Operating Notes
Imported: 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
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/filesystem-context, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@20-andruia-niche-intelligence-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@2d-games - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
LangChain Deep Agents: How agents can use filesystems for context engineering
Cursor: Dynamic context discovery patterns
Anthropic: Agent Skills specification
Imported: 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
defhandle_tool_output(output: str, threshold: int = 2000) -> str:
iflen(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)
returnf"[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:
# 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
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:
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.
Subsequent sessions include a step to load user preferences if the file exists.
Caution
This pattern is still emerging. Self-modification requires careful guardrails to prevent agents from accumulating incorrect or contradictory instructions over time.
Filesystem Search Techniques
Models are specifically trained to understand filesystem traversal. The combination of ls, glob, grep, and read_file with line ranges provides powerful context discovery:
grep: Search file contents for patterns, returns matching lines
read_file with ranges: Read specific line ranges without loading entire files
This combination often outperforms semantic search for technical content (code, API docs) where semantic meaning is sparse but structural patterns are clear.
Semantic search and filesystem search work well together: semantic search for conceptual queries, filesystem search for structural and exact-match queries.
Imported: Practical Guidance
When to Use Filesystem Context
Use filesystem patterns when:
Tool outputs exceed 2000 tokens
Tasks span multiple conversation turns
Multiple agents need to share state
Skills or instructions exceed what fits comfortably in system prompt
Logs or terminal output need selective querying
Avoid filesystem patterns when:
Tasks complete in single turns
Context fits comfortably in window
Latency is critical (file I/O adds overhead)
Simple model incapable of filesystem tool use
File Organization
Structure files for discoverability:
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. Include timestamps or IDs in scratch files for disambiguation.
Token Accounting
Track where tokens originate:
Measure static vs dynamic context ratio
Monitor tool output sizes before and after offloading
Track how often dynamic context is actually loaded
Optimize based on measurements, not assumptions.
Imported: 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
