Assistant Workflows
ContextCrumb is designed for agent context loading. The core habit is:
Compress prose-heavy context before placing it into the model context.
The skill instructions focus on file loading because that is the easiest behavior to teach an assistant with a SKILL.md. Developers can also use ContextCrumb deeper in the stack: prompt compression, history compression, subagent output compression, and tool-output compression.
Who This Is For
This section is for people using coding assistants, research agents, MCP clients, or editor-integrated tools. If you are adding ContextCrumb to an application or pipeline, start with the Python API or Local Service.
Recommended Agent Rule
Use ContextCrumb for large natural-language files and supported code files:
contextcrumb load path/to/file.md
contextcrumb load path/to/file.py
For supported Python, JavaScript, TypeScript, JSX, TSX, Go, and Rust files, default auto mode preserves executable source exactly and compresses only comments/docstrings.
Do not use compressed output as the only source for exact syntax:
- Unsupported source code, or exact source edits
- Config files
- Diffs
- Schemas
- Commands
- Legal or policy text
For structured tool results, preserve the structure and compress only prose values. For example, compress a long body or description field, but keep ids, URLs, scores, timestamps, and schema fields raw.
Common Prompts
Ask an agent:
Use ContextCrumb to compress the project notes before you use them as context.
Or:
Before reading the full transcript into context, run contextcrumb load on it and work from the compressed output.
For safer code-adjacent use:
Use ContextCrumb for supported source files in code-comments mode. Load commands, configs, and unsupported code raw.
Agent Integration Choices
| Integration | Use when |
|---|---|
| Skill files | Your agent supports repo-owned or installed skills |
| MCP server | Your agent can call MCP tools |
| MCP shrink proxy | Tool catalogs are verbose before any tool call happens |
| Local service | You want one warm model shared by multiple agent calls |
Why This Helps Agents
- Reduces context spent on filler wording
- Keeps the original sequence of the document
- Avoids inventing summaries before the agent understands the task
- Lets agents process more source material before hitting context pressure
- Provides stats for how much context was saved