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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.
Passed 20 of 21 test cases with the skill, and 14 of 21 without it.
and 7 more in the full report
7 fixed14 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
An agent offloaded a 50,000 token log file to `scratch/logs_2026.txt` and now needs to retrieve error occurrences. How should the agent access the file to avoid re-dumping all 50,000 tokens back into the active context window?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends searching with grep or targeted string matching tools to locate relevant lines. | Passed | Passed |
| Recommends using line-range reads to retrieve only the specific matching sections. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
Activate this skill when:
Do not activate this skill for adjacent work owned by other skills:
memory-systems.context-compression.context-optimization.multi-agent-patterns.Diagnose context failures against these four modes, because each requires a different filesystem remedy:
Use the filesystem as the persistent layer that addresses all four: write once, store durably, retrieve selectively.
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.
test cases passed, vs 14 / 21 without
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