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delegate_to_deepseek

Run a sub-agent with its own tool loop to handle batch, repetitive, or mechanical tasks end-to-end, saving main conversation tokens.

Instructions

Delegate a focused task to DeepSeek as a real sub-agent.

DeepSeek runs its own agent loop with Read/Write/Edit/Bash/Glob/Grep/NotebookEdit tools inside the configured workspace. Use this for batch / repetitive / mechanical tasks where you want to save main-conversation tokens and let DeepSeek do the heavy lifting end-to-end.

Good fits:

  • Extract i18n keys from N files into JSON

  • Translate large chunks of text

  • Scan logs for patterns

  • Bulk refactors with a clear pattern

  • One-off ETL scripts

Bad fits (do it yourself instead):

  • Architectural design / cross-file judgment

  • Bug root-cause analysis

  • Tasks requiring project-specific idioms from CLAUDE.md or other repo conventions

Args: task: Clear description of what DeepSeek should accomplish, including success criteria and file paths involved. context: Optional additional context — project conventions, related files DeepSeek should consider, output format requirements. Include this when project-specific knowledge matters.

Returns: A summary of what DeepSeek did, including files affected, turns used, tokens consumed, and any issues. Always verify the result by reading a sample of the affected files before declaring success to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
contextNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes that DeepSeek runs its own agent loop with specific tools, saves main-conversation tokens, and returns a summary including files, turns, tokens, and issues. It also advises verifying results. Could mention potential failures or permissions but overall transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections and bullet points for good/bad fits, front-loading the main purpose. Every sentence adds value, though the list of tools (Read/Write/Edit/Bash/etc.) could be slightly trimmed but is still informative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the tool and that an output schema exists (though not shown), the description covers parameters, use cases, and return value. Sibling is only 'ping', so no confusion. Could mention edge cases or error handling, but adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description provides detailed semantics for both parameters: 'task' is a clear description with success criteria and file paths; 'context' is optional additional context for project-specific knowledge. This significantly adds value beyond the schema property names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool delegates a task to DeepSeek as a sub-agent, listing its capabilities (Read/Write/Edit/Bash/etc.) and specifying the scope of tasks (batch/repetitive/mechanical). It distinguishes itself from the only sibling tool 'ping' which is a simple health check.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Excellent usage guidelines: explicitly lists 'Good fits' (e.g., extract i18n keys, translate, scan logs, bulk refactors) and 'Bad fits' (architectural design, bug analysis, tasks needing project-specific idioms), advising the agent to 'do it yourself instead' for bad fits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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