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claude

Execute code implementation tasks using Anthropic Claude CLI agent for translating requirements into working code with strong programming capabilities.

Instructions

Run Anthropic Claude CLI agent (code implementation).

NO SHARED MEMORY:

  • Cannot see messages/outputs from codex/gemini/opencode.

  • Only sees: (1) this prompt, (2) files in context_paths, (3) its own history via continuation_id.

CROSS-AGENT HANDOFF:

  • Small data: paste into prompt.

  • Large data: save_file -> context_paths -> prompt says "Read ".

CAPABILITIES:

  • Strongest code writing and implementation abilities

  • Excellent at translating requirements into working code

  • Good at following patterns and conventions

BEST PRACTICES:

  • Be explicit about target: "Replace old implementation completely"

  • Specify cleanup: "Remove deprecated code paths"

Supports: system_prompt, append_system_prompt, agent parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDetailed instructions for the agent. IMPORTANT: If 'continuation_id' is NOT set, you MUST include ALL context (background, file contents, errors, constraints), as the agent has no memory. If 'continuation_id' IS set, you may be brief and reference previous context.
workspaceYesProject root directory. Boundary for 'workspace-write'. Use absolute paths or relative paths.
continuation_idNoResume session WITHIN THIS TOOL ONLY. Use only the <continuation_id> returned by this same tool. IDs are agent-specific: codex ID won't work with gemini/claude/opencode. Switching agents does NOT sync info; pass updates via prompt or context_paths.
permissionNoSecurity level: 'read-only' (analyze files), 'workspace-write' (modify inside workspace), 'unlimited' (full system access). Default: 'read-only'.read-only
modelNoOptional model override (e.g., 'gemini-2.5-pro'). Use only if specifically requested.
save_fileNoPREFERRED when agent needs to write files or produce lengthy output. Output is written directly to this path, avoiding context overflow. This write is permitted even in read-only mode (server-handled). Essential for: code generation, detailed reports, documentation.
save_file_with_wrapperNoWhen true AND save_file is set, wrap output in <agent-output> XML tags with metadata (agent name, continuation_id). For multi-agent assembly.
save_file_with_append_modeNoWhen true AND save_file is set, append instead of overwrite. For multi-agent collaboration on same document.
report_modeNoGenerate a standalone, document-style report (no chat filler) suitable for sharing.
context_pathsNoList of relevant files/dirs to preload as context hints.
system_promptNoComplete replacement for the default system prompt. Use only when you need full control over agent behavior. Prefer append_system_prompt for most cases.
append_system_promptNoAdditional instructions appended to the default system prompt. Recommended way to customize behavior. Example: 'Focus on performance optimization, avoid adding new dependencies'
agentNoSpecify an agent for the current session (overrides the default agent setting). Use predefined agent names configured in Claude Code settings.
task_noteNoREQUIRED user-facing label. Summarize action in < 60 chars (e.g., '[Fix] Auth logic' or '[Read] config.py'). Shown in GUI progress bar to inform user.
debugNoEnable execution stats (tokens, duration) for this call.
Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers comprehensive behavioral disclosure. It explains critical constraints (no shared memory, agent-specific continuation IDs), security implications through permission parameter context, cross-agent collaboration patterns, and practical limitations like context overflow management. This goes well beyond basic functional description.

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?

The description is well-structured with clear sections (NO SHARED MEMORY, CROSS-AGENT HANDOFF, CAPABILITIES, BEST PRACTICES, Supports) that make information easy to find. While comprehensive, some sections could be more concise - the CAPABILITIES and BEST PRACTICES sections contain some redundancy with the opening statement. Overall, most sentences earn their place by adding important operational context.

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?

For a complex 15-parameter tool with no annotations and no output schema, the description provides substantial context about behavioral constraints, collaboration patterns, and practical usage. It effectively compensates for the lack of structured metadata. The main gap is not explaining return values or output format, but given the tool's nature as an agent runner, the operational context provided is quite complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 15 parameters thoroughly. The description adds some context about parameter usage (e.g., prompt context requirements, save_file benefits, cross-agent considerations), but doesn't provide significant semantic value beyond what's in the parameter descriptions themselves. Baseline 3 is appropriate given complete schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Run Anthropic Claude CLI agent (code implementation)' - a specific verb+resource combination. It distinguishes Claude from other agents by highlighting its unique capabilities ('Strongest code writing and implementation abilities'), but doesn't explicitly differentiate from all sibling tools like codex or gemini beyond mentioning they have separate memory spaces.

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?

The description provides excellent usage guidance with explicit when/when-not scenarios. It explains memory limitations ('NO SHARED MEMORY'), cross-agent handoff procedures, when to use save_file vs prompt inclusion, and best practices for code replacement and cleanup. It clearly defines the tool's specific role among agents based on capabilities.

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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