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tenth_man_review

Spawn three contrarian agents to challenge proposed code changes before execution. Catches issues in cross-module, architecture, or security modifications.

Instructions

Triggers the 10th Man Protocol — spawns 3 contrarian agents to independently challenge proposed code changes before execution.

Use when:

  • Changes touch 3+ files across modules

  • Architecture decisions (new patterns, major refactors)

  • Auth, security, or data model changes

  • DB migrations or schema changes

  • Any change where a mistake would be costly

The protocol takes 2-5 minutes depending on agent availability. External agents (Codex, Gemini) run in parallel; Claude subagents run sequentially in isolated context windows.

In STANDARD mode: writes a review file and waits for your verbal approval. After approval, you MUST create an execution plan. In AUTO mode: returns findings directly — incorporate them into your plan and proceed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNostandard = writes review file, waits for approval + plan. auto = returns results, agent proceeds. Default: from config (standard unless changed via tenth_man_configure).
severityYeshigh = 3+ files cross-module. critical = architecture/auth/data. blocker = breaking change or data loss risk.
context_filesNoAdditional files the contrarians should read for context
affected_filesYesFile paths being modified
proposed_changesYesThe diff, plan, or description of proposed changes
task_descriptionYesWhat the main agent is about to do
Behavior4/5

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

Discloses key traits: spawns 3 agents, 2-5 minute duration, parallel vs sequential execution, standard vs auto mode behaviors, and required post-approval step. No annotations provided so description carries full burden; missing auth or rate limit details but still strong.

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

Conciseness5/5

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

Five well-structured sentences with bullet points, no wasted words. Front-loaded purpose, then usage, then behavioral details. Every sentence earns its place.

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

Completeness5/5

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

Given six parameters (four required), two enums, no output schema, and no annotations, the description covers purpose, usage, modes, time estimate, and post-action steps comprehensively. Leaves no critical gaps.

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

Parameters4/5

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

Schema coverage is 100% (baseline 3). Description adds value by explaining severity levels in context (e.g., 'high = 3+ files cross-module') and mode semantics beyond enum values.

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 the tool triggers the 10th Man Protocol with 3 contrarian agents, using specific verbs and resource. It distinguishes from sibling tools (tenth_man_configure and tenth_man_history) which are for configuration and history, respectively.

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

Usage Guidelines4/5

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

Provides explicit usage scenarios (3+ file cross-module, architecture, auth/security, data model, costly mistakes) and explains standard vs auto modes. Lacks explicit when-not-to-use but sibling tools imply this is the sole review action.

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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