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export_onboarding

Generate per-workflow onboarding markdown files with purpose, risks, and key reads to orient new contributors on an unfamiliar codebase.

Instructions

Generates a workflow-first onboarding package for a new engineer (or new coding agent) joining the repository. Writes one markdown file per workflow under veris-reports/onboarding/ — each file lists the workflow's purpose, member files, key risks, suggested first reads, plus an index.md tying them together. Beats reading raw source code in tree order. Use this when bringing a contributor up to speed on an unfamiliar codebase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Helper function computeFanIn - computes fan-in (incoming edge count) for each node in the graph.
    function computeFanIn(graph: BehavioralGraph): Map<string, number> {
        const map = new Map<string, number>();
        for (const e of graph.getEdges()) {
            map.set(e.targetId, (map.get(e.targetId) || 0) + 1);
        }
        return map;
    }
  • Helper function shortId - shortens node IDs for display in markdown output.
    function shortId(id: string): string {
        return id.split('/').slice(-2).join('/');
    }
Behavior4/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It transparently states that the tool writes markdown files (creating files in the repo), which is a side effect. However, it does not mention permission requirements or whether the operation is reversible, so it's not fully transparent.

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?

The description is two sentences, front-loaded with the core action, and each sentence adds meaningful detail without redundancy. No wasted words.

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 zero parameters, no output schema, and low complexity, the description sufficiently covers the tool's purpose, output format, and usage context. It differentiates from siblings and is complete for an agent to decide when to invoke it.

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?

The input schema has zero parameters, so schema coverage is 100%. The description adds value by explaining the output contents (workflow purpose, member files, key risks, etc.), which compensates for the lack of parameters. Baseline for 0 parameters is 4.

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 generates a workflow-first onboarding package for new engineers or coding agents. It specifies the output format (markdown files per workflow under veris-reports/onboarding/) and differentiates itself from reading raw source code, making it distinct from sibling tools like analyze_repository or list_workflows.

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?

The description explicitly states when to use the tool: 'when bringing a contributor up to speed on an unfamiliar codebase.' It implies this is better than reading raw source, but does not explicitly exclude alternative tools, earning a 4.

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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