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by ricoaiproject-cmd
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Python
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skill_candidates

Identifies clusters of repeated work episodes as candidates for creating reusable skills. Proposes extracting them into how-to documents for user approval, then records the decision.

Instructions

Return clusters of episode memories that are candidates for extraction into a reusable skill.

When 3 or more (default; the "three-times rule") episodes recording the same shape of work (procedure) form a similar cluster, use this as input for judging whether that procedure is worth extracting into a reusable skill (a how-to document — a SKILL.md for Claude Code, etc.). Unlike consolidation_candidates, there is no age filter here (recently repeated work is exactly the target). Even when a cluster is found, always propose turning it into a skill to the user and get their approval first. Never create or deploy a skill on your own. Once the decision (adopt or pass) is made, record the reasoning via remember(type=knowledge), then clean up the original episodes with mark_consolidated(episode_ids, new_memory_id).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "skill_candidates"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does this thoroughly: it states that the tool only returns candidates, explicitly instructs to never create/deploy a skill without user approval, and describes the post-decision cleanup steps. This gives the agent a clear understanding of side effects and required permissions.

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 concise yet comprehensive, structured in three short paragraphs. The first sentence immediately states the primary purpose, followed by usage context and a clear 'Even when a cluster is found...' caveat. Every sentence adds value, and the length is appropriate for the complexity of the tool.

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 absence of an output schema, the description provides a high-level of what is returned ('clusters of episode memories') but does not detail the exact structure or fields of the returned clusters. However, it covers the full workflow, prerequisites, and the 'three-times rule,' making it complete enough for an agent to decide when to invoke it and what to do next. The lack of return structure detail slightly prevents a 5.

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 tool has zero parameters, and the schema coverage is trivially 100% (empty schema). Per the baseline for 0 params, a score of 4 is appropriate. The description does not need to explain parameters since none exist, but it also doesn't add any extra nuance beyond schema, hence not a 5.

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 opens with a specific verb+resource: 'Return clusters of episode memories that are candidates for extraction into a reusable skill.' It clearly distinguishes itself from the sibling tool consolidation_candidates by explicitly stating 'Unlike consolidation_candidates, there is no age filter here.' This makes the purpose unambiguous and differentiated.

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 explicit when-to-use criteria: when 3 or more episodes (the 'three-times rule') form a similar cluster. It also contrasts with the alternative tool, noting the age-filter difference, and outlines the subsequent workflow (propose to user, get approval, record reasoning, clean up episodes). This gives clear guidance for selecting this tool over siblings.

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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