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Synthesize Self-Evolving Skill

kilo_synthesize_skill
Destructive

Distill a solved architectural pattern or bugfix into a reusable, validated SKILL.md for AI coding agents.

Instructions

Distill a newly solved architectural pattern or bugfix methodology into a reusable, validated SKILL.md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNomarkdown
categoryNoTarget skill category (defaults to 'learned')
keywordsNoKeywords for discovery
skillNameYesName of the skill to synthesize
solutionPatternYesProven solution pattern and code guidelines
problemDescriptionYesDescription of the problem solved
verificationGuidanceYesVerification and testing steps

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, readOnlyHint=false, and idempotentHint=false, so the agent knows this is a mutating, non-idempotent operation. The description adds that the output is 'reusable, validated,' but never explains what gets created/overwritten, whether it persists to disk, or what 'validated' implies. It neither contradicts the annotations nor richly extends them.

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?

A single, front-loaded sentence with no waste. The purpose and intended input are conveyed immediately without redundancy.

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

Completeness3/5

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

For a destructive, non-idempotent tool with no output schema, the description doesn't cover critical context such as what the synthesized SKILL.md contains, where it is stored, or whether synthesis requires a prior solve. The schema is well-documented, but behavioral completeness falls short.

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 86%, so the schema already documents the 7 parameters well, including enums and formats. The description contributes no additional parameter-level detail (e.g., how format affects output, keyword conventions), so baseline 3 applies.

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?

States a specific verb (distill/synthesize) and resource (SKILL.md, self-evolving skill), and names the trigger condition (a newly solved architectural pattern or bugfix methodology). It is clearly distinguishable from siblings like kilo_get_skill and kilo_search_skills, which read rather than create skills.

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

Usage Guidelines3/5

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

The phrase 'newly solved' implies the timing (after solving a problem), which gives some usage context, but no explicit when-not-to-use or alternative tool is named. It does not tell the agent to prefer this over kilo_record_reflection or kilo_remember_fact for capturing learnings.

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