AI Skill Report Card

Converting Docs to Skills

Converts technical documentation, agent frameworks, and workflow notes into structured SKILL.md specifications and Gemini Spark automations. Use when turning raw docs, chat history, or coursework notes into reusable agent skills, when building task-identifier prompts for multi-agent systems, or when packaging Rust microservice/MCP architecture patterns into skill format.

B+78·Sep 27, 2026·Source: Web
12 / 15

Given a source artifact (doc, chat log, framework spec), produce a SKILL.md:

Input: Rust microservice retry-handling notes + MCP tool-call schema
Output: SKILL.md with name "handling-microservice-retries", workflow steps
        for backoff/circuit-breaking, and example MCP tool payloads

Default output is always a single skill file with YAML frontmatter, never a summary or explanation.

Recommendation▾
Quick Start example output is described abstractly rather than showing the actual full SKILL.md content generated — include a real rendered example.
14 / 15

Progress:

  • Step 1: Identify the core repeatable task in the source material (strip away narrative/history)
  • Step 2: Name the skill in gerund-kebab-case
  • Step 3: Write third-person description with explicit trigger phrases
  • Step 4: Extract or infer a concrete Quick Start example
  • Step 5: Break the methodology into ordered workflow steps (checklist if >4 steps)
  • Step 6: Pull 2-3 concrete input/output examples from source material
  • Step 7: List best practices actually evidenced in the source (not generic advice)
  • Step 8: List pitfalls observed (e.g., vague descriptions, missing triggers, monolithic skills)
  • Step 9: If source describes a multi-agent trigger system, add a "task-identifier" subsection mapping trigger phrases → agent/skill routing
  • Step 10: Trim to ~500 lines; if longer, split into linked files (e.g., reference.md, examples.md)

Task-Identifier Sub-Workflow

When the source is about routing tasks across multiple agents/skills:

  1. Enumerate candidate agents/skills and their one-line purpose.
  2. For each, extract 3-5 trigger phrases from real usage (chat history, queries).
  3. Build a decision table: trigger phrase pattern → skill name → confidence signal.
  4. Flag ambiguous triggers that map to >1 skill; resolve by specificity (prefer the more specialized skill).
  5. Emit this table as a task-identifier.md reference file alongside the SKILL.md, not inline in frontmatter.
Recommendation▾
Examples section shows partial YAML frontmatter snippets but not complete input-to-output transformations; flesh out at least one example fully end-to-end.
14 / 20

Example 1: Input: A month of chat history showing repeated searches for "SKILL.md spec", "Gemini Spark automation", "MCP tool schema" Output:

YAML
--- name: building-gemini-spark-workflows description: Builds automated Gemini Spark workflows from architecture notes and prompt requests. Use when converting saved Sparks into executable automation chains or when drafting new Spark prompt templates. ---

Plus Quick Start showing a Spark trigger → action → output chain.

Example 2: Input: "Build task-identifier skill prompt" + "Explore multi-agent workflow triggers" Output: A routing-agent-tasks skill whose Quick Start shows a raw user request being matched against a trigger table and routed to the correct downstream skill, with the task-identifier sub-workflow table included as a linked reference file.

Example 3: Input: Gmail coursework snippets on Rust microservices + MCP architecture Output: A documenting-mcp-tool-contracts skill capturing how to translate a Rust service's tool schema into an MCP-compliant SKILL.md, with examples of request/response JSON.

Recommendation▾
Add an explicit 'bad output' example (e.g., a poorly-specified skill) contrasted with a good one to reinforce the pitfalls section.
  • Mine actual chat/search history for trigger phrases instead of inventing generic ones — real queries make better description fields.
  • Keep one skill = one repeatable task; split "SKILL.md builder" from "Spark workflow builder" from "task router" rather than merging them.
  • When multiple related skills emerge (as here), also produce a short index noting how they relate (e.g., router → calls → specific skill).
  • Preserve architecture-template language (Rust, MCP) verbatim in examples so the skill stays technically grounded.
  • Date-stamp nothing in the skill body itself — skills should be timeless artifacts, not snapshots of "yesterday's" work.
  • Don't leave description vague ("helps with skills") — it must name concrete triggers like "when converting chat history into SKILL.md".
  • Don't bundle the task-identifier/router logic into every skill's frontmatter; keep it in a dedicated routing skill or reference file.
  • Don't narrate the user's activity history inside the skill file — extract the pattern, discard the anecdote.
  • Don't let multi-agent trigger tables balloon the main SKILL.md past ~500 lines; externalize them.
0
Grade B+AI Skill Framework
Scorecard
Criteria Breakdown
Quick Start
12/15
Workflow
14/15
Examples
14/20
Completeness
16/20
Format
14/15
Conciseness
13/15