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Skillsskill-router

skill-router

Agents & models

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Updated 10-16 03:39

About this skill

Skill Router

The library holds 191 skills across 15 categories. This skill answers which ones apply to a task, and how to combine them.

Core Concepts

Route on the Task, Not the Technology

"Fix this slow query" is not a PostgreSQL question until you know the query is in PostgreSQL. Start from what the user is trying to do, then narrow by the technology actually in the repository.

Two Shapes of Skill

Shape Naming Answers Examples
Domain expertise <domain>-expert "How does X work, and what is good practice?" postgresql-expert, react-expert
Procedure <activity>-workflow "What do I do, in what order?" tdd-workflow, debugging-workflow

A task usually needs one of each: the workflow drives the sequence, the expert supplies the judgement inside it.

Compose, Don't Concatenate

Loading five skills fills the context and dilutes attention. Load the workflow plus the one or two experts whose material the task actually touches, and consult a third only if the work reaches it.

Finding a Skill

The catalogue is machine-readable and is the fastest route:

# By keyword across names, descriptions and tags
jq -r '.skills[] | select(
         (.name + " " + .description + " " + (.tags | join(" ")))
         | ascii_downcase | contains("kafka")
       ) | "\(.category)/\(.name)"' stdlib/catalog/skill-catalog.json

# Everything in a category
jq -r '.skills[] | select(.category == "security") | .name' stdlib/catalog/skill-index.json

# What a skill covers, before loading it
jq -r '.skills[] | select(.name == "rag-expert") | .description' stdlib/catalog/skill-catalog.json

Or by filesystem, which is often quicker for a name you half-remember:

find stdlib -maxdepth 2 -type d -name '*postgres*'
grep -rl "watermark" stdlib --include=SKILL.md

Every description contains an explicit Use when … clause naming the keywords and tasks that should select it. Read that clause rather than guessing from the name.

The Category Map

Category Holds Reach for it when
workflows/ Procedures: TDD, refactoring, debugging, code review The question is "how do I proceed?"
languages/ 23 programming languages Idiom, tooling, or language-specific behaviour
frameworks/ 15 web, mobile and desktop frameworks Framework conventions and lifecycle
api/ REST, GraphQL, gRPC, OpenAPI, microservices Designing or consuming an interface
data/ Databases, warehouses, pipelines, BI Storage, queries, analytics, streaming
devops/ Containers, orchestration, IaC, CI/CD, observability Build, deploy, run
cloud/ AWS, Azure, GCP, Cloudflare Provider-specific services
ai/ ML, LLM engineering, RAG, agents Anything model-based
security/ AppSec, identity, secrets, supply chain, compliance A security question, at any stage
qa/ Test frameworks, load, chaos Writing or running tests
tools/ Git, documents, browsers, chat platforms, this skill Working with a tool rather than a domain
domains/ Industry verticals and enterprise platforms Domain rules: healthcare, finance, SAP
professional/ Accounting, legal, banking, FinOps, standards Non-engineering expertise
scientific/ Research, quantum, biology Scientific computing
design/ Architecture patterns, accessibility Structure or interface quality

Note that design/design-expert covers software architecture, not visual design — a legacy naming artefact. accessibility-expert is the interface skill.

Choosing Between Overlapping Skills

Several pairs look similar. The distinctions that matter:

If the task is… Use Not
Driving a browser to test your own app playwright-expert browser-automation-expert
Driving a browser to get data from a site browser-automation-expert playwright-expert
The process of reviewing a change code-review-workflow code-review-expert
What good code looks like per dimension code-review-expert code-review-workflow
A security breach investigation incident-response-expert debugging-workflow
A production defect, no attacker debugging-workflow incident-response-expert
Transactional workload, rows postgresql-expert, mysql-expert analytical-databases-expert
Aggregates over huge tables analytical-databases-expert postgresql-expert
Moving events continuously stream-processing-expert airflow-expert
Scheduled batch orchestration airflow-expert stream-processing-expert
Prompt design and model output llm-engineering-expert agent-engineering-expert
A model that calls tools agent-engineering-expert llm-engineering-expert
Answering over your documents rag-expert elasticsearch-expert

Composition Patterns

Common tasks and the skills that serve them:

"Add a feature to our API, test-first" tdd-workflow (the loop) → api-design-expert (contract) → the language skill → code-review-workflow (before merge)

"Our checkout is slow" debugging-workflow (method) → performance-expert (measurement) → the database or framework skill, once measurement says which

"Build an assistant over our documentation" rag-expert (retrieval) → llm-engineering-expert (generation and evaluation) → agent-engineering-expert (only if it calls tools) → identity-access-expert (who may see which documents)

"Harden our release pipeline" supply-chain-security-expert (provenance, SBOM) → cicd-expert (pipeline) → secrets-management-expert (credentials)

"Make the dashboard accessible" accessibility-expert (conformance) → the framework skill (implementation) → qa-expert (regression coverage)

"Migrate a large table with no downtime" refactoring-workflow (parallel change) → the database skill (mechanics) → sre-expert (rollout and rollback)

When No Skill Fits

Before concluding a skill is missing:

  1. Search the catalogue by tag, not by the name you expected.
  2. Check references/ — the topic may be a section inside a broader skill. rails lives in ruby-expert, spark in databricks-expert and analytical-databases-expert.
  3. Check whether a broader skill covers it. vector-databases is inside rag-expert by design, because the choice only matters in that context.

If it is genuinely absent, the test for adding one is whether it is a distinct decision context rather than a distinct technology. Five vector stores share one mental model and belong in one skill with per-store references; MySQL and PostgreSQL differ enough in design consequences to warrant separate skills.

Use skill-creator-expert to author it, and scripts/validate-skills.py to check conformance.

Best Practices

  • Read the Use when clause before loading a skill; it is written to be the routing signal.
  • Start with the workflow when the question is procedural — it will name the expert skills it needs.
  • Load two or three skills, not six. Add a fourth when the work reaches it.
  • Prefer the specific skill. mysql-expert over sql-expert when the database is MySQL.
  • Consult security/ early, not as a final review — the cheapest security fixes are design decisions.
  • Follow the reference links rather than loading a second skill for a detail; references/ is where the depth lives.

Anti-Patterns

  • Routing on a keyword in isolation. "Java" in "JavaScript" is not java-expert.
  • Loading every plausibly related skill — dilutes attention and fills the context budget.
  • Picking the general skill when a specific one exists.
  • Skipping the workflow skill and improvising the procedure.
  • Assuming a topic is missing because the skill is not named after it.

Resources

  • stdlib/catalog/skill-index.json — name, category, path, tags
  • stdlib/catalog/skill-catalog.json — full descriptions, sizes, reference lists
  • stdlib/SKILLS_INVENTORY.md — human-readable listing by category
  • stdlib/CHANGELOG.md — versioning and migration notes
  • skill-creator-expert — authoring a new skill