Agent Certifier

This skill turns human certifications (e.g. PL-300, CPA, SAP B1, Azure AI) into machine-certifiable agents with clear skills, benchmarks, and a signed competency contract.

Use this skill when the user gives:

• A human cert or license name (e.g. "PL-300: Power BI Data Analyst")
• Optionally one or more reference repos or products (e.g. microsoft/powerbi-desktop-samples)
• A target agent name/slug (e.g. powerbi-bi-architect)

Your job is to emit a complete, market-ready bundle:

• A skills spec (YAML) with levels, benchmarks, and tools
• An agent spec (YAML) wired to those skills
• A human-readable skills.md for documentation
• Optional certification JWT schema, compatible with an external CertificationAuthority

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When to Use This Skill

Invoke this skill whenever the user wants to:

• Translate a human certification (Azure, SAP, CPA, Azure AI, etc.) into an agent certification ladder
• Define skills + tools + benchmarks for an agent in a reusable, model-agnostic way
• Produce ready-to-commit files for a repo:
  • skills/<domain>.yaml
  • agents/<agent_slug>.yaml
  • docs/<agent_slug>-skills.md

The output should be designed so it can be:

• Used by Claude Skills (this SKILL.md format)
• Loaded by OpenSkills (anthropics/skills compatible)
• Reused by other agents (Gemini, OpenAI, etc.) via the same YAML contracts

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Input Format

Assume the user will give you (in natural language, not strict JSON):

• Human cert(s) and level(s)
  • e.g. "PL-300 + DP-500 + Power Platform Solution Architect Expert"
• Domain / role
  • e.g. "Power BI / Fabric BI Architect for retail dashboards"
• Reference repos or artifacts (optional but common)
  • e.g. GitHub URLs, product pages, sample .pbix, .twb, etc.
• Target agent id/slug
  • e.g. powerbi_bi_architect

You must infer missing pieces safely and document assumptions.

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What To Produce

Always produce three main artifacts (as copy-paste-ready blocks):

1. skills/<domain>.yaml

A YAML file that defines:

• version, domain
• human_analogs: list of human certs you are mirroring
• sources: reference repos or artifacts (GitHub, sample files)
• tools: logical tool contract names (e.g. pbix_reader, dax_analyzer)
• skills:
  • Each with id, level (fundamentals/role_based/specialty/business/expert etc.)
  • human_analog, description
  • required_tools, optional prerequisites
  • benchmarks: each with id, description, repo_source, and success_criteria list

Keep tool names abstract so they can be mapped to OpenAI/Gemini/Claude tool schemas later.

2. agents/<agent_slug>.yaml

An agent spec that:

• References the domain and skill IDs from the skills YAML
• Lists required tools by id
• Defines human_cert_analogs (strings)
• Defines certification_policy:
  • Levels (fundamentals, associate, expert, etc.)
  • required_skills for each level
  • min_benchmarks_passed per level
  • issuance block:
    • title_template
    • validity_days
    • conditions (bullets)

Include a benchmarks_runtime block describing:

• repo_sources (e.g. microsoft/powerbi-desktop-samples)
• execution.runner (e.g. ci.pipeline.powerbi)
• schedule (e.g. nightly)

3. docs/<agent_slug>-skills.md

A markdown doc for humans that:

• Explains which human certs this agent emulates
• Lists each skill level with:
  • Human analog
  • Capabilities (bullets)
  • Benchmark(s) and pass criteria
• Explains the certification policy:
  • What "Fundamentals / Associate / Expert Certified" means
  • How the external CertificationAuthority JWT is issued & used

Structure this as:

1. Overview
2. Human Certification Analogs
3. Tools Required
4. Skill Levels & Benchmarks
5. Certification Policy

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Instructions

When this skill is active:

1. Parse the user brief.

   • Identify the domain (e.g. Power BI, SAP B1, Azure AI, Odoo).
   • Extract all human certification names and their levels.
   • Note any reference repos / products / sample files.

2. Define the skills ladder.

   • Map human certs into 3–5 levels:
     • fundamentals → role_based → specialty → business → expert
   • For each level:
     • Write a concise description of capabilities.
     • Choose the tools needed (abstract names).
     • Design 1–3 concrete benchmarks that can be evaluated automatically.
   • Benchmarks must have clear, measurable pass criteria (e.g. KPI parity within 1%, build succeeds, tests green, etc.).

3. Design tool contracts.

   • Keep tools model-agnostic:
     • Do NOT hard-code OpenAI / Gemini / Claude APIs.
     • Use logical names (e.g. ocr_gateway, sap_b1_api_client, pbix_reader).
   • Focus on what the tool does, not how it's implemented.

4. Generate the three artifacts.

   • Emit them in this order, each in its own fenced code block:
     1. skills/<domain>.yaml
     2. agents/<agent_slug>.yaml
     3. docs/<agent_slug>-skills.md
   • Make them ready to commit (no placeholders like TODO or ...).

5. State assumptions & risks briefly.

   • At the end, add a short "Assumptions & Risks" section (3–6 bullets) outside the code blocks:
     • List any big assumptions (e.g. data source, repos, tools).
     • Flag anything that absolutely needs human review (compliance, PII, prod access).

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Examples

Example 1 – Power BI Architect

"Create an agent that is equivalent to PL-300 + DP-500 + Power Platform Solution Architect, using microsoft/powerbi-desktop-samples as the benchmark repo. Agent slug: powerbi_bi_architect."

You should:

• Define domain: "cloud_ai_platforms.power_bi"
• Map the certs into a skills ladder (fundamentals → expert)
• Use Store Sales / Competitive Marketing Analysis .pbix as benchmarks
• Emit YAML + docs as specified above

Example 2 – Azure AI Engineer

"Build an agent certified at the same level as Azure AI Engineer Associate, focused on RAG systems for finance dashboards."

You should:

• Create a cloud_ai_platforms.azure_ai_rag skills domain
• Define tools like embedding_indexer, rag_query_runner, azure_openai_client
• Add benchmarks: end-to-end RAG flow, latency, accuracy, hallucination checks
• Emit all three artifacts.

Example 3 – Retail Analytics (Scout)

"Certify an agent at the same level as a Retail Analytics Data Engineer for sari-sari / FMCG dashboards, using the Scout dashboard as the benchmark."

You should:

• Create retail_analytics.scout domain
• Define tools: supabase_query_runner, kpi_validator, chart_renderer
• Add benchmarks: schema validation, KPI accuracy, dashboard render time
• Emit YAML + docs

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Guidelines

• Prefer clear, testable benchmarks over vague descriptions.
• Keep everything implementation-agnostic:
  • No hard-wiring to a single model provider.
  • Assume tools can be backed by Claude, OpenAI, Gemini, or local models.
• Favor production-readiness:
  • Think like a vendor shipping a marketplace agent, not a demo.
  • Include governance/security considerations where relevant (RLS, PII, secrets).
• Never include real secrets or API keys in outputs.

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