claude-certification

AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

Quality

88%

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Securityby

Passed

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Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, highly actionable workflow with strong sequencing and validation checkpoints for a complex multi-mode tutor. Its main weakness is progressive disclosure: it is a long single-file document with no reference files, so some inline templates and question banks could be split out.

Suggestions

Move the full CLAUDE-CERTIFICATION.md state template and the track-by-track audience/recommendedExperience mappings into a references/ file (e.g. references/state-template.md and references/tracks.md), keeping SKILL.md as an overview that links to them.

Extract the onboarding question bank and the per-mode step lists into a reference file so the body stays a concise mode-dispatch overview.

Trim a few directive restatements (e.g. repeated 'Never claim the learner...' formulations) to tighten conciseness without losing the policy constraints.

Dimension	Reasoning	Score
Conciseness	Dense procedural directives with no padding of concepts Claude already knows (it never explains what a certification or quiz is), but the onboarding question bank and full state-file template add length that could be trimmed, sitting noticeably above the midpoint rather than fully lean.	4 / 5
Actionability	Provides concrete commands ('python3 <lesson-path>/code/main.py', 'python3 -m unittest discover -s <lesson-path>/code/tests -v'), specific JSON fields ('pre', 'check', 'post', 'correct'), and a full state template, with only minor abstract guidance like 'Adapt depth to the learner's responses'.	4 / 5
Workflow Clarity	Multi-mode workflows are clearly sequenced (Lesson mode steps 1-4, Assessment mode steps 1-7) with explicit validation checkpoints and feedback loops ('Do not mark practical work verified if the runtime or tests did not actually run', 70% quiz gate, review queue), so the destructive/batch cap does not apply.	5 / 5
Progressive Disclosure	Well-organized into clear sections, but it is a ~275-line monolithic SKILL.md with no bundle files and no external references; the state-file template, track map, and onboarding question bank are inline content that could plausibly live in reference files.	3 / 5
	Total	16 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that clearly states both capability and trigger conditions with concrete exam codes and tool names. The only minor gap is trigger-term synonym coverage, which keeps it just short of perfect on that one dimension.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions—'choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains'—giving comprehensive coverage rather than vague verbs.	5 / 5
Completeness	Explicitly answers both what ('AI-native tutor and onboarding workflow for the four independent Claude certification tracks') and when ('Use when a learner wants to...') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Includes natural phrases a learner would say ('choose a Claude certification', 'resume a certification path', 'diagnostic or mock exam', 'remediate weak exam domains') plus tool names (Claude Code, Codex, ChatGPT, Cursor), but a few common variations are absent, so it sits just below the comprehensive anchor.	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche—the four named Claude certification tracks and their exam codes—with distinct triggers and minimal overlap risk against other skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: rohitg00/ai-engineering-from-scratch
Path: skills/claude-certification/SKILL.md
Commit: c02ca08

Reviewed: 4 days ago

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