soccer-workshop-setup

Bootstrap the soccer analytics agent workshop. Starts the Oracle AI Database Free container, applies schema, loads the FIFA dataset, optionally trains models, populates LangChain OracleVS hybrid retrieval plus semantic memory, applies LangGraph OracleDB observability, and verifies OCI GenAI access. Use when starting the workshop or resetting a stale environment.

Quality

85%

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Securityby

Low

Low-risk findings worth noting

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.

An exceptionally actionable, well-sequenced operational skill: copy-paste commands, exact error codes, expected outputs, and explicit validation/feedback loops throughout. It loses points on token efficiency (duplicated gating/contract/grant content across sections) and on progressive disclosure, since a substantial block of reference material is inlined rather than offloaded to a references file.

Suggestions

Move the 'Pitfalls & lessons learned' section (Oracle, in-DB ONNX, python-oracledb, OCI GenAI, container networking) into a references/ file (e.g. references/pitfalls.md) and keep a one-line pointer plus only the pitfalls that affect a live setup step in SKILL.md.

De-duplicate the OCI-value gating: keep the full policy in step 2 and reduce the top-level 'Required workshop-day OCI values' section to a single sentence pointing at step 2; likewise reference the step-4 'CREATE MINING MODEL' note from Pitfalls instead of restating it.

Collapse the 'Hybrid retrieval contract' and 'LangGraph OracleDB observability contract' sections into a short contract statement each, since steps 10, 13, and 14 already restate the operational details.

Dimension	Reasoning	Score
Conciseness	Mostly efficient — the bulk is genuinely non-obvious operational knowledge (ORA-54426, ORA-01031, the ONNX tokenizer-in-graph quirk) — but there is real redundancy: the OCI-value gating appears both as a top-level section and again in step 2, the 'CREATE MINING MODEL' grant is explained in step 4 and repeated in Pitfalls, and the hybrid-retrieval contract is restated across sections 1, 10, and 13. Fits 'mostly efficient but could be tightened', not 4 given the duplicated blocks.	3 / 5
Actionability	Every step carries an exact copy-paste command ('bash .claude/skills/soccer-workshop-setup/scripts/01_start_oracle.sh', 'uv run python scripts/load_predictions.py'), exact expected outcomes ('roughly 2,500+ rows', 'features_used: 92'), exact failure signatures, and fallback paths — fully executable and covering the common cases.	5 / 5
Workflow Clarity	Fifteen strictly ordered steps with 'Stop and surface the error on any failure', per-step 'If it fails' feedback loops, a verification step that maps each red check back to the step to re-run ('A red on PREDICCIONES_FINAL ... re-run steps 6 and 9'), and an end-to-end post-build verification with error recovery ('revert the front-end source ... a polished-but-broken UI is worse than the shipped default').	5 / 5
Progressive Disclosure	The body is well-sectioned with clear headers and its two bundled scripts (scripts/01_start_oracle.sh, scripts/setup.sh) are real and referenced by path, but the ~35-line 'Pitfalls & lessons learned' reference material (Oracle, in-DB ONNX, python-oracledb, OCI GenAI, container networking) is inlined in SKILL.md instead of being split into a references/ file — 'content that should be separate is inline'. Not 2: the document's own structure and signaling are good, not minimal.	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 description: third-person voice, concrete multi-action capability list, and an explicit 'Use when' clause with natural trigger phrases. The only gap is minor — a few natural synonym phrasings for triggering the skill are absent.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — 'Starts the Oracle AI Database Free container, applies schema, loads the FIFA dataset, optionally trains models, populates LangChain OracleVS hybrid retrieval plus semantic memory, applies LangGraph OracleDB observability, and verifies OCI GenAI access' — comprehensive coverage matching the score-5 anchor. Not 4: there are no real gaps in the capability list.	5 / 5
Completeness	Explicitly answers both 'what' (the enumerated actions) and 'when' ('Use when starting the workshop or resetting a stale environment'), exactly matching the score-5 anchor with concrete trigger phrases.	5 / 5
Trigger Term Quality	Natural trigger phrases are present ('Use when starting the workshop or resetting a stale environment', 'Bootstrap'), plus domain keywords (Oracle, FIFA, workshop). A few natural variations a user might say — 'set up / initialize the workshop', 'environment setup' — are missing, matching the 'good keyword coverage; a few natural terms missing' anchor rather than the comprehensive 5.	4 / 5
Distinctiveness Conflict Risk	A clear niche — bootstrapping this specific soccer-analytics-agent workshop — with distinct triggers ('starting the workshop', 'resetting a stale environment') and minimal conflict risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 15 missing	Warning

	Total	15 / 16 Passed

Repository: oracle-devrel/oracle-ai-developer-hub
Path: workshops/soccer-analytics-agent/.claude/skills/soccer-workshop-setup/SKILL.md
Commit: 93ecdd7

Reviewed: 2 days ago

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