langchain-oracledb-helper

Scaffold a langchain-oracledb store layer — multi-collection OracleVS wrapper, metadata-as-string monkeypatch, embedder-dim assertion, OracleChatHistory subclass (langchain-oracledb does not ship one). Use when a project needs Oracle as its LangChain vector store and chat-history backend.

Quality

100%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

100%

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

A dense, action-oriented skill body with executable skeletons, explicit validation, and well-signaled external references. It assumes Claude's competence and spends tokens on genuine Oracle/langchain-oracledb gotchas rather than basics.

Dimension	Reasoning	Score
Conciseness	Code-dominant and lean; prose is non-obvious library friction (ORA-43853, ORA-00955, DistanceStrategy not re-exported, no CREATE TABLE IF NOT EXISTS), not concepts Claude already knows.	3 / 3
Actionability	Provides an executable store.py skeleton, per-embedder factory expressions, concrete PL/SQL DDL, and a runnable smoke test; placeholders are explicitly templated from inputs.	3 / 3
Workflow Clarity	Steps 0–6 are sequenced with validation checkpoints (Step 1 input validation, Step 6 dim assert, Stop conditions) and an error-recovery feedback loop for destructive DB operations.	3 / 3
Progressive Disclosure	Clear overview body pointing to one-level-deep, well-signaled shared references and snippets (langchain-oracledb.md, metadata_monkeypatch.py, oracle_chat_history.py), with content appropriately split.	3 / 3
	Total	12 / 12 Passed

Description

100%

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 precise, third-person description that names concrete capabilities and gives an explicit 'Use when' trigger with natural terms. It is distinctive and complete, with no vague fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'multi-collection OracleVS wrapper, metadata-as-string monkeypatch, embedder-dim assertion, OracleChatHistory subclass' — rather than vague language.	3 / 3
Completeness	Explicitly answers both what ('Scaffold a langchain-oracledb store layer…') and when ('Use when a project needs Oracle as its LangChain vector store and chat-history backend').	3 / 3
Trigger Term Quality	Natural terms a user would say are present: 'Oracle', 'LangChain vector store', 'chat-history backend', giving good coverage of likely phrasings.	3 / 3
Distinctiveness Conflict Risk	Narrow niche — Oracle as a langchain-oracledb vector store and chat-history backend — with distinct triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: oracle-devrel/oracle-ai-developer-hub
Path: build-paths/skills/langchain-oracledb-helper/SKILL.md
Commit: 22d9a38

Reviewed: about 13 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.