pinecone

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Quality

74%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./backend/cli/skills/llm-tools/pinecone/SKILL.md

Quality

Content

65%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 highly actionable reference with comprehensive executable examples, weakened by a destructive-operations section lacking validation checkpoints and an orphaned deployment.md reference that is never linked from the body.

Suggestions

Add an explicit validation/confirmation step before destructive deletes (e.g., list matching IDs or run describe_index_stats before index.delete(delete_all=True)) so destructive workflows clear the workflow_clarity cap.

Link references/deployment.md from the body (e.g., a '## Deployment' section pointing to [deployment.md](references/deployment.md)) and move LangChain/LlamaIndex integration detail there or into a separate file to improve progressive disclosure.

Remove the duplicated 'Create index' example from Quick start or Core operations, and move the 'Pricing (as of 2025)' figures into the deployment reference so the time-sensitive data does not pad the main body.

Dimension	Reasoning	Score
Conciseness	Mostly efficient executable code, but the 'Create index' example is duplicated between Quick start and Core operations, and the 'Pricing (as of 2025)' block is time-sensitive material not placed in a deprecated/old-patterns section, both of which pad the context unnecessarily.	3 / 5
Actionability	Fully executable, copy-paste-ready code covers the common cases end-to-end: install, initialize, create index, upsert (single + batch), query (with filters/namespaces), hybrid search, and LangChain/LlamaIndex integration.	5 / 5
Workflow Clarity	Operations are well-sectioned, but the destructive 'Delete vectors' operations (delete by filter, delete_all in namespace, delete entire index) carry no validation or confirmation checkpoints, which caps workflow clarity at 3 per the destructive-operations rule.	3 / 5
Progressive Disclosure	Section structure is reasonable, but the bundled references/deployment.md is never referenced or linked from the body, and integration/deployment content that belongs in separate files is inlined, so references are not clearly signaled.	3 / 5
	Total	14 / 20 Passed

Description

83%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, third-person description that clearly states both capabilities and trigger contexts with concrete specifics. It would reach the top anchor by framing features as actions and adding synonyms like 'embeddings' and 'similarity search'.

Dimension	Reasoning	Score
Specificity	Lists several specific concrete capabilities ('hybrid search (dense + sparse), metadata filtering, and namespaces', 'auto-scaling', 'Low latency (<100ms p95)') but presents them as feature nouns rather than action verbs, leaving minor coverage gaps versus a fully action-oriented list.	4 / 5
Completeness	Explicitly answers both what ('Managed vector database... with hybrid search, metadata filtering, and namespaces') and when ('Use for production RAG, recommendation systems, or semantic search at scale') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Good natural keyword coverage with 'production RAG, recommendation systems, or semantic search at scale' plus 'serverless, managed infrastructure', but misses common synonyms like 'embeddings' or 'similarity search'.	4 / 5
Distinctiveness Conflict Risk	The managed/serverless production positioning carves a clear Pinecone niche, but it still risks minor overlap with other vector-database skills (Chroma, Weaviate, FAISS) that handle similar RAG/semantic-search triggers.	4 / 5
	Total	17 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/llm-tools/pinecone/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

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