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ai-generation-persistence

AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation

56

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/vercel/skills/ai-generation-persistence/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

The body is highly actionable with complete, executable code and clear sequencing, but is held back by minor redundancy, absent validation checkpoints in its write workflows, and a single-file structure that could externalize reference material.

Suggestions

Add explicit validation steps for writes (e.g., verify Blob upload succeeded, confirm DB insert before redirecting) with a fix-and-retry loop.

Move the persistence schema and detailed code examples into a references/ file (e.g. SCHEMA.md) and link from a leaner overview to improve progressive disclosure.

Tighten redundancy between the Storage Strategy table and the schema block so each fact appears once.

DimensionReasoningScore

Conciseness

Mostly efficient and code-driven with tight directives, but contains minor redundancy (the storage table restates schema fields and prose restates what the code shows) that could be tightened.

2 / 3

Actionability

Provides multiple fully executable, copy-paste-ready code blocks with real imports and APIs (nanoid, Drizzle, @vercel/blob, @upstash/redis, ai's generateText).

3 / 3

Workflow Clarity

Steps are clearly sequenced (ID before generation, create-then-redirect) but there are no explicit validation checkpoints or feedback loops for the database/Blob write operations.

2 / 3

Progressive Disclosure

Well-sectioned but monolithic at ~170 lines with all content inline and no bundle files or one-level-deep references to split out the schema and detailed code.

2 / 3

Total

9

/

12

Passed

Description

67%

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

The description is specific and well-differentiated, naming concrete capabilities in a distinct niche, but lacks an explicit "when to use" trigger clause and relies on somewhat technical trigger phrasing rather than natural user language.

Suggestions

Add an explicit "Use when..." clause naming natural triggers, e.g. "Use when the user asks to save AI generations, persist chat history, or build an AI chat app."

Replace technical phrasing like "persistence patterns" and "addressable URLs" with user-facing terms ("save generations", "shareable chat links").

DimensionReasoningScore

Specificity

Lists four concrete capabilities ("unique IDs, addressable URLs, database storage, and cost tracking"), matching the multiple-specific-actions anchor.

3 / 3

Completeness

Clearly answers "what" the skill does but has no explicit "Use when..." clause or equivalent trigger guidance, which caps completeness at 2 per the guidelines.

2 / 3

Trigger Term Quality

Has some natural keywords ("LLM generation", "cost tracking") but uses technical phrasing ("persistence patterns", "addressable URLs") and omits common user variations like "save chat history" or "chat app".

2 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (persisting AI/LLM generations) with distinct terms unlikely to trigger for unrelated skills.

3 / 3

Total

10

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

Total

14

/

16

Passed

Repository
openai/plugins
Reviewed

Table of Contents

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