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codeq-natural-language-processing-api

Codeq Natural Language Processing API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Codeq Natural Language Processing API data.

Quality

67%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/codeq-natural-language-processing-api/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The content delivers strong executable guidance for the Membrane CLI workflow, including a well-specified connection-state machine with polling feedback. Its weaknesses are moderate verbosity, a completely flat single-file structure with no progressive disclosure, and dangling 'Step 2' references that break the workflow's internal navigation.

Suggestions

Fix the internal navigation: the body says 'skip to **Step 2**' twice but no Step 2 section exists — either label the subsequent sections as numbered steps or reword to reference them by name.

Move the action catalog (Document/Analysis/Entities etc.) into a references file with each action's input/output schema, and link to it from the overview to implement progressive disclosure.

Trim the opening overview paragraph and merge the duplicated 'action list' instructions in 'Searching for actions' and 'Popular actions' into one section.

DimensionReasoningScore

Conciseness

The body is mostly efficient — command blocks, a flags table, and tight state-machine bullets — but includes unnecessary padding such as the opening paragraph explaining what the API is ('provides tools for understanding and processing human language. Developers use it to add features like...'), the action-tree filler 'Use action names and parameters as needed', and a duplicated command between 'Searching for actions' and 'Popular actions'. This matches anchor 3: could be tightened without losing information.

3 / 5

Actionability

Nearly everything is copy-paste ready: 'npm install -g @membranehq/cli@latest', 'membrane login --tenant --clientName=<agentType>', 'membrane connection ensure "https://api.codeq.com/index" --json', 'membrane action run <actionId> --connectionId=CONNECTION_ID --input ... --json', plus a complete proxy flag table. It falls short of anchor 5 because no concrete actionId from the Document/Batch tree is ever instantiated in an example, leaving the discovery-to-run gap to the reader.

4 / 5

Workflow Clarity

The setup sequence (install → login → connection ensure → poll state → search actions → run) is clear, with explicit checkpoints for each connection state and a feedback loop ('poll again with membrane connection get <id> --json to check if the state moved to READY'). Minor gaps keep it below anchor 5: the text says 'skip to **Step 2**' twice but no Step 2 section exists, and the '#### 1b' heading implies a numbered step sequence that was never established. No destructive or batch operations are involved, so no cap applies.

4 / 5

Progressive Disclosure

The body is well-organized with headers, but the entire skill is a single ~150-line file with no bundle files at all: the action catalog (Document/Analysis/Entities, Batch variants) is inlined as a bare tree with no schemas or examples, and generic Membrane CLI usage could be signaled reference material. This matches anchor 3 — some structure, but content that would be better placed in a reference file sits inline; it is above anchor 2 because sections are cleanly organized and easy to navigate within the file.

3 / 5

Total

14

/

20

Passed

Description

57%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.

The description correctly names the target product and includes an explicit 'Use when' clause, satisfying the completeness floor. However, its action list is generic integration boilerplate rather than the API's actual NLP capabilities, weakening both specificity and trigger-term coverage.

Suggestions

Replace 'Manage data, records, and automate workflows' with the API's concrete capabilities, e.g. 'Analyze sentiment, extract entities and key phrases, and parse syntax from text documents.'

Add natural trigger terms users would say, such as 'sentiment analysis', 'text summarization', 'entity recognition', or 'NLP', to the 'Use when' clause.

Make the 'when' clause non-tautological by describing user intent (e.g. 'Use when the user wants to analyze or process natural language text') rather than restating the API name.

DimensionReasoningScore

Specificity

The description names the domain ('Codeq Natural Language Processing API integration') but its actions — 'Manage data, records, and automate workflows' — are generic boilerplate that could describe any API integration. It does not reach anchor 3 because no concrete NLP capabilities (sentiment analysis, summarization, entity recognition) are stated.

2 / 5

Completeness

Both parts are explicit: 'Manage data, records, and automate workflows' answers what, and 'Use when the user wants to interact with Codeq Natural Language Processing API data' answers when. Not anchor 5 because the 'what' is generic and the 'when' is tautological (use it when the user wants to use the thing it integrates with) rather than naming concrete trigger phrases.

4 / 5

Trigger Term Quality

'Codeq Natural Language Processing API' and 'API data' provide some relevant keywords, but the natural phrases users would actually say — 'sentiment analysis', 'summarize text', 'entity recognition', 'NLP' — are missing. It is above anchor 2 because the product-specific keyword is present and genuinely matches how users would refer to this skill.

3 / 5

Distinctiveness Conflict Risk

The named product 'Codeq Natural Language Processing API' gives it a clear niche with minimal conflict risk against unrelated skills. It stays below anchor 5 because 'Manage data, records, and automate workflows' is generic and overlaps with virtually any data-integration skill description.

4 / 5

Total

13

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

membranedev/application-skills

Path

skills/codeq-natural-language-processing-api/SKILL.md

Reviewed

Table of Contents

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