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BytesAgain AI Skills Search

by com.bytesagain

run_pipeline

Full automated content pipeline. Given a topic, it: 1) Discovers relevant skills from 60,000+ database (search + rank) 2) Scores each on 6 dimensions (downloads, stars, category relevance, description quality, source diversity, name match) 3) Uses AI to select the best 5-8 skills that genuinely fit the topic 4) Generates a structured use case (title, description, skill stack with reasons) 5) Writes a full 800+ word article in markdown 6) Creates 3 tweet drafts promoting the use case 7) Saves article to Supabase posts table and use case to use_cases table 8) Generates a 1792x1024 cover image and uploads to Supabase Storage 9) Returns everything in one response Set publish=true to auto-publish. Set publish=false (default) for draft-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesTopic for the full pipeline. Example: "automate invoice processing", "write code documentation". Required.
publishNoAuto-publish to live site. Default: false (draft). Set true to set status=published.

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses side effects: saving to Supabase posts and use_cases tables, uploading cover image to storage, and auto-publishing behavior controlled by the publish parameter. This transparency about mutations (writes, uploads) and default draft-only mode exceeds typical descriptions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average, but the numbered steps make it scannable and information-dense. Every sentence contributes: the opening defines scope, steps detail the process, and the final sentence clarifies the publish flag. Acceptable for a complex pipeline tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with no output schema, the description covers inputs, the full sequence of operations, data persistence effects, and publishing behavior. It does not describe the response format in detail, but 'Returns everything in one response' gives adequate closure. The absence of error handling discussion is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for both parameters, but the description enriches semantics by stating 'Given a topic' and providing an example for query, and explaining the publish parameter's effect ('Set publish=true to auto-publish'). This adds practical meaning beyond the schema's simple type descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Full automated content pipeline' and enumerates a 9-step process, clearly specifying the tool's comprehensive behavior. It distinguishes itself from sibling tools like generate_usecase or score_skills by covering the entire workflow from skill discovery to publishing, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the tool takes a topic and produces a complete article with use case and tweets, implying use when a full content pipeline is desired. It does not explicitly mention alternatives or when NOT to use it, but the 'Full automated' framing sets clear context against more granular sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation3/5

Several tools have overlapping purposes: evaluate_skill and scan_skill both assess skill safety, while generate_usecase, get_workflow, and score_skills all involve skill scoring and recommendation. Description differences exist but boundaries are not always crisp, potentially causing misselection. The unrelated get_deals tool also adds confusion.

Naming Consistency4/5

Tool names mostly follow a consistent verb_noun snake_case pattern (e.g., search_skills, get_skill, submit_request). Minor inconsistencies exist: popular_skills uses an adjective instead of a verb, and generate_usecase uses 'usecase' while search_use_cases uses 'use_cases'.

Tool Count4/5

With 14 tools, the server is on the higher end of the typical range but still well-scoped for its broad functionality (search, evaluation, workflows, community, content pipeline). Each tool serves a distinct functional area, though a few could be consolidated.

Completeness4/5

The core workflow of searching, retrieving, and evaluating skills is well covered, including use cases and community requests. However, there are minor gaps such as lack of a category browsing tool or direct single-skill installation, and the inclusion of unrelated AliExpress deals seems out of place.

Resources