algolia-search
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning
63
56%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-algolia-search/SKILL.mdQuality
Discovery
54%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 benefits from strong, specific technology keywords (Algolia, React InstantSearch) that make it distinctive and easy to match against relevant user queries. However, it reads more like a topic list than an actionable skill description, lacking concrete actions and entirely missing a 'Use when...' clause to guide skill selection.
Suggestions
Add a 'Use when...' clause with explicit triggers, e.g., 'Use when the user asks about Algolia search setup, configuring search indices, building InstantSearch UIs, or tuning search relevance.'
Replace abstract topic areas with concrete actions, e.g., 'Configures Algolia search indices, builds React InstantSearch components, sets ranking and relevance rules, and manages faceted filtering.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Algolia search) and some actions/areas like 'implementation', 'indexing strategies', 'React InstantSearch', and 'relevance tuning', but these are more like topic areas than concrete actions. It doesn't list specific operations like 'configure search indices', 'build faceted search UI', or 'set ranking rules'. | 2 / 3 |
Completeness | Describes the 'what' at a high level but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause should cap completeness at 2, and the 'what' itself is also weak (topic areas rather than clear capabilities), warranting a 1. | 1 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Algolia', 'search', 'indexing', 'React InstantSearch', and 'relevance tuning'. These are terms developers would naturally use when seeking help with Algolia integration. | 3 / 3 |
Distinctiveness Conflict Risk | Algolia is a very specific technology, and the combination of 'Algolia', 'React InstantSearch', and 'relevance tuning' creates a clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 9 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill excels at actionability with comprehensive, executable code examples covering the full Algolia integration surface area. However, it suffers from being a monolithic document that would benefit significantly from progressive disclosure — splitting detailed code examples and configuration scripts into separate referenced files. The Sharp Edges and Validation Checks sections are skeletal (just labels and severity) and add little value in their current form, hurting both conciseness and workflow clarity.
Suggestions
Split the seven major pattern sections into separate referenced files (e.g., react-instantsearch.md, indexing.md, security.md) and keep SKILL.md as a concise overview with links to each.
Flesh out the Sharp Edges section with actual explanations and mitigation steps, or remove the empty entries — currently they're just severity-labeled headers that waste tokens without providing value.
Add an overarching workflow section at the top that sequences the patterns (e.g., '1. Configure index settings → 2. Index data → 3. Build search UI → 4. Secure API keys') to help Claude understand the integration order.
Consolidate the Validation Checks with the Anti_patterns sections since they largely overlap, reducing redundancy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is quite long (~500+ lines) with some redundancy. The Sharp Edges section lists items without explanations (wasted headers), and the Validation Checks section repeats information already covered in Anti_patterns. However, the code examples themselves are reasonably efficient and the anti-patterns format is compact. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples for every major pattern: client setup, SSR integration, indexing, API key security, relevance configuration, faceted search, and autocomplete. Code includes TypeScript types, environment variable patterns, and real import paths. | 3 / 3 |
Workflow Clarity | Individual patterns are clear but there's no overarching workflow connecting them (e.g., 'first configure index, then index data, then build UI'). The full reindex function shows an atomic swap sequence which is good, but the Sharp Edges and Validation Checks sections are just lists of labels with severity levels and no actionable remediation steps or verification procedures. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with no bundle files to offload detail into. All seven major patterns with full code examples, anti-patterns, sharp edges, validation checks, and delegation triggers are crammed into a single file. The code examples alone are hundreds of lines that could be split into referenced files for each pattern. | 1 / 3 |
Total | 8 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (925 lines); consider splitting into references/ and linking | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Repository
- boisenoise/skills-collections
- Commit
431bfad
- Reviewed
Table of Contents
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.