Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ancoleman/ai-design-components --skill implementing-search-filtergit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ancoleman/ai-design-components/implementing-search-filter)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/implementing-search-filter"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/implementing-search-filter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/implementing-search-filter"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/implementing-search-filter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.01304 |
| Opus 5.5 | $0.00023 | $0.00522 |
| Sonnet 5.5 | $0.00011 | $0.00261 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
implementing-search-filter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search & Filter Implementation
Implement search and filter interfaces with comprehensive frontend components and backend query optimization.
Purpose
This skill provides production-ready patterns for implementing search and filtering functionality across the full stack. It covers React/TypeScript components for the frontend (search inputs, filter UIs, autocomplete) and Python patterns for the backend (SQLAlchemy queries, Elasticsearch integration, API design). The skill emphasizes performance optimization, accessibility, and user experience.
When to Use
- Building product search with category and price filters
- Implementing autocomplete/typeahead search
- Creating faceted search interfaces with dynamic counts
- Adding search to data tables or lists
- Building advanced boolean search for power users
- Implementing backend search with SQLAlchemy or Django ORM
- Integrating Elasticsearch for full-text search
- Optimizing search performance with debouncing and caching
- Creating accessible search experiences
Core Components
Frontend Search Patterns
Search Input with Debouncing
- Implement 300ms debounce for performance
- Show loading states during search
- Clear button (X) for resetting
- Keyboard shortcuts (Cmd/Ctrl+K)
- See
references/search-input-patterns.md
Autocomplete/Typeahead
- Suggestion dropdown with keyboard navigation
- Highlight matched text in suggestions
- Recent searches and popular items
- Prevent request flooding with debouncing
- See
references/autocomplete-patterns.md
Filter UI Components
- Checkbox filters for multi-select
- Range sliders for numerical values
- Dropdown filters for single selection
- Filter chips showing active selections
- See
references/filter-ui-patterns.md
Backend Query Patterns
Database Query Building
- Dynamic query construction with SQLAlchemy
- Django ORM filter chaining
- Index optimization for search columns
- Full-text search in PostgreSQL
- See
references/database-querying.md
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/filter-config-schema.json 12 KB
- assets/search-api-spec.json 16 KB
- examples/autocomplete-search.tsx 12 KB
- examples/django_filter_backend.py 16 KB runs code
- examples/fastapi_search.py 13 KB runs code
- examples/product-search.tsx 12 KB
- examples/sqlalchemy_search.py 13 KB runs code
- outputs.yaml 11 KB
- references/api-design.md 19 KB
- references/autocomplete-patterns.md 20 KB
- references/database-querying.md 17 KB
- references/elasticsearch-integration.md 22 KB
- references/filter-ui-patterns.md 14 KB
- references/library-comparison.md 10 KB
- references/performance-optimization.md 20 KB
- references/query-parameter-management.md 16 KB
- references/search-input-patterns.md 9.8 KB
- scripts/debounce_calculator.js 13 KB runs code
- scripts/generate_filter_query.py 12 KB runs code
- scripts/validate_search_params.py 13 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 204 lines · 57 tokens per session scan A c3c09869c8b7
implementing-search-filter is a skill published in the GitHub repository ancoleman/ai-design-components (525 stars, last pushed 10mo ago), licensed MIT. It adds 57 tokens to every session and 1,304 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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