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SKILL verified MIT Self-run

Meilisearch

skill-ashish7802-awesome-api-skills-meilisearch · by ashish7802

A Claude skill from ashish7802/awesome-api-skills.

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Install

$ agentstack add skill-ashish7802-awesome-api-skills-meilisearch

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Meilisearch API Skill

Quick Start

Meilisearch is an open-source, typo-tolerant search engine optimized for developer experience.

npm install meilisearch

Common Workflows

Indexing and Searching

Add JSON documents to an index. Meilisearch automatically infers the schema. From the frontend or backend, query the index. It natively handles typos, highlighting, and sorting out of the box.

Production Patterns

Primary Keys

Always explicitly define the primaryKey when creating an index or passing documents. If omitted, Meilisearch will attempt to infer it (looking for id), which can lead to unpredictable behavior if your data uses UUIDs under custom field names.

Error Recovery

Handle MeiliSearchCommunicationError. Indexing in Meilisearch is asynchronous. Pushing documents returns a taskUid. You must poll client.waitForTask(taskUid) if you need to guarantee the data is searchable before proceeding.

Security Notes

Generate strict Tenant Tokens to allow frontend clients to search the database directly while enforcing security rules (e.g., restricting a user to only search documents where owner_id = 123).

Performance Considerations

While Meilisearch is extremely fast for reads, pushing thousands of documents individually is slow. Always batch document uploads in arrays of up to 100,000 documents per task to maximize throughput.

Testing Guidance

You can easily spin up a Meilisearch instance in a Docker container (getmeili/meilisearch) for CI/CD integration tests, destroying the container post-test.

Troubleshooting

If specific fields aren't returning in search results, verify the displayedAttributes setting. If filtering fails, ensure the field is explicitly added to filterableAttributes.

References

Related Skills

  • [Typesense](/skills/typesense)
  • [Algolia](/skills/algolia)

Why use this skill

Use this when your agent works with meilisearch — structured patterns beat pasted docs and prevent common hallucinations.

AI pitfalls

  • Inventing column names or schema fields
  • Using deprecated driver methods or wrong connection strings
  • Omitting connection pooling or transaction boundaries

Production checklist

  • [ ] Migrations version-controlled and applied via CI
  • [ ] Connection limits and pooling configured
  • [ ] Backups and restore procedure documented

Related skills

  • [typesense](../typesense/SKILL.md) — related to
  • [algolia](../algolia/SKILL.md) — related to

> Last Verified: 2026-07-02

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.