replicate
Discover, compare, and run AI models using Replicate's API
What this skill does
## Docs - Reference docs: https://replicate.com/docs/llms.txt - HTTP API schema: https://api.replicate.com/openapi.json - MCP server: https://mcp.replicate.com - Set an `Accept: text/markdown` header when requesting docs pages to get a Markdown response. ## Workflow Here's a common workflow for using Replicate's API to run a model: 1. **Choose the right model** - Search with the API or ask the user 2. **Get model metadata** - Fetch model input and output schema via API 3. **Create prediction** - POST to /v1/predictions 4. **Poll for results** - GET prediction until status is "succeeded" 5. **Return output** - Usually URLs to generated content ## Choosing models - Use the search and collections APIs to find and compare the best models. Do not list all the models via API, as it's basically a firehose. - Collections are curated by Replicate staff, so they're vetted. - Official models are in the "official" collection. - Use official models because they: - are always running - have stable API interfaces - have predictable output pricing - are maintained by Replicate staff - If you must use a community model, be aware that it can take a long time to boot. - You can create always-on deployments of community models, but you pay for model uptime. ## Running models Models take time to run. There are three ways to run a model via API and get its output: 1. Create a prediction, store its id from the response, and poll until completion. 2. Set a `Prefer: wait` header when creating a prediction for a blocking synchronous response. Only recommended for very fast models. 3. Set an HTTPS webhook URL when creating a prediction, and Replicate will POST to that URL when the prediction completes. Follow these guideliness when running models: - Use the "POST /v1/predictions" endpoint, as it supports both official and community models. - Every model has its own OpenAPI schema. Always fetch and check model schemas to make sure you're setting valid inputs. Even popular models change their schemas. - Validate input parameters against schema constraints (minimum, maximum, enum values). Don't generate values that violate them. - When unsure about a parameter value, use the model's default example or omit the optional parameter. - Don't set optional inputs unless you have a reason to. Stick to the required inputs and let the model's defaults do the work. - Use HTTPS URLs for file inputs whenever possible. You can also send base64-encoded files, but they should be avoided. - Fire off multiple predictions concurrently. Don't wait for one to finish before starting the next. - Output file URLs expire after 1 hour, so back them up if you need to keep them, using a service like Cloudflare R2. - Webhooks are a good mechanism for receiving and storing prediction output.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.