Provider Docs Agent Skill

Create, update, and review Terraform provider documentation for Terraform Registry using HashiCorp-recommended patterns, tfplugindocs templates, and schema descriptions. Use when adding or changing provider configuration, resources, data sources, ephemeral resources, list resources, functions, or guides; when validating generated docs; and when troubleshooting missing or incorrect Registry documentation.

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

157 d ago

last touched

this folder, not the whole repository

Install

one command, takes just this skill from the repository

npx skills add https://github.com/hashicorp/agent-skills --skill provider-docs

The instruction itself

4 sections, as written by the author

Terraform Provider Docs

Follow This Workflow

Confirm scope and documentation targets.
Map code changes to the exact doc targets: provider index, resources, data sources, ephemeral resources, list resources, functions, or guides.
Decide whether content should come from schema descriptions, templates, or both.

Write schema descriptions first.
Add precise user-facing descriptions to schema fields so generated docs stay aligned with behavior.
Keep wording specific to argument purpose, constraints, defaults, and computed behavior.

Add or update template files in docs/.
Create only files that map to implemented provider objects.
Use HashiCorp-recommended template paths:
docs/index.md.tmpl
docs/data-sources/<name>.md.tmpl
docs/resources/<name>.md.tmpl
docs/ephemeral-resources/<name>.md.tmpl
docs/list-resources/<name>.md.tmpl
docs/functions/<name>.md.tmpl
docs/guides/<name>.md.tmpl
Keep templates focused on overview and examples; rely on generated sections for field-by-field details.

Generate documentation with tfplugindocs.
Prefer repository defaults when configured:

go generate ./...

Otherwise run the generator directly:

go run github.com/hashicorp/terraform-plugin-docs/cmd/tfplugindocs generate --provider-name <provider_name>

Re-run generation after every schema or template edit.

Validate the generated markdown.
Verify files in docs/ match the current provider implementation.
Verify examples are valid HCL and reflect current argument/attribute names.
Verify required/optional/computed semantics in docs match schema behavior.

Apply Registry publication rules before release.
Use semantic version tags prefixed with v (for example v1.2.3).
Create release tags from the default branch.
Keep terraform-registry-manifest.json in the repository root.
Expect docs to be versioned in Registry and switchable with the version selector.

Preview or troubleshoot publication when needed.
Use the HashiCorp preview process to inspect rendered docs before release when accuracy risk is high.
If docs are missing in Registry, check tag format, tag source branch, manifest file presence, and provider publication status.

Enforce Quality Bar

Keep documentation behaviorally accurate; never describe unsupported arguments or attributes.
Keep examples minimal, realistic, and runnable.
Keep terminology and naming consistent across provider, resources, and data sources.
Avoid duplicating generated argument/attribute blocks in manual templates.
Keep doc changes tied to the same PR as schema/API changes whenever possible.

Load References On Demand

Read references/hashicorp-provider-docs.md for source-backed rules and official links.
Load only the sections needed for the current change to keep context lean.

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take hashicorp/provider-docs from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.