Fix Terraform provider end user documentation issues detected by swissshepherd (ss). Removes an ignored target from the config, runs ss, validates findings, fixes the documentation, and commits.
npx skills add https://github.com/hashicorp/terraform-provider-aws --skill fixdocs
<!-- Copyright IBM Corp. 2014, 2026 -->
<!-- SPDX-License-Identifier: MPL-2.0 -->
swissshepherdFix Terraform provider documentation issues by removing targets from the swissshepherd ignore list, validating findings against the schema, and correcting the documentation.
Trigger this skill when the user:
Optional:
aws_s3_bucket) or prefix (e.g., aws_s3_)resource, data_source, ephemeral, etc.)If no target is specified, pick the next one from ignore_targets in .ci/swissshepherd-weak.hcl.
make swissshepherd
This MUST output "All checks passed." before proceeding. If it doesn't, stop and tell the user the baseline is dirty.
.ci/swissshepherd-weak.hclignore_targets list (within a check block)swissshepherd --config .ci/swissshepherd-weak.hcl --target <name> --type <type>
If "All checks passed" — the target was already clean. Commit the config removal and move to the next target.
For each finding, determine if it's valid by checking the schema source of truth:
internal/service/<service>/<resource>.go or *_data_source.go) to confirm the attribute/block exists in the schema.If a finding appears to be a swissshepherd bug (schema says one thing, ss reports another), note it and skip — do NOT fix the doc incorrectly.
BOTH WARNINGS AND ERRORS should be fixed!!
Open the doc file (path is in the ss output) and apply fixes:
| Finding | Fix |
|---------|-----|
| "block X is not documented" | Add a ### \block_name\ Block section with its attributes listed |
| "attribute X should be documented in Attribute Reference" | Add to Attribute Reference section |
| "attribute X should not appear in Argument Reference" | Move from Arguments to Attributes |
| "documented attribute X does not exist in schema" | Remove from docs (it's phantom) |
| "missing (Required) or (Optional) label" | Add the correct label based on schema |
| "heading ... should be ..." | Rename to the suggested heading |
| "byline does not match expected texts" | Replace with a standard byline |
| "reference-style link definition" | Convert ref]: url to inline [text |
When adding or editing documentation:
* \name\ - (Required) Description. or * \name\ - (Optional) Description.* \name\ - Description. (no Required/Optional label) ### block_name Block Authoritative reference: docs/end-user-documentation.md. When this skill and that document disagree, the document wins.
swissshepherd --config .ci/swissshepherd-weak.hcl --target <name> --type <type>
Must output "All checks passed." If not, iterate on remaining findings.
make swissshepherd
Must output "All checks passed." to confirm no regressions.
Stage and commit:
git add .ci/swissshepherd-weak.hcl website/docs/
git commit -m "<resource_name>: Fix documentation per swissshepherd"
Use the resource name without the aws_ prefix in the commit message scope when it matches a single service. For multi-target batches, use the service name.
--config .ci/swissshepherd-weak.hcl — running without config produces 20,000+ findings.ignore_targets under check "schema_docs" AND check "import_section" (or others). Remove from all.ignore_contents_check unless the user explicitly asks — those are structural exceptions. ### y Block nested contextually after the parent.User: "fix docs for aws_s3_bucket_lifecycle_configuration"
make swissshepherd passesaws_s3_bucket_lifecycle_configuration from ignore_targets in the schema_docs check blockswissshepherd --config .ci/swissshepherd-weak.hcl --target aws_s3_bucket_lifecycle_configuration --type resourceERROR [schema_docs] ... block "rule.filter" is not documentedfilter block exists under rule ### filter Block section with its attributesmake swissshepherd — passesAssess 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.
Take hashicorp/fixdocs from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.