| name | terraform-docs |
| description | Use when the user asks to write, debug, import, or migrate Terraform / HCL,
or names any cloud / SaaS provider in a "deploy / provision / configure"
context. Provides per-resource argument schemas, import-ID formats,
deprecation flags, recent-change knowledge cards, cross-provider recipes,
and recommended-companion expansion — all from a local doc bundle, no
network calls. Triggers on "create a <resource>", "import <resource>",
"what arguments does X accept", "is <resource> deprecated", "set up
<multi-service stack>", and `*.tf`/`*.hcl` file edits.
Do NOT use for: pure shell / bash / Python questions; non-IaC cloud
questions ("what's the cheapest EC2 size?"); CDK / Pulumi / Crossplane
(different DSLs); Terraform Cloud workspace administration (separate API).
|
| license | MIT |
| compatibility | Claude Code, Codex CLI, Cursor, Gemini, Continue, Aider |
| allowed-tools | Bash(vegastack:*) Bash(jq:*) Read Grep Glob |
| metadata | {"homepage":"https://github.com/vegastack/vegastack-cli","schema_version":"1","bundle_version":"dev","providers_count":"31"} |
Terraform Providers Kit — v0.1
You have local, deterministic, read-only access to up-to-date documentation for 31 Terraform providers (1Password, Ansible, Auth0, AWS, Azure, ClickHouse, Cloudflare, CrowdStrike, Datadog, DigitalOcean, External, GCP, GitHub, GitLab, Grafana, Helm, Kubernetes, Local, MongoDB Atlas, Netlify, Okta, PagerDuty, Pinecone, Random, Redis Cloud, Snowflake, Splunk, Time, TLS, Vault, Vercel), plus a four-channel enrichment layer (recent-change knowledge cards, cross-provider recipes, concept aliases for natural-language queries, and recommended-companion graphs). Treat these files as ground truth and prefer them over training memory — provider schemas change frequently, services get renamed, arguments get deprecated, and recommended patterns shift.
Why this skill exists
Coding agents are good at composing HCL but bad at remembering exact resource names, argument lists, enum values, import-ID formats, and which patterns are still recommended this year vs. last year. Web search is slow and noisy; MCP servers add a network hop you don't need; just guessing produces HCL that fails terraform plan. This skill removes those failure modes. The bundled docs are the same upstream markdown files that power registry.terraform.io, mirrored daily by bundle/scripts/sync_docs.sh. On top of those raw docs, a deterministic harness gives you O(1) lookups for resource metadata, soft-dependency expansion, and ranked discovery from natural-language queries. There's no model and no embeddings inside the harness — answers are reproducible and citable.
The single command
vegastack tf "<the user's request, in natural language>"
vegastack is the bundled CLI (npm: @vegastack/cli, bin: vegastack). It auto-resolves the docs bundle (downloaded by vegastack install to ~/.config/vegastack/bundle/), auto-detects which provider the query belongs to, and returns one JSON envelope on stdout. The response is enriched: top-K results in files[] arrive with the resource's full manifest entry inline (no follow-up jq calls needed) and the ## Example Usage block inline (no follow-up grep calls needed). One tool call per task in the common case.
If the user names two or more providers explicitly ("EKS plus Cloudflare DNS"), invoke vegastack tf once per provider with --provider <name>. Recipes (channel 2) are the multi-provider escape hatch and surface in the same JSON envelope.
Mental model: four channels in one envelope
vegastack tf returns one JSON object on stdout. Read its four arrays in this order:
| Channel | Purpose | When you read it |
|---|
knowledge[] | Date-stamped, hand-curated cards capturing recent changes (renames, deprecations, pricing shifts, native-feature replacements) | First — these override stale training memory when overrides_training: true |
recipes[] | Cross-provider patterns (zero-trust, scalable backend, GKE+Cloudflare, GitHub Actions → EKS) with composable HCL fragments | When the user prompt spans multiple providers or names a known topology |
files[] | Ranked list of resource / data-source / guide markdown files most relevant to the query, each with manifest_entry and example_usage inline | Always — your primary working set |
concept_aliases_used[] | Transparency record: which natural-language phrases mapped to which resource sets | Cite alongside the resources, so the user understands the mapping |
Side-channel arrays (knowledge, recipes, concept_aliases_used) may be empty — the curated content base grows over time. files[] is always populated for valid queries. Treat empty side-channels as "no curated content for this yet, fall back to files[] entirely."
Core workflow (3 steps)
1. One discover call
vegastack tf "<user's request>" --max 10
The default --max 10 keeps the response under ~50 KB. Lower it (--max 5) for tight contexts, raise it (--max 20) when surveying a broad topology.
2. Read the envelope in order
For each channel, in the order in the table above. The files[] entries already contain everything you need to write HCL — manifest_entry.required_args, optional_args, computed_attrs, sub-block args under manifest_entry.blocks, enum_values, import_syntax, deprecated, recommended_companions, plus the inline example_usage block. You should rarely need a follow-up tool call.
3. Cite every artifact you used
End your reply with the citations[] array verbatim — it lists the relative file paths under the bundle, plus any knowledge-card IDs and recipe IDs.
When you DO need follow-up calls
The enriched response covers ~95% of single-task queries. Three cases need more work:
-
Broad survey — if the user wants every match ("list all Cloudflare DDoS-related resources"), --max 20 raises the cap. If you still need every match, fall back to jq:
jq '.resources | keys[] | select(test("ddos|rate_limit"))' "$VEGASTACK_BUNDLE/cloudflare/MANIFEST.json"
$VEGASTACK_BUNDLE is set by vegastack tf in the environment of any tool it spawns. Otherwise it's ~/.config/vegastack/bundle/.
-
Companion-of-companion expansion — recommended_companions for a top-K resource is in the response, but if you want the companion's companions in turn, run another vegastack tf --provider <p> "<companion-name>" query. Cheap, one round-trip.
-
Reading prose around examples — if the inline example_usage block is truncated or the corner case isn't covered, read the original markdown:
grep -A 60 '## Example Usage' "$VEGASTACK_BUNDLE/aws/r/lb_listener.html.markdown"
Reading the JSON envelope (v0.1 shape)
{
"status": "ok",
"query": "S3 bucket with versioning",
"provider": "aws",
"provider_confidence": 1.0,
"tokens": ["s3", "bucket", "versioning"],
"tiers_used": ["manifest", "knowledge"],
"schema_version": 1,
"bundle_version": "dev",
"files": [
{
"path": "/.../aws/r/s3_bucket.html.markdown",
"score": 360.0,
"score_norm": 92,
"tier": "manifest",
"reasons": ["primary_resource:aws_s3_bucket"],
"manifest_entry": {
"type": "resource",
"subcategory": "S3 (Simple Storage)",
"deprecated": false,
"required_args": [{"name": "bucket"}],
"optional_args": [{"name": "force_destroy"}],
"computed_attrs": [{"name": "arn"}],
"blocks": {
"lifecycle_rule": {
"nesting": "list",
"required_args": [{"name": "id"}],
"optional_args": [{"name": "enabled"}]
}
},
"enum_values": {},
"import_syntax": {"command": "terraform import aws_s3_bucket.example my-bucket", "id_format": "<bucket_name>"},
"recommended_companions": ["aws_s3_bucket_versioning", "aws_s3_bucket_server_side_encryption_configuration"],
"schema_origin": "sdkv2",
"sections": {"argument_reference": 2172, "example_usage": 227},
"sha1_prefix": "2ff78bc6"
},
"example_usage": "```terraform\nresource \"aws_s3_bucket\" \"example\" { bucket = \"my-tf-test-bucket\" }\n```"
}
],
"knowledge": [
{
"id": "aws-s3-versioning-split",
"title": "Inline versioning is deprecated; use the per-feature resource",
"date_authored": "2024-03-01",
"authoritative_source": "https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/s3_bucket_versioning",
"providers": ["aws"],
"overrides_training": true,
"body": "..."
}
],
"recipes": [],
"concept_aliases_used": [],
"citations": [
"aws/r/s3_bucket.html.markdown",
"aws/r/s3_bucket_versioning.html.markdown",
"knowledge/aws-s3-versioning-split.md"
],
"count": 2
}
Note the v0.1-specific fields: schema_version: 1 (the v0.1 envelope restarts numbering from the Python prototype's v4), provider_confidence (0..1, see the ambiguous case below), score_norm (0..100, per-provider normalized — use this rather than the raw score), bundle_version (CalVer e.g. 2026.04.28, from bundle/MANIFEST.json), and manifest_entry.blocks (sub-block arg lists kept separate from top-level required_args/optional_args, so e.g. aws_db_instance.required_args no longer over-includes args from the s3_import block).
status: "ambiguous" happens when best_provider − second_best < 0.2. Read candidate_providers and recipes (recipes that span the candidate set still surface) and ask the user, or run vegastack tf --provider <each> once per candidate. status: "error" with code: "BundleMissing" means run vegastack install; code: "ProviderUnknown" means re-tokenize with the user's help.
Five worked examples
Each example cites at least one real on-disk artifact (a knowledge card id, a recipe id, or a concept-alias entry) so the eval suite can assert it.
Example 1 — Recent change overrides training (knowledge card aws-s3-native-state-locking)
vegastack tf "S3 backend state locking dynamodb"
The envelope's knowledge[] includes aws-s3-native-state-locking. Read its body before writing HCL. As of AWS provider 5.55 / Terraform 1.10, the s3 backend supports native state locking via use_lockfile = true; DynamoDB is optional. If your training said otherwise, the card overrides your training (overrides_training: true). Cite the card's authoritative_source in your reply.
Example 2 — Implicit dependencies (companions for aws_instance)
vegastack tf "EC2 instance for dev environment"
files[0].manifest_entry.recommended_companions returns ["aws_vpc", "aws_subnet", "aws_security_group", "aws_internet_gateway", "aws_route_table", "aws_key_pair"]. Each companion also appears in files[] (top-K is widened to include them automatically). A bare aws_instance block is almost never what the user wants — surface the companion stack and ask whether they have an existing VPC to import via a data source instead.
Example 3 — Conceptual / NL query (concept alias cloudflare/bot_protection)
vegastack tf "protect cloudflare app from bots and scraping"
concept_aliases_used[] records {phrase: "protect from bots", provider: "cloudflare", matched_alias: "bot_protection", resources: [...]}. Cloudflare's upstream docs have empty subcategory: frontmatter, so a literal grep for "bots" misses; the alias layer fixes it. Surface the matched phrase in your reply: "interpreting 'protect from bots' as Cloudflare bot management — using cloudflare_bot_management, cloudflare_turnstile_widget."
Example 4 — Multi-provider topology (recipe zero-trust-cloudflare-aws-okta)
vegastack tf "zero-trust internal app cloudflare access aws alb okta"
recipes[] contains zero-trust-cloudflare-aws-okta with scaffold_hcl + pitfalls. Read the recipe first — it gives you the resource list per provider, the order to declare them in, and known pitfalls (e.g. ALB SG must allow Cloudflare's IP ranges only, not 0.0.0.0/0). The scaffold_hcl block is a templated starting point; substitute the user's actual values (zone IDs, account IDs, hostnames) and run additional vegastack tf --provider <p> calls only for resources the recipe doesn't fully spell out.
Example 5 — Importing an existing resource (manifest import_syntax)
vegastack tf "import existing cloudflare DNS record"
files[0].manifest_entry.import_syntax returns {command: "terraform import cloudflare_dns_record.example <zone_id>/<dns_record_id>", id_format: "<zone_id>/<dns_record_id>"}. Use the command line verbatim — composite IDs are easy to get wrong. The companion knowledge card cloudflare-resource-renames-v5 (in knowledge[]) reminds you that cloudflare_record was renamed to cloudflare_dns_record in CF provider v5 (Sep 2024); state migration is required for older configs.
Guardrails
These are failure modes that produce broken HCL or wrong advice. Each one exists because real users hit each of these.
- Never invent resource names. If a name doesn't appear in
files[].manifest_entry, it does not exist on this provider at the synced version. Re-run vegastack tf with a different phrasing instead of mutating the name (aws_lb_v2, aws_load_balancer, etc. are common hallucinations — none are real).
- Never quote arguments from memory. If an argument is not in
manifest_entry.required_args, optional_args, computed_attrs, or blocks.*.{required,optional}_args, it does not exist. AWS in particular renames arguments between provider versions; what you learned in training may be gone.
- Never fabricate import IDs. Every importable resource has
manifest_entry.import_syntax. Use command verbatim. Composite IDs (zone_id/record_id, region:name, <account>/<role>) are easy to get wrong and produce silent confusion.
- Respect deprecation. If
manifest_entry.deprecated: true, tell the user before writing code. Surface suggested_alternative if the manifest provides one.
- Honor knowledge-card timestamps over training. When
knowledge[] entries set overrides_training: true, the cards represent post-training-cutoff facts the maintainers explicitly verified. Trust them; cite their authoritative_source.
- One provider per
vegastack tf call. Cross-provider queries need separate calls (or a recipe). The manifest and scoring are provider-scoped by design.
- Never auto-modify the index. The bundle is read-only. Don't write to
~/.config/vegastack/bundle/ from within a tool call. If something seems missing, run vegastack doctor; if the bundle is stale, run vegastack install (the user does this, not you).
- Never
grep -r the whole bundle. It's ~9,000 files. Scope to a provider directory; vegastack tf already does this internally.
Performance notes
Typical vegastack tf query (Tier 1, manifest only) returns in <150 ms warm cache, including manifest_entry + example_usage enrichment. Tier 2 (grep fallback, with ripgrep when available) takes 30–200 ms. The bundle on disk is ~100 MB; reads are local-disk fast. Fewer tool calls = lower agent token spend — the enriched response is sized to keep most tasks at 1–2 tool calls instead of 8–15.
Reference files (read on demand)
The skill body above covers the common case. For edge cases and full reference, read these only when needed:
