AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Foundry IQ provides a structured framework for creating, diagnosing, and connecting knowledge bases to AI agents. It supports making local or Blob-based documents searchable, managing multi-source knowledge configurations, and retrieving information with citations. The skill emphasizes strict adherence to defined procedures for service discovery, resource creation, and troubleshooting to ensure reliable agent-knowledge integration.
What it does
- Creates searchable knowledge bases from local or Azure Blob storage documents.
- Connects existing knowledge bases to AI agents.
- Diagnoses issues with knowledge base configurations or unsupported connectors.
- Retrieves information from knowledge bases with source citations.
- Manages the lifecycle of search services and knowledge sources.
- Routes requests based on specific operational needs rather than source counts.
When to use it
- When you need to make local or cloud-stored documents searchable by an AI agent.
- When connecting an existing knowledge base to a new or current agent.
- When troubleshooting or diagnosing a knowledge base configuration.
- When you need to create a new search service or knowledge source.
- When performing retrieval tasks that require citations from a knowledge base.
Advantages
- Provides a clear, procedural approach to knowledge base management.
- Ensures consistent handling of multi-source knowledge configurations.
- Includes built-in diagnostic paths for troubleshooting.
- Supports secure, citation-backed retrieval for agent responses.
- Prevents unauthorized or unsupported configuration changes.
Usage examples
- “Create a new knowledge base from my Azure Blob container.”
- The agent will initiate the knowledge base creation procedure, validating the source and setting up the necessary search indexing.
- “Connect my existing knowledge base to the current agent.”
- The agent will follow the connection workflow to link the specified knowledge base to the agent's retrieval pipeline.
- “Why is my multi-source knowledge base failing to index?”
- The agent will trigger the diagnostic procedure to identify configuration drifts or unsupported connector issues.
- “Retrieve information about project timelines from the knowledge base.”
- The agent will query the knowledge base and provide an answer with citations from the source documents.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/foundry-iq/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip foundry-iq.zip -d .agents/skills
Documentation by Microsoft
Foundry IQ
Read one procedure before questions/actions; invocation is not a read.
Read the owning procedure before blocked/unsupported responses too.
Before mutation plans/approval, successfully read its required pre-action references, selected
branches only. Do not reload successful reads. On failure, try only permitted
bounded exact-path reads with any supported reader; never bypass restrictions
or search broadly. If still unavailable: blocked: reference-unavailable, name
missing references and inability to plan. Never invent requirements/plans.
Failures first.
Cleanup and receipt-backed execution go directly to their lifecycle/producer owner; do not reopen Search intake, provisioning or hardening.
Before expensive service discovery, reuse supplied resource/intent; otherwise ask one early USE EXISTING / FIND CANDIDATES / CREATE NEW choice. Only FIND enumerates; supplied identity uses exact/minimum scoped resolution. CREATE checks its proposed name, not existing-service inventories. Preserve these answers across source/KB/CU/model handoffs; selection is not write approval.
Searchable docs: KB + validated retrieval. Confirm intent once; KS-only must be explicit. Child success is not KB completion. Unclear/compound/mode/completion: read.
Route by requested operation, not existing KB source count. Connecting an existing KB with two or more sources, without changing the KB, uses Connect. Read-only operations use their owning procedure and actual helper constraints; this does not add retrieval modes or supported source kinds. Explicit unsupported provisioning or multi-source KB creation/reconfiguration uses Diagnose and stops before discovery, even when connecting an agent is also requested. This routing takes precedence over classic-search terms (e.g. hybrid search) in compound requests. Never end in a plain refusal: Diagnose gives the boundary, docs pointer and agent handoff; writes still need an approved concrete plan. Do not silently execute only the supported part of a compound request. If existing-KB connection versus KB creation/reconfiguration is unclear, read intent-routing and clarify that scope before Azure discovery. Never infer KB mutation.
| Outcome | Read |
|---|---|
| Cleanup | Plan cleanup |
| Failure/drift | Diagnose |
| Unsupported connector provisioning / multi-source KB creation or reconfiguration | Diagnose |
| Connect | Connect |
| Read KB | Query |
| Search only | Search |
| File KS only | File |
| Blob/ADLS KS only | Blob |
| Searchable/KB | KB |
Generic agents: microsoft-foundry.
Reads: no approval; approve unchanged plans before writes.
Hide hashes. No Search/Storage keys, scope widening, guessed identity/boundaries,
drift repair or joint cleanup/creation approval. Acceptance != success.
Files
- ATTRIBUTION.mdAdded by AutoGineer977 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md3.7 KiB
- agents/connect-hosted-toolbox.md7.8 KiB
- agents/connect-hosted.md7.8 KiB
- agents/connect-prompt-sdk-fallback.md7.8 KiB
- agents/connect.md7.8 KiB
- agents/create-missing-agent.md7.8 KiB
- helpers/_blob_observation.pyScript4.4 KiB
- helpers/_blob_source_read.pyScript39.5 KiB
- helpers/_bootstrap_io.pyScript30.1 KiB
- helpers/_cleanup_dependencies.pyScript27.5 KiB
- helpers/_cleanup_receipts.pyScript13.5 KiB
- helpers/_common.pyScript36.2 KiB
- helpers/_document_adapters.pyScript15.0 KiB
- helpers/_document_limits.pyScript3.9 KiB
- helpers/_embedding.pyScript7.0 KiB
- helpers/_file_inventory.pyScript24.4 KiB
- helpers/_file_source_config.pyScript15.6 KiB
- helpers/_indexer_observation.pyScript7.1 KiB
- helpers/_initial_prompt.pyScript12.5 KiB
- helpers/_private_json.pyScript2.4 KiB
- helpers/_progress.pyScript12.1 KiB
- helpers/_prompt_read.pyScript17.7 KiB
- helpers/_search_read.pyScript6.0 KiB
- helpers/_source_readback.pyScript1.6 KiB
- helpers/blob-contracts.md7.8 KiB
- helpers/blob-cu-contracts.md7.8 KiB
- helpers/blob_inventory.pyScript17.1 KiB
- helpers/blob_recheck.pyScript64.6 KiB
- helpers/blob_source.pyScript35.8 KiB
- helpers/bootstrap-contracts.md7.8 KiB
- helpers/bootstrap_azure.pyScript41.5 KiB
- helpers/cleanup_plan.pyScript41.4 KiB
- helpers/contracts.md7.8 KiB
- helpers/cu_ingestion_auth.pyScript12.5 KiB
- helpers/document-assessment.md7.8 KiB
- helpers/document_assess.pyScript14.0 KiB
- helpers/file-upload-recovery.md7.8 KiB
- helpers/file_cu_canary.pyScript32.7 KiB
- helpers/file_cu_mi.pyScript9.6 KiB
- helpers/file_ingest.pyScript15.4 KiB
- helpers/file_source.pyScript31.7 KiB
- helpers/file_upload.pyScript53.7 KiB
- helpers/hosted_connect.pyScript30.8 KiB
- helpers/kb-contracts.md7.8 KiB
- helpers/knowledge_base_retrieve.pyScript7.7 KiB
- helpers/model-discovery-contracts.md7.8 KiB
- helpers/model-discovery-scopes.md7.8 KiB
- helpers/model_discovery.pyScript23.7 KiB
- helpers/private-artifacts.md7.2 KiB
- helpers/private_artifacts.pyScript4.8 KiB
- helpers/prompt_cleanup.pyScript22.0 KiB
- helpers/prompt_connect.pyScript62.3 KiB
- helpers/requirements-assessment.txt103 B
- helpers/retrieval-verification-contracts.md7.8 KiB
- helpers/retrieval_verify.pyScript19.6 KiB
- helpers/search-intake-contracts.md7.6 KiB
- helpers/search_intake.pyScript18.4 KiB
- helpers/search_reconcile.pyScript59.7 KiB
- helpers/source_vector.pyScript33.2 KiB
- helpers/throttle-recovery.md5.0 KiB
- helpers/vector-contracts.md7.8 KiB
- knowledge-bases/create.md8.1 KiB
- knowledge-bases/retrieve.md7.8 KiB
- knowledge-sources/create-azure-blob.md7.8 KiB
- knowledge-sources/create-file.md7.8 KiB
- lifecycle/cleanup.md7.8 KiB
- references/abstention.md1.0 KiB
- references/blob-binding-evidence.md7.8 KiB
- references/blob-indexer-observation.md7.7 KiB
- references/blob-ingestion-progress.md6.5 KiB
- references/blob-readiness-recheck.md7.8 KiB
- references/blob-vector-readiness.md2.6 KiB
- references/bootstrap-azure.md7.8 KiB
- references/content-fit.md7.8 KiB
- references/cu-ingestion.md7.8 KiB
- references/file-cu-canary.md7.8 KiB
- references/intent-routing.md7.7 KiB
- references/owner.md2.1 KiB
- references/platform-contracts.md7.7 KiB
- references/platform-interfaces.md7.8 KiB
- references/resource-intake.md7.8 KiB
- references/search-substrate.md7.8 KiB
- references/standard-cu.md7.8 KiB
- search-services/create.md7.8 KiB
- troubleshooting/diagnose.md8.7 KiB
helpers/_search_read.py
"""Search definition, version evidence and read-only resource access."""
from __future__ import annotations
import re
from typing import Any
from urllib.parse import urlencode
try:
from ._common import (
HelperFailure, HttpResult, ReadRecovery, Transport, odata_name, validate_search_endpoint,
)
except ImportError:
from _common import (
HelperFailure, HttpResult, ReadRecovery, Transport, odata_name, validate_search_endpoint,
)
SUPPORTED_API_VERSIONS = {"2026-04-01", "2026-08-01-preview"}
RESOURCE_SEGMENTS = {
"knowledge-source": "knowledgesources",
"knowledge-base": "knowledgebases",
}
DYNAMIC_FIELDS = {
"@odata.context",
"@odata.etag",
"currentSynchronizationState",
"lastSynchronizationState",
"synchronizationStatus",
"apiKey",
"createdResources",
# Azure Search always returns "<redacted>" for connectionString on GET
# (secret redaction), never the submitted value, so it can never be
# compared for exact equality against a desired definition.
"connectionString",
}
# Search normalizes these endpoint fields inconsistently on readback.
URI_FIELDS = {"resourceUri", "uri"}
SHA256 = re.compile(r"^sha256:[a-f0-9]{64}$")
def _odata_name(name: Any) -> str:
return odata_name(name)
def _resource_url(plan: dict[str, Any]) -> str:
endpoint = validate_search_endpoint(plan.get("endpoint"))
resource_type = plan.get("resource_type")
segment = RESOURCE_SEGMENTS.get(resource_type)
if segment is None:
raise HelperFailure(
"resource-type-invalid",
"resource_type must be knowledge-source or knowledge-base.",
blocked_at="input-resolution",
)
api_version = plan.get("api_version")
if api_version not in SUPPORTED_API_VERSIONS:
raise HelperFailure(
"api-version-invalid",
"API version must be 2026-04-01 or 2026-08-01-preview.",
blocked_at="input-resolution",
)
return (
f"{endpoint}/{segment}('{_odata_name(plan.get('name'))}')?"
+ urlencode({"api-version": api_version})
)
def _definition(value: Any, *, key: str | None = None) -> Any:
if isinstance(value, dict):
result = {
child_key: _definition(child, key=child_key)
for child_key, child in sorted(value.items())
if child_key not in DYNAMIC_FIELDS and child is not None
}
# Preview returns this documented default even when omitted on creation.
if value.get("kind") == "azureBlob" and result.get("resultsProcessing") == "rerank":
result.pop("resultsProcessing")
return {
child_key: child
for child_key, child in result.items()
if child not in ({}, [])
}
if isinstance(value, list):
return [_definition(child) for child in value]
# Azure Search silently strips a single trailing slash from
# azureOpenAIParameters.resourceUri on readback (while preserving it
# verbatim on aiServices.uri), so a byte-exact comparison would
# false-negative on functionally identical endpoints that differ only
# by a trailing slash. Normalize both known endpoint-URI field names.
if (
key in URI_FIELDS
and isinstance(value, str)
and value.endswith("/")
and len(value) > 1
):
# Remove only one trailing slash; preserve intentional extra
# slashes (e.g. "https://example.com//") for exact comparison.
return value[:-1]
return value
def response_etags(result: HttpResult) -> dict[str, Any]:
return {
"body": result.body.get("@odata.etag") if isinstance(result.body, dict) else None,
"headers": list(result.etag_values) if result.etag_values is not None else [
value for name, value in result.headers.items() if name.lower() == "etag"
],
}
def resolve_etag(evidence: dict[str, Any], request_id: str | None = None) -> str | None:
values = [*evidence["headers"]]
if evidence["body"] is not None:
values.append(evidence["body"])
if any(not isinstance(value, str) or not value.strip() for value in values):
raise HelperFailure("etag-invalid", "Search returned malformed version evidence.",
blocked_at="verification", request_id=request_id)
if len(set(values)) > 1:
raise HelperFailure("etag-conflict", "Search response header/body ETags conflict.",
blocked_at="verification", request_id=request_id)
return values[0] if values else None
def _get(
url: str,
token: str,
*,
transport: Transport,
recovery: ReadRecovery | None = None,
) -> tuple[dict[str, Any] | None, str | None]:
try:
result = (recovery.get(url, token, transport=transport) if recovery is not None
else transport("GET", url, token))
except HelperFailure as failure:
if failure.http_status == 404 and failure.blocked_at != "local-persistence":
return None, failure.request_id
raise
if result.status != 200 or not isinstance(result.body, dict):
raise HelperFailure(
"readback-invalid",
"Search resource readback did not return one JSON object.",
blocked_at="reconciliation",
request_id=result.request_id,
status=result.status,
)
etag = resolve_etag(response_etags(result), result.request_id)
body = {**result.body, "@odata.etag": etag} if etag is not None else result.body
return body, result.request_id
def resource_url(plan: dict[str, Any]) -> str:
"""Validate and address the exact selected Search resource."""
return _resource_url(plan)
def read_resource(
url: str, token: str, *, transport: Transport
) -> tuple[dict[str, Any] | None, str | None]:
"""Read one identity; only a definitive 404 means absent."""
return _get(url, token, transport=transport)
def definitions_match(desired: dict[str, Any], current: dict[str, Any]) -> bool:
return _definition(desired) == _definition(current)
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
The author says: Azure
Security
What our scanner noticed· Medium risk
- References to credential files
- Instructions that use the network
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/skills · .github/plugins/foundry-iq-skills/skills/foundry-iq · commit 0696600
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from .github/plugins/foundry-iq-skills/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Microsoft.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download foundry-iq.zip