Foundry Agentic Loop
GitHubOfficialFirst committed by vieiraaeDistributed by GitHub
- Skill
- Workflow
- Software development
- MIT
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
foundry-agentic-loop provides a structured framework for developing agentic applications. It supports two primary modes: rapid prototyping for existing projects and Spec-Driven Development (SDD) for new projects requiring full Azure deployment pipelines. The skill manages the lifecycle of agents, toolbox tools, Foundry IQ grounding, and web application components, ensuring consistent architecture, security, and observability across the development process.
What it does
- Orchestrates end-to-end agentic solution development from architecture to Azure deployment.
- Provides a Spec-Driven Development workflow covering Specify, Implement, Verify, and Deploy phases.
- Supports rapid prototyping for existing Foundry projects.
- Manages Foundry hosted agents, toolbox tools, and Foundry IQ grounding configurations.
- Automates deployment of React/FastAPI applications to Azure Container Apps.
- Enforces security best practices through managed identities and least-privilege RBAC.
When to use it
- When building new agentic solutions on Microsoft Foundry that require a structured development lifecycle.
- When you need to deploy agentic applications to Azure using standardized pipelines.
- When prototyping agentic features within an existing Foundry project.
- When you need to integrate Foundry IQ grounding or custom toolbox tools into an agent.
Advantages
- Ensures architectural consistency through a contract-first development approach.
- Automates complex deployment tasks using azd and Bicep infrastructure layers.
- Provides built-in observability via OpenTelemetry and Application Insights integration.
- Maintains project history and lessons learned to improve future development cycles.
- Reduces manual configuration errors by enforcing standardized workspace structures.
Usage examples
- “Start a new agentic project using the SDD workflow.”
- The agent initializes the SDD workflow, creates the necessary project structure, and begins the Specify phase to define the project requirements.
- “Deploy my current Foundry project to Azure.”
- The agent validates the current configuration, runs the deployment pipeline, and reports the status of the Azure Container Apps deployment.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/github/foundry-agentic-loop/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip foundry-agentic-loop.zip -d .agents/skills
Documentation by vieiraae
Foundry Agentic Loop
Coordinate the solution and use microsoft-foundry for platform operations. Explicit user choices override defaults. Reuse known context, ask only for missing decisions, read only the references a step needs, and report decisions, blockers and endpoints concisely.
Bootstrap (once per session)
Foundry skill. If
microsoft-foundryis unavailable, ask, then install it (copilot plugin install azure@awesome-copilot, orgh skill install microsoft/azure-skills microsoft-foundry --agent github-copilot --scope project) and reload skills.Canvas and workflow. This plugin ships
foundry-agentic-loop-canvasandfoundry-agentic-loop-workflow, listed asfoundry-agentic-loop:<name>or<name>. Callextensions_managewithoperation: "list". Never install extensions from a URL and never edit Copilot settings files. If one is listed under neither name, tell the user which is missing and that it ships with the plugin, then ask before runningcopilot plugin install foundry-agentic-loop@awesome-copilot; after they approve, callextensions_reloadonce. If one is disabled, tell the user to re-enable it in their extension settings and continue in a new session: running sessions keep it disabled, even after a reload. If one failed,inspectit and report its log. Without the canvas, continue in Chat; without the workflow, SDD cannot start, so offer rapid prototyping.Canvas. When work starts from Chat, open it once:
open_canvaswithcanvasId: "foundry-agentic-loop-canvas",instanceId: "foundry-agentic-loop"andinput: { mode }. Prompts that begin[Agentic Loop canvas action]come from the canvas; follow their target, approval and resume instructions literally.
Choose the mode
| Signal | Mode |
|---|---|
Root AGENTS.md has an Agentic Loop section | Its mode. Switch only when the user confirms, then rewrite the section |
| Canvas prompt or explicit choice | As stated |
| Existing Foundry project selected or named; fast prototype; no deployment pipeline needed | Rapid prototyping: rapid.md |
| Spec, new Foundry project, azd or IaC, CI-ready repo or full Azure deployment | SDD: sdd.md |
| Improve, learn from runs, or the canvas improve prompt | Improve: improve.md; measure changes with node scripts/evals.mjs (index, then run --base <commit>; exit 2 means evals are unavailable, so stop) |
| One named SDD phase, with no run active | Its guide (Specify, Implement, Verify or Deploy) with the SDD defaults |
Otherwise ask once with the choices "Rapid prototyping (existing Foundry project)" and "SDD (new project, deployed with azd)".
Lock it in. Before any other workspace write (in the rapid preflight, or just before the SDD workflow starts), upsert the section from agents-md-rapid.md or agents-md-sdd.md into the root AGENTS.md: replace everything between its foundry-agentic-loop begin and end markers, or append it, keeping other content; then read it back. The section routes short commands such as deploy and locks the mode for Chat, the canvas and the workflow. Azure settings go only in .azure/deployment-plan.md, never in AGENTS.md. Improve mode never writes it.
Defaults
Agents: Foundry hosted agents exposing the Responses API, Python 3.13, code deployment. Create them through
microsoft-foundry'sfoundry-agent/create/quick-start-hosted.mdwith the Basic sample, resolved through that skill, never a guessed URL. Use its advanced workflow only when quick-start cannot support a capability, keeping these defaults. Prompt agents only when requested.Framework: the Copilot SDK for agents that use runtime skills (recipe, reference agent); Microsoft Agent Framework for basic agents and explicit workflows. If a workflow also needs skills, clarify. One framework per agent.
Models: rapid keeps the Basic sample's model deployment, or verifies and reuses a supplied one; SDD defaults to
gpt-5.4-miniunless the spec needs otherwise. Run the joint placement preflight in foundry-models.md before creating resources. Never hard-code a replacement or silently duplicate a deployment.Tools and knowledge: one governed Foundry toolbox for built-in tools, MCP connections and agent skills; agents use its endpoint, not hard-coded tool URLs. Private knowledge uses Foundry IQ through the toolbox, with citations for supported claims and a deterministic insufficient-evidence response; test both. Add Search only when needed. Reuse run skills the user names from the run catalog instead of regenerating them.
App: React + Vite, FastAPI and FastMCP (streamable HTTP) on Azure Container Apps. Rapid deploys with
az containerapp up --name <app> --source <dir> --resource-group <rg> --environment <env>; SDD uses azd with amicrosoft.foundrylayer plus a Bicep (AVM) app layer,remoteBuild: trueand one ACR pulled by managed identity. Add services from the reference architecture only when the solution needs them.App access: decide it from the data. When everything the app shows is sample, synthetic or deliberately public, default to no end-user sign-in, even for staff users; services still authenticate with managed identities and need no anonymous-session or identity-verification workarounds. When it shows private, sensitive or user-specific data, ask for the access model before deploying and default to internal ingress or Entra sign-in; anonymous access needs explicit approval. With sign-in, the API pre-authorizes the SPA, the spec states how automated browser tests get a user token, and Implement proves that token path as soon as the app registrations exist.
Identity and safety: managed identities and least-privilege keyless RBAC (contract; preflight with check_rbac_preflight.py). Check management-plane, data-plane and role-assignment rights separately; subscription Owner does not grant Foundry data-plane roles. Plan roles for identities not yet created and verify actual principals after creation. No credentials in browser code or Git; Foundry guardrails on.
Observability: OpenTelemetry from the backend, agents, MCP servers and model calls into one Application Insights connected to the project (observability.md). Sensitive content capture stays off unless approved with redaction and retention controls.
Workspace files
| File | Holds |
|---|---|
AGENTS.md (root) | The mode lock and short-command routes; never Azure settings |
README.md (rapid) or docs/spec.md (SDD, from the spec template) | The contract |
.azure/deployment-plan.md | Every Azure setting, in both modes (template) |
.env (Git-ignored) | What the canvas reads: FOUNDRY_PROJECT (full ARM ID, once validated or created), LOCAL_FRONTEND and DEPLOYED_FRONTEND (verified URLs only). Upsert, keep other keys and read back |
docs/LOOP.md | Lessons (template) |
.agentic-loop/runs/<runId>.json | SDD flight records, written by the workflow |
.devcontainer/devcontainer.json | Optional dev container (asset) |
Working rules
Contract first. Freeze service names, environment variables, protocols, endpoints, schemas, auth, streaming, errors and versions (implementation-contract.md) before parallel work.
Integration first. Pass the hosted integration checkpoint with a minimal agent before building full orchestration, UI or backend: always a model request, plus a toolbox skill load and a safe MCP call only when the architecture needs them. Record excluded capabilities as
N/Awith the reason, never as passed, and never add unused ones to satisfy the checkpoint.Evidence, not signals. HTTP 200, exit 0 or
activenever pass alone; require terminal completion, schema-valid output, execution evidence and correlated traces. Fix with bounded diagnostics, not unchanged redeployments.Wait by completion. Provisioning, deployments, role-assignment propagation and agent readiness end when their command or status check says so: run the command synchronously with a long initial wait, or poll its status every 30-60 s against a stated deadline. Never chain fixed sleeps; keep coding while Azure works.
Subagents. Before the checkpoint, parallelize only research and preflight. After it, give each worker a bounded goal, exclusive workspace-relative files or folders, skill references, verified context, the contract, acceptance checks and stop conditions. One owner (the coordinator, or the SDD phase lead) holds the contract and the shared files:
azure.yaml,infra/,docs/,.env,.azure/,.agentic-loop/,AGENTS.mdand the root lockfiles. Keep teams small, avoid recursive delegation, serialize shared Azure mutations and integrate centrally; worker success is not acceptance. Delegation grants no extra approval. Without subagents, work sequentially and say so.Safety. Reuse resources; confirm billable, security or compliance changes; delete without approval only a resource this build created whose provisioning failed; never sign in for the user; never report an unverified deployment as successful.
Workspace boundary. Builds stay inside the workspace: read lessons only from its own
docs/LOOP.md, scaffold from the Foundry samples and these references, and use code from other folders only when the user names them. Only improve mode reads past runs elsewhere.Work IQ context. Only when the user or a canvas prompt asks for it, query the
workiqMCP server before clarifying questions, fold in only the requirements that matter, cite each by source type and confirm them. Never copy confidential content, personal data or secrets into the workspace; if Work IQ is unavailable or needs consent, say so and continue without it.Companions and SDKs. Verify installed SDK APIs against the latest stable release (or the newest stable one the build's package index serves, recording the gap) and lock versions before generating code; package sources beat stale guidance. Consult the build catalog only for needed capabilities; respect pins, ask before installs and route third-party skill defects upstream.
Lessons
Right after a fix is verified (the failing probe or an equivalent boundary check reruns successfully; an edit, commit or deployment alone is not resolution), upsert a lesson in the workspace's docs/LOOP.md from the template before unrelated work, then read it back. Keep user notes, update matching lessons instead of duplicating them, and never include secrets, raw .env values or payloads. The coordinator, or the SDD phase lead, owns writes; workers report evidence. Lessons never authorize skill edits or weaker gates; improve mode applies them with approval.
Files
- ATTRIBUTION.mdAdded by AutoGineer873 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md12.1 KiB
- assets/agents-md-rapid.md1.1 KiB
- assets/agents-md-sdd.md1.4 KiB
- assets/deployment-plan-template.md3.0 KiB
- assets/devcontainer.json800 B
- assets/loop-template.md1.3 KiB
- assets/spec-template.md4.0 KiB
- evals/evals.json94.0 KiB
- evals/fixtures/improve-history/.agentic-loop/runs/wr_101.json2.4 KiB
- evals/fixtures/improve-history/.agentic-loop/runs/wr_117.json2.4 KiB
- evals/fixtures/improve-history/.agentic-loop/runs/wr_123.json1.6 KiB
- evals/fixtures/improve-history/AGENTS.md1.4 KiB
- evals/fixtures/improve-history/docs/LOOP.md2.0 KiB
- evals/fixtures/rapid-locked/.azure/deployment-plan.md868 B
- evals/fixtures/rapid-locked/.env207 B
- evals/fixtures/rapid-locked/AGENTS.md1.2 KiB
- evals/fixtures/rapid-locked/README.md1010 B
- evals/fixtures/rapid-locked/gitignore41 B
- evals/fixtures/sdd-deployed/.agentic-loop/runs/wr_88.json2.6 KiB
- evals/fixtures/sdd-deployed/.azure/deployment-plan.md1.6 KiB
- evals/fixtures/sdd-deployed/AGENTS.md1.4 KiB
- evals/fixtures/sdd-deployed/docs/spec.md1.9 KiB
- evals/fixtures/sdd-deployed/docs/verify.md374 B
- evals/fixtures/sdd-paused/.agentic-loop/runs/wr_42.json2.9 KiB
- evals/fixtures/sdd-paused/AGENTS.md1.4 KiB
- evals/fixtures/sdd-paused/docs/LOOP.md371 B
- evals/fixtures/sdd-paused/docs/spec.md1.9 KiB
- evals/fixtures/sdd-paused/docs/verify.md322 B
- evals/fixtures/sdd-paused/src/backend/routing.pyScript172 B
- evals/fixtures/sdd-paused/tests/contract/test_routing.pyScript208 B
- evals/fixtures/team-conventions/AGENTS.md224 B
- references/azd-layout.md11.3 KiB
- references/build-skills-catalog.md4.8 KiB
- references/check_rbac_preflight.pyScript18.9 KiB
- references/copilot-sdk-foundry.md16.6 KiB
- references/copilot-sdk-with-toolbox.pyScript14.3 KiB
- references/deploy.md4.4 KiB
- references/foundry-hosted-agent.md6.0 KiB
- references/foundry-iq-grounding.md6.8 KiB
- references/foundry-models.md5.2 KiB
- references/foundry-toolbox.md5.5 KiB
- references/hosted-integration-checkpoint.md13.4 KiB
- references/implement.md5.9 KiB
- references/implementation-contract.md3.7 KiB
- references/improve.md5.0 KiB
- references/observability.md5.9 KiB
- references/rapid.md8.9 KiB
- references/rbac-contract.md9.9 KiB
- references/rbac-preflight.md10.3 KiB
- references/reference-architecture.md3.4 KiB
- references/run-skills-catalog.md3.4 KiB
- references/sdd.md11.1 KiB
- references/specify.md4.4 KiB
- references/verify.md5.3 KiB
- scripts/evals.mjsScript22.0 KiB
references/hosted-integration-checkpoint.md
Hosted integration checkpoint
Read for architecture, the first hosted deployment, and changed identity/endpoint integrations. Prove the hosted boundary before expanding orchestration or implementing the full application. This is an acceptance gate, not a replacement for microsoft-foundry platform workflows.
Endpoint contract
Record these distinct values in the root README.md (rapid) or docs/implementation.md (SDD) and persist discovered values in the applicable deployment configuration and Git-ignored .env. Record their source, authentication audience, consuming component and version. Unknown values remain pending until discovery; do not synthesize endpoints from display names.
| Application variable | Meaning and validation |
|---|---|
APP_FOUNDRY_PROJECT_ENDPOINT | Full project URL including /api/projects/<project>, returned by project discovery. Used by project clients. |
MODEL_API_BASE_URL | Actual model-hosting account's OpenAI-compatible base ending in /openai/v1/. Used by the model provider; it is not the project or hosted-agent URL. Record model deployment name separately. |
HOSTED_RESPONSES_ENDPOINT | Deployed agent's actual Responses request URL, including required API version and agent routing. Record the agent version used by the probe; distinguish this complete request URL from an SDK base URL that appends /responses. |
TOOLBOX_ENDPOINT | Toolbox MCP URL, including api-version=v1, mapped from the azd-written TOOLBOX_<NAME>_MCP_ENDPOINT. Record the toolbox name, its published default version and whether the URL is the consumer form (follows the default) or pins /versions/<v>/. |
Keep FOUNDRY_PROJECT as the project ARM ID, not an endpoint. Keep platform-owned FOUNDRY_* variables untouched. A reported build had its custom FOUNDRY_PROJECT_ENDPOINT overwritten with an account-only URL; use APP_FOUNDRY_PROJECT_ENDPOINT for the application's verified full URL, explicitly pass it to project clients, and validate its path at startup. Do not silently append guessed project names or fall back to the reserved value. Verify how current adapters consume platform configuration through microsoft-foundry.
Minimal deployment and identity
Reuse the Basic sample, agreed framework and model deployment. Implement one deterministic, read-only probe that always requires a model response, plus one toolbox skill load when the architecture uses runtime skills and one MCP tool call when it uses MCP tools; the response uses their results. Never add an unused skill or tool to satisfy the checkpoint. Keep full UI, backend and multi-agent orchestration out of this slice. Record each excluded capability as
N/Awith its reason; mocks or skipped checks cannot pass a required boundary.Deploy only the necessary MCP dependency and toolbox skill/version, if any, then code-deploy the agent. Confirm approval for billable operations. Provisioning or
activeonly enables the probe; it does not pass it.Discover the deployed agent's actual principal/object ID from deployment/identity metadata (
azd ai agent show <agent> --output json:instance_identitytakes Azure roles; theblueprinttakes none, and its client ID only joins MCP Easy Auth allow-lists). A new principal cannot be checked before creation. Distinguish object ID from client/application ID and the agent resource ID. Record tenant, agent/version, principal, resource scope and required operation/role.Verify effective grants (including inheritance/conditions) and actual data-plane access. Use the following reported-build combinations as checks, not unconditional grants to every solution. Resolve current roles and least-privilege scope through
microsoft-foundry.
| Principal | Resource scope | Role to verify when this path requires it |
|---|---|---|
| Runtime instance identity | Foundry project | Foundry User for explicit project/toolbox access, including runtime skill download (without it the hosted invoke returns 500); account for platform-provided access and current role definitions. |
| Runtime instance identity | Account actually hosting the model | Cognitive Services OpenAI User for direct account-level OpenAI inference. Project access alone does not establish this. |
| Identity directly emitting authenticated Application Insights telemetry | Application Insights component | Monitoring Metrics Publisher. Verify the runtime instance identity when the agent is the emitter; do not substitute the deployer's grant. |
| Collector identity for native Azure Monitor OTLP ingestion, when used | Destination Data Collection Rule (DCR) | Monitoring Metrics Publisher. Verify the collector's actual principal, which can differ from the agent; do not add this route or grant when unused. |
Keep the deployer, project managed identity, agent identity, runtime caller and toolbox connection identity separate. Agent-to-toolbox access and connection-to-downstream MCP/service access are different checks. Confirm the configured connection auth mode and its principal/resource permissions; an agent grant cannot repair the wrong downstream identity.
Invoke the deployed endpoint with an authorized caller, but ensure downstream model/toolbox operations use the hosted workload credential and platform caller context. A developer token can authorize the external probe; it must not replace the agent's internal credential. No copied az login tokens or secret-key fallback to make the checkpoint pass.
Probes and pass criteria
Run a minimal direct model Responses request and toolbox skill/tool probe inside the hosted identity/context path, then run the equivalent SDK-mediated agent request. Record the actual skill version loaded and MCP tool name/call result using SDK/tool events or safe correlated telemetry, not the model's claim that it used them. A local run proves readiness and skill load only:
AgenticIdentityTokentool calls fail outside the host ("requires AgentInstanceClientId and AgentBlueprintClientId").Toolbox MCP calls can appear as
<server>___<tool>in Responses output: parse the server prefix and require the exact tool name. Check the toolbox version the agent uses: read back the published default version (azd ai toolbox show <name>) or, for a pinned URL, the version in its path, and require the expected tools and skills there.Exercise JSON and SSE using the actual returned content type. For SSE, consume through
response.completed; EOF,[DONE], text deltas,session.idleor a stopped spinner alone are insufficient. For nonstreaming JSON, require a final response object withstatus: "completed"; queued/in-progress responses require bounded polling. Also prove the streaming terminal event when streaming is supported.Treat
response.failed,response.incomplete, cancellation, timeout, protocol errors and embedded error payloads as failures, even with HTTP 200 or CLI exit code zero. A CLI wrapper must parse the response and return failure for unsuccessful terminal status.Require nonempty, schema-valid application output with expected probe values and evidence of every required skill/tool/model operation. Reject absent upstream fields explicitly; do not turn missing results into empty success.
Correlate application request ID, response ID, hosted session ID, SDK session/turn ID, agent version and service request IDs. Save a concise checkpoint record and evidence locations in the same file as the endpoint contract. Missing evidence leaves the gate blocked; label unavailable IDs as such, never invent them.
Require the deployed probe's hosting-request, model-request and required tool-execution spans to arrive in the configured telemetry destination, without enabling sensitive prompt/completion/tool-content capture. Verify trace/parent relationships and agent/version identification using the destination's actual schema. For Copilot SDK, configure both producers through the telemetry recipe. A project connection, exporter startup message or hosting-request span alone is insufficient. Allow bounded ingestion delay; missing required spans leave the checkpoint incomplete. Only after the required checks pass may full application work begin.
Diagnostic decision tree
| Failure | Next bounded action |
|---|---|
| Import, startup or dependency resolution | Capture exact Python, SDK, hosting adapter and running Copilot runtime versions. Check installed signatures and package/runtime compatibility. For remote-resolution failures use the conditional packaging fallback, not a framework rewrite. |
| 401 | Compare endpoint/base path, expected token audience, refresh callback and forwarded platform context for direct versus SDK requests under the same deployed identity. Log no tokens or caller-context header values. |
| 403 | Check actual principal/resource/role combinations and toolbox downstream identity separately. After a confirmed role change, retry the minimal probe with backoff and a fixed deadline (for example five attempts within five minutes). Refresh credentials through the supported provider; do not repeatedly redeploy identical code. |
| Developer probe passes, hosted probe fails | Compare the actual identities, network access, full project URL, model account and platform caller context. Developer success is not hosted proof. Do not disable network isolation to bypass a failure. |
| Direct hosted request passes, SDK fails | Compare sanitized method, route, deployment name, API version, audience, header names, content type and payload schema. Check adapter/SDK/runtime signatures before changing RBAC. |
| HTTP success but failed/missing output | Inspect response ID, final status, structured error and matching service request IDs. Parse JSON/SSE correctly; never reinterpret transport success as agent success. |
| Intermittent failures under load | Check request-local context, response-ID matching, session isolation, token refresh, cancellation and atomic tool-budget reservation before async work. |
| Missing or host-only telemetry | Check host and runtime producer configuration separately, active trace context, receiver/export route, destination/schema, actual exporter identity permissions, sampling and lifecycle flush. Allow bounded ingestion delay; do not pass the gate or repeatedly redeploy unchanged code. |
Transient postdeploy failure after a successful deploy | Rerun only the idempotent hook (azd hooks run postdeploy) and its read-back checks; do not redeploy unchanged code. |
Log safe structured diagnostics: application correlation ID, response ID, hosted and SDK session IDs, agent name/version, available service request IDs, SDK/runtime versions, status/error code and sanitized error summary. Capture response headers such as x-agent-session-id on failures too. Exclude Authorization, cookies, tokens, opaque caller-context values, .env dumps and sensitive bodies.
Retry only transient failures within a declared attempt/deadline budget; do not blindly replay non-idempotent tool calls. Redeploy only for changed code, dependencies or deployment configuration. Track debug processes and hosted/SDK sessions created by this task; stop only those temporary resources afterward. Preserve the user's sessions and the local app servers required by the acceptance handoff.
Contract tests
Add targeted tests before expanding each boundary, then run the applicable full-app cases at local acceptance:
Missing/null upstream IDs, output, tool results or schema fields produce explicit errors.
Responses text comes from the final
messageoutput item: the top-leveloutput_textcan be null when tool items precede it. Omitprevious_response_idon a first turn; null returns HTTP 400.JSON and SSE success, embedded errors, incomplete status, truncated streams and timeouts produce correct terminal state and error rendering. When the API emits only a terminal SSE event, call the agent without upstream streaming, which can take a different model path, and test that JSON and SSE return the same validated result.
Concurrent/interleaved requests map events and results to their own response/request/session IDs; a late result cannot resolve another request or overwrite its persisted state.
Reserve tool-call limits atomically before dispatch/await; simultaneous calls cannot exceed the cap. Test the boundary and rejected calls, including failure/cancellation accounting.
Token expiry/refresh and per-request context reach both model and toolbox without cross-user/session leakage.
Concurrent hosted requests retain separate trace/parent associations through runtime and tool callbacks. Check content-capture defaults, graceful flush and explicit export failures; local mocks do not replace deployed span-ingestion evidence.
App failures render clearly; retry, persistence/reload and export preserve validated results and failure state without secrets. Exercise mobile layout and keyboard navigation/focus as well as the happy path.
Platform sources
Resolve commands and changing platform contracts through the installed microsoft-foundry skill. References checked on 2026-09-22:
Requirements and compatibility
Written for: GitHub Copilot
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 an MCP server, Needs Python, Needs Node.js
Security
What our scanner noticed· Low risk
- 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
- First committed by
vieiraae
- Publisher
- GitHub (Official)
- Distributed by
- GitHub
- Source
- github/awesome-copilot · skills/foundry-agentic-loop · commit 797337f
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- vieiraae
AutoGineer is not affiliated with or endorsed by vieiraae or GitHub.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download foundry-agentic-loop.zip