agentleFS
MicrosoftKnown publishermicrosoft/chaos-studio/copilot-cli-plugin/skills/start-chaos/SKILL.md

start-chaos

Skill3 starsChanged 5 months agoMITView on GitHub

Orchestrate the full Chaos Studio v2 workflow: auth → workspace → scenario → run. Shares a single state file and supports resume.

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 5 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If you are an autonomous agent with no user to prompt — or you need typed, atomic operations to compose into your own workflow — use the

    line 16
  2. Very specific:

    chaos-studio MCP server: instead (see mcp/README.md).

    line 18
  3. Very specific:

    All pipeline logic lives in scripts/Invoke-StartChaos.ps1.

    line 28
  4. Distinctive:

    When exit 2 is followed by exit 3 on re-run, combine both user answers in the next invocation:

    line 58
  5. Distinctive:

    When exit 3 fires, show the user the parameter table from the output, ask which values they want to change, then re-run with autofill + …

    line 67
  6. Distinctive:

    Run execution, polling, action status tracking

    line 91
  7. Distinctive:

    All error cards with remediation commands

    line 93

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

StartChaos — Orchestrated Chaos Experiment Pipeline

⛔ ABSOLUTE RULE: Do NOT improvise, skip, or substitute any step. On ANY error, STOP and wait for the user.

When to use this skill (vs. the MCP server)

This skill is the human-interactive path: it persists state, renders cards, prompts for missing inputs, and generates an HTML report at the end. Use it when there is a user in the loop.

If you are an autonomous agent with no user to prompt — or you need typed, atomic operations to compose into your own workflow — use the chaos-studio MCP server instead (see mcp/README.md). The MCP exposes the same Chaos Studio v2 surface as discrete tools (chaos_create_workspace, chaos_list_recommended_scenarios, chaos_execute_scenario, …) without the state file, cards, or interactive prompts.

Both surfaces target Microsoft.Chaos 2026-05-01-preview and use the local az login session for auth.

How It Works

All pipeline logic lives in scripts/Invoke-StartChaos.ps1. The script handles auth, workspace creation, scenario setup, execution, permission validation/fix, and the final summary card.

The AI orchestrator's only job is:

  1. Set $env:STARTCHAOS_STATE_PATH to ${SESSION_DIR}/startchaos-state.json
  2. Run the script
  3. Handle exit codes that require user input (see below)
  4. Re-run the script with the user's answers as parameters

Running the Script

$env:STARTCHAOS_STATE_PATH = "<session-dir>/startchaos-state.json"
& "<skill-dir>/scripts/Invoke-StartChaos.ps1" @args

On resume: the script reads state and skips completed phases automatically.

Exit Codes → AI Actions

ExitMeaningAI Action
0Pipeline completeDone — summary card already rendered by script
1ErrorSTOP. Render the error from script output. Wait for user.
4Workspace inputs neededask_user for: resource group, workspace name, location, identity type, scopes. Re-run with -ResourceGroup, -WorkspaceName, -Location, -IdentityType, -Scopes.
2Scenario selection neededRead state.setup.recommendedScenarios from state file. ask_user with scenario names+descriptions as choices. Re-run with -ScenarioName <chosen>.
3Parameter mode neededShow the parameter table from the script output. ask_user which parameters they want to customize (or accept all defaults). Re-run with -ParameterMode autofill and -ParameterValues @{ key = 'value' } for any overrides.

Important: Exit 2 → 3 chaining

When exit 2 is followed by exit 3 on re-run, combine both user answers in the next invocation:

& Invoke-StartChaos.ps1 -ScenarioName "ZoneDown-1.0" -ParameterMode "autofill" -ParameterValues @{ duration = 'PT5M' }

Important: Manual mode does NOT work

-ParameterMode manual uses Read-Host prompts that do not render in a non-interactive AI session. Always use -ParameterMode autofill combined with -ParameterValues for any overrides the user requests. When exit 3 fires, show the user the parameter table from the output, ask which values they want to change, then re-run with autofill + overrides.

Script Parameters (passed through to sub-phases)

ParameterPhaseDescription
-ResourceGroup1Azure resource group
-WorkspaceName1Workspace name
-Location1Azure region (default: westus2)
-IdentityType1SystemAssigned or UserAssigned
-UserAssignedIdentityResourceId1Required when IdentityType=UserAssigned
-Scopes1ARM resource ID(s) for workspace scope
-ScenarioName2Selected scenario (e.g. ZoneDown-1.0)
-ParameterMode2autofill (always use this — manual does not work in AI sessions)
-ParameterValues2Hashtable of parameter overrides, e.g. @{ duration = 'PT5M' }. Applied on top of defaults during configuration creation.
-ForceReauth0Force re-authentication

What the Script Handles (no AI logic needed)

  • Phase sequencing and state-based resume
  • Auth pre-flight (existing session detection, device-code login)
  • Workspace creation, identity binding, RBAC grants
  • Scenario evaluation, recommendation discovery, configuration creation
  • Pre-run validation and fixResourcePermissions auto-remediation
  • Run execution, polling, action status tracking
  • Final summary card and HTML report generation
  • All error cards with remediation commands

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.