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.
In this repository11 other files it gives its agents.
- samples/aks-zone-down-demo/AGENTS.mdAGENTS.md
- chaos-impactSkill
- chaos-study-designSkill
- chaos-study-historySkill
- chaos-study-reportSkill
- chaos-study-runSkill
- chaos-study-scopeSkill
- chaos-studySkill
- create-workspaceSkill
- run-scenarioSkill
- setup-scenarioSkill
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
- Distinctive: line 16
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
- Very specific: line 18
chaos-studioMCP server: instead (see mcp/README.md). - Very specific: line 28
All pipeline logic lives in scripts/Invoke-StartChaos.ps1.
- Distinctive: line 58
When exit 2 is followed by exit 3 on re-run, combine both user answers in the next invocation:
- Distinctive: line 67
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 + …
- Distinctive: line 91
Run execution, polling, action status tracking
- Distinctive: line 93
All error cards with remediation commands
≈ “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.
Files
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:
- Set
$env:STARTCHAOS_STATE_PATHto${SESSION_DIR}/startchaos-state.json - Run the script
- Handle exit codes that require user input (see below)
- 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" @argsOn resume: the script reads state and skips completed phases automatically.
Exit Codes → AI Actions
| Exit | Meaning | AI Action |
| 0 | Pipeline complete | Done — summary card already rendered by script |
| 1 | Error | STOP. Render the error from script output. Wait for user. |
| 4 | Workspace inputs needed | ask_user for: resource group, workspace name, location, identity type, scopes. Re-run with -ResourceGroup, -WorkspaceName, -Location, -IdentityType, -Scopes. |
| 2 | Scenario selection needed | Read state.setup.recommendedScenarios from state file. ask_user with scenario names+descriptions as choices. Re-run with -ScenarioName <chosen>. |
| 3 | Parameter mode needed | Show 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)
| Parameter | Phase | Description |
-ResourceGroup | 1 | Azure resource group |
-WorkspaceName | 1 | Workspace name |
-Location | 1 | Azure region (default: westus2) |
-IdentityType | 1 | SystemAssigned or UserAssigned |
-UserAssignedIdentityResourceId | 1 | Required when IdentityType=UserAssigned |
-Scopes | 1 | ARM resource ID(s) for workspace scope |
-ScenarioName | 2 | Selected scenario (e.g. ZoneDown-1.0) |
-ParameterMode | 2 | autofill (always use this — manual does not work in AI sessions) |
-ParameterValues | 2 | Hashtable of parameter overrides, e.g. @{ duration = 'PT5M' }. Applied on top of defaults during configuration creation. |
-ForceReauth | 0 | Force 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
fixResourcePermissionsauto-remediation - Run execution, polling, action status tracking
- Final summary card and HTML report generation
- All error cards with remediation commands
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
- Partly
- It didn't
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.

