Borrowing it
Nothing to install: this file belongs to KeepSoftwareSimple/compass-calendar. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/KeepSoftwareSimple/compass-calendar/main/.agents/skills/chaos/SKILL.mdgit clone --depth 1 https://github.com/KeepSoftwareSimple/compass-calendarWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/keepsoftwaresimple/compass-calendar/chaos)<a href="https://agentmods.dev/skills/keepsoftwaresimple/compass-calendar/chaos"><img src="https://agentmods.dev/badge/skills/keepsoftwaresimple/compass-calendar/chaos/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/keepsoftwaresimple/compass-calendar/chaos"><img src="https://agentmods.dev/badge/skills/keepsoftwaresimple/compass-calendar/chaos.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.01596 |
| Opus 5.5 | $0.00032 | $0.00638 |
| Sonnet 5.5 | $0.00016 | $0.00319 |
| Haiku 4.5 | $0.00008 | $0.00160 |
Grade A, and why
chaos scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When
Before a production release, or when asked for exploratory / chaos testing.
Steps
Prepare → happy path → focused probes → diagnose/fix → simplify/review/ship.
Output
Evidence of signed-in exercise plus shipped fixes, or a report of stops.
Pass
Happy path worked; probes were focused; credentials were not entered by the agent.
Anti-patterns
Do not enter credentials. See
anti-patterns.md.
Escalate
Auth/setup missing, production-only defect, or a product fork.
Chaos
Treat a real, existing signed-in profile as an observation and interaction surface—not a source for credentials, cookies, tokens, or private data. Build confidence from visible behavior and reproducible evidence, then make the smallest fix that removes a confirmed defect.
Boundaries
- Target local development or an explicitly named non-production environment. Never probe production, payment flows, or third-party systems by default.
- Verify the selected account visibly before mutating data. If the expected test account is unknown, ambiguous, or not signed in, stop and ask the user.
- Use unique, clearly test-labelled records. Preserve pre-existing records; remove only records created by this run through the normal recoverable UI, then reload to confirm cleanup persisted.
- Do not enter credentials, grant/revoke OAuth, change account/security settings, bypass browser security warnings, or perform irreversible actions. Report those cases with a safe manual reproduction instead.
- Bound adversarial testing: do not use load generation, fuzz external APIs, scrape data, bypass authorization, or test other users' data. Test only the current account and normal product surfaces.
- Treat an exception, failed request, data loss, stale UI, inaccessible flow, privacy exposure, or significant interaction latency as a finding even when the UI appears to complete the action.
1. Prepare the test surface
- Read
AGENTS.md, inspect the current branch and diff, and identify the release-critical and recently changed user-visible flows. - Read the relevant acceptance docs and tests. Follow
docs/development/local-development.mdif the required local services or configuration are not healthy. - Select the best available real-browser surface that holds the signed-in test session. Honor an explicitly requested browser. Prefer a user-connected browser or Chrome session for authenticated flows; use Computer Use only when a browser-specific tool cannot perform the required visible action. Use isolated Playwright only for repeatable coverage that does not require the real account.
- Navigate to the named environment, identify the account from rendered UI, and record the target URL, browser surface, account state, and baseline console/network condition. Do not inspect browser storage, cookies, or passwords.
- If the selected environment or session is not suitable, stop with the exact setup needed. Do not substitute anonymous coverage for an authenticated pass.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 158 lines · 79 tokens per session scan A b3caeb0e332f
chaos is a skill published in the GitHub repository KeepSoftwareSimple/compass-calendar (226 stars, last pushed today), licensed MIT. It adds 79 tokens to every session and 1,596 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-07.
chaos compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| chaosKeepSoftwareSimple/compass-calendar | Skill | 226 | today | MIT | A | 79 | Codex |
| playwright-page-objectsbartstc/vite-ts-react-template | Skill | 123 | 15d ago | MIT | A | 30 | Claude Code |
| webapp-testingDDS-Solutions/AI-TadPole-OS | Skill | 8 | 2d ago | MIT | A | 28 | Claude Code |
| next-cache-components-optimizervercel/next.js | Skill | 143k | yesterday | MIT | A | 123 | Claude Code |
| next-partial-prefetching-optimizervercel/next.js | Skill | 143k | yesterday | MIT | A | 82 | Claude Code |
| merge-seedkriasoft/react-starter-kit | Skill | 24k | 6d ago | MIT | A | 49 | Claude Code, Codex |
| ux-acceptance-coordinatoremdash-cms/emdash | Skill | 14k | today | MIT | A | 59 | Claude Code, Codex |