Babysitter is a workflow engine for AI coding agents that enforces predefined steps, quality checks, human approvals, and decision records. It is used to coordinate complex, repeatable agent workflows across supported coding tools. The catalogue contains skills, agents, instructions, settings, a plugin, and an MCP integration for its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add a5c-ai/babysitter --skill mcp-csp-investigationgit clone --depth 1 https://github.com/a5c-ai/babysitterWrote 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/a5c-ai/babysitter/mcp-csp-investigation)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/mcp-csp-investigation"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/mcp-csp-investigation/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/a5c-ai/babysitter/mcp-csp-investigation"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/mcp-csp-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 22 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00043 | $0.02247 |
| Opus 5 | $0.00022 | $0.01123 |
| Sonnet 5 | $0.00009 | $0.00449 |
| Haiku 4.5 | $0.00004 | $0.00225 |
Grade A, and why
mcp-csp-investigation scanned grade A with 1 finding 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 6d ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
fetch(apiUrl); // Works for default, fails for custom API_URL How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mcp-csp-investigation
Perform exhaustive Content Security Policy audits for MCP Apps running in sandboxed iframes where all network requests fail SILENTLY without proper CSP declarations.
Overview
MCP Apps run in sandboxed iframes with no same-origin server. This means:
- ALL network requests fail silently without CSP -- no errors, no warnings, just silent failure
- Every external origin (CDN, API, font, image, WebSocket) must be declared in CSP
- CSP is configured in the
contents[]return from theregisterAppResourceread callback - Missing even ONE origin causes that resource to silently not load
This skill provides a systematic methodology for discovering every network origin an app uses, tracing each to its source, and generating the correct CSP configuration.
Capabilities
Build Output Analysis
- Build the application and capture all output files (HTML, CSS, JS, assets)
- Search every file for network origin references
- Identify fetch/XHR targets, script sources, link hrefs, image sources, font URLs, iframe sources, WebSocket endpoints
Origin Tracing
- Trace each discovered origin to its source in the codebase
- Classify as: hardcoded constant, environment variable, or conditional logic
- Document whether origin is universal, dev-only, or prod-only
- Check third-party libraries for hidden network requests (analytics, telemetry, CDN fallbacks)
CSP Domain Categorization
- resourceDomains: Scripts, stylesheets, images, fonts (maps to
script-src,style-src,img-src,font-src) - connectDomains: fetch/XHR targets, WebSocket endpoints (maps to
connect-src) - frameDomains: Nested iframes (maps to
frame-src)
CSP Configuration Generation
- Generate the CSP object for
registerAppResourceread callback - Handle environment-specific origins with proper conditional logic
- Verify conditional origins have matching runtime URL and CSP entry
Usage
Step 1: Build the Application
# Build to produce final output files
npm run build
# Identify all output files
find dist/ -type f \( -name "*.html" -o -name "*.js" -o -name "*.css" \)
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.
- 6d ago First seen · 259 lines · 43 tokens per session scan A 70e54f99815f
mcp-csp-investigation is a skill published in the GitHub repository a5c-ai/babysitter (1,788 stars, last pushed 6d ago), licensed MIT. It adds 43 tokens to every session and 2,247 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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