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 notque/vexjoy-agent --skill joy-checkgit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/joy-check)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/joy-check"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/joy-check/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/notque/vexjoy-agent/joy-check"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/joy-check.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 Privilege Escalation · line 143 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00013 | $0.01715 |
| Opus 5.5 | $0.00005 | $0.00686 |
| Sonnet 5.5 | $0.00003 | $0.00343 |
| Haiku 4.5 | $0.00001 | $0.00171 |
Grade A, and why
joy-check 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 25d 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.
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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Joy Check
Two modes:
- writing — Joy-grievance spectrum for human-facing content (blog posts, emails, articles). Evaluates curiosity/generosity vs. grievance/accusation framing.
- instruction — Positive framing for LLM-facing content (agents, skills, pipelines). Evaluates "what to do" vs. "what to avoid" (ADR-127).
Evaluates each paragraph/instruction independently, produces a score (0-100), suggests reframes without modifying content. Flags: --fix rewrites flagged items in place and re-verifies; --strict fails on any item below 60; --mode writing|instruction overrides auto-detection.
Checks framing, not topic or voice. The writing workflow owns voice fidelity and AI-pattern detection.
Reference Loading Table
| Signal | Load These Files | Why |
|---|---|---|
| Scoring agents, skills, pipelines, or toolkit documentation | references/instruction-rubric.md |
Positive-framing patterns, scoring, and examples. |
| Scoring articles, emails, posts, or other human-facing prose | references/writing-rubric.md |
Joy-grievance patterns, scoring, and examples. |
Instructions
Phase 0: DETECT MODE
Auto-detection (priority order):
- Explicit
--modeflag → use that agents/*.md→ instructionskills/*/SKILL.md→ instructionskills/workflow/references/*.md→ instructionCLAUDE.mdorREADME.md→ instruction- Everything else → writing
Load references/{mode}-rubric.md for scoring criteria and examples.
GATE: Mode determined, rubric loaded.
Phase 1: PRE-FILTER
Regex scanning as a fast gate before LLM semantic analysis.
Writing mode:
python3 ~/.claude/scripts/scan-negative-framing.py [file]
Instruction mode:
grep -nE 'NEVER|do NOT|must NOT|FORBIDDEN' [file]
grep -nE "^-?\s*Don't|^-?\s*Avoid|^#+.*Anti-[Pp]attern|^#+.*Avoid" [file]
Report findings with reframe suggestions from the rubric. If --fix, apply reframes and re-run.
GATE: Zero regex/grep hits. Resolve obvious patterns before Phase 2.
What ships with it
2 files 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.
- 25d ago Changed 1e8a58d91ff9
- 26d ago Changed · -27 lines 6008260b7f66
- 27d ago Changed · -1 lines c2b1b2d28c08
- 1mo ago First seen · 230 lines · 13 tokens per session scan A 80b883232f37
joy-check is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 1,715 once invoked, about $0.0001 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 2026-09-03.
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