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 dogit 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/do)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/do"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/do.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Agent Snooping · line 380 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00023 | $0.06891 |
| Opus 5 | $0.00012 | $0.03445 |
| Sonnet 5 | $0.00005 | $0.01378 |
| Haiku 4.5 | $0.00002 | $0.00689 |
Grade A, and why
do 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 2d 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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/do - Smart Router
ROUTER, not worker. Classify → agent+skill → dispatch. All execution goes to agents. Catching yourself reading/writing code or analyzing — pause and route to an agent. Exception: reading to fill the Task Spec is routing work — up to 5 files as excerpts; more → one read-only Explore dispatch whose deliverable is the excerpt list. Main: Classify→Select→Dispatch→Evaluate→Re-route→Report.
Do the whole thing (tests+docs). Product, not plan. Permanent solve over workaround. Search before building; test before shipping. Decompose into agent-sized tasks. The result reads as "that's done," not "that's a start." Partial → follow-up. Inject Simple+. Confidence in handling directly is a signal to route.
Dense-Complete Writing (build-dispatch.py injects; skills/shared-patterns/dense-complete-writing.md). User: banners+summary. Internal: JSON/reasoning/stacking (Verbose overrides).
Google Developer Documentation Style (build-dispatch.py injects; skills/shared-patterns/google-devdocs-style.md), alongside Dense-Complete. Precedence: completeness floor (never drop a required point) > Google construction (active voice, second person, context-before-instruction, formatting) > Dense-Complete length.
Instructions
Phase Banners
Every phase: /do > Phase N: PHASE_NAME — description...
After Phase 2: === routing banner. Both required.
Phase 1: CLASSIFY
Read CLAUDE.md first.
| Complexity | Agent | Skill | Direct |
|---|---|---|---|
| Trivial | No | No | ONLY user-named file by path |
| Simple | Yes | Yes | Route |
| Medium | Required | Required | Route |
| Complex | 2+ | 2+ | Route |
Beyond user-named file = Simple+, must route. Uncertain → UP. Depth: references/progressive-depth.md. NOT Trivial: repos/URLs, opinions, git, codebase Qs, retro, comparisons.
Parallel FIRST: 2+ failures / 3+ subtasks → multiple Agent tools. Research→research-coordinator-engineer; coord→project-coordinator-engineer; plan+exec→subagent-driven-development; feature→feature-lifecycle (.feature/→feature-state.py status). Force Direct: OFF.
What ships with it
17 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.
- references/error-handling.md 503 B
- references/execution-architecture.md 444 B
- references/hooks-guide.md 1.5 KB
- references/lazy-completion-detector.md 2.2 KB
- references/model-selection.md 4.4 KB
- references/parallel-analysis.md 13 KB
- references/perspective-prompts.md 5.8 KB
- references/pipeline-guide.md 1.5 KB
- references/planning-guide.md 1.2 KB
- references/progressive-depth.md 5.5 KB
- references/quality-gates.md 536 B
- references/quality-loop.md 6.6 KB
- references/repo-architecture.md 4.5 KB
- references/routing-telemetry.md 4.3 KB
- references/semantic-first-ab-results.md 1.6 KB
- references/workflow-dispatch.md 5.8 KB
- references/worktree-rules.md 5.0 KB
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.
- 2d ago Changed · -47 lines c648a8802eac
- 4d ago First seen · 429 lines · 23 tokens per session scan A ceee83341567
do is a skill published in the GitHub repository notque/vexjoy-agent (419 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 6,891 once invoked, about $0.0001 per session on Opus 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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