MoAI-ADK is a Go-based harness that organizes and verifies Claude Code work across planning, implementation, synchronization, and review stages. Developers use it to structure agentic coding tasks, apply quality gates, and route work across language models, while the catalogue entries extend its workflow with skills, hooks, commands, MCP servers, instructions, and settings.
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 modu-ai/moai-adk --skill moai-jev-skill-suggestiongit clone --depth 1 https://github.com/modu-ai/moai-adkWrote 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/modu-ai/moai-adk/moai-jev-skill-suggestion)<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-jev-skill-suggestion"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-jev-skill-suggestion/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/modu-ai/moai-adk/moai-jev-skill-suggestion"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-jev-skill-suggestion.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 | $0.00083 | $0.01159 |
| Opus 5.5 | $0.00033 | $0.00464 |
| Sonnet 5.5 | $0.00017 | $0.00232 |
| Haiku 4.5 | $0.00008 | $0.00116 |
Grade A, and why
moai-jev-skill-suggestion 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 11d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Suggestion, Display-Only
The gate comes first
The capability is gated behind workflow.jev.enabled, and the shipped
default is off. Confirm the gate before anything else. With the gate off, a
caller constructs no request, makes no network call, and proceeds with no
signal: an absent signal is NO SIGNAL — the same as "we did not ask" — never
a negative answer, and never a reason to change course.
Three readiness surfaces exist for the on state: the gate key itself, the
credentials file referenced by convention (~/.moai/.env.typesafe, read by
the credential loader — never inlined, never copied into settings), and the
doctor readiness check (a TCP-only probe that reports gate, credential, and
reachability in three lines without sending any judgment request). A caller
that cannot confirm all three proceeds as if the gate were off.
The reachable surface itself lives in the product catalogue, not here. This skill owns mechanism and contract only; the catalogue owns where the call lives.
What a suggestion is
A suggestion is a signal a person reads: a labelled model answer that names a skill that might be worth loading and carries a confidence, and it does nothing else. Four things it is not:
- Not a selection. The existing selector keeps its selection authority; a suggestion never flips it.
- Not an auto-load. No skill content is loaded, attached, or injected because a suggestion was emitted.
- Not a judgment. Not a completion decision, not a merge approval, not a queue mutation — anything hard to undo stays with people.
- Not a code-path input. Nothing branches on the answer; a human reads it, and the reading is the only consumer.
The loading mechanisms rejected here
Two mechanisms were evaluated and rejected; recording them keeps them from returning quietly:
- Hiding the listing and injecting a chosen SKILL.md body directly. This breaks the display-only chain: a skill that never appeared in context has no reading human, and a direct content injection is precisely the auto-load the contract forbids.
- An ambient function-hook wiring that evaluates a suggestion on every prompt. It commercializes a model call per prompt on an unmeasured behavior, fragments the template distribution, and rides an early-experiment surface that can shift without notice. Not adopted. Revisit only with measured evidence that a constant-answer baseline has been beaten — the measurement bar belongs to a separate track, not to this skill.
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.
- 11d ago First seen · 116 lines · 83 tokens per session scan A 7dae53196c2b
moai-jev-skill-suggestion is a skill published in the GitHub repository modu-ai/moai-adk (1,234 stars, last pushed 2d ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,159 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 2026-10-06.
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