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.
Borrowing it
Nothing to install: this file belongs to modu-ai/moai-adk. 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/modu-ai/moai-adk/main/.claude/skills/moai-ref-supply-chain/SKILL.mdgit 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-ref-supply-chain)<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-ref-supply-chain"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-ref-supply-chain/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-ref-supply-chain"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-ref-supply-chain.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 Prompt Injection · line 300 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00167 | $0.04378 |
| Opus 5 | $0.00084 | $0.02189 |
| Sonnet 5 | $0.00033 | $0.00876 |
| Haiku 4.5 | $0.00017 | $0.00438 |
Grade A, and why
moai-ref-supply-chain 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 — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Software Supply-Chain Defensive Security Reference
Defensive practitioner reference for hardening a software supply chain — the chain
from source, through build, to the artifact a consumer installs. Every section is
framed as defense, hardening, detection, or verification: it describes the weakness,
how to detect it, and how to prevent it, never how to exploit it. AI/LLM-specific
supply-chain concerns (model and training-data provenance) live in
moai-ref-llm-security; web-application vulnerabilities live in
moai-ref-owasp-checklist.
Target Use
Apply when building, releasing, or consuming software components. The threat model is an untrusted supply chain: any dependency you pull, any build step you run, and any artifact you ship may have been substituted, tampered with, or impersonated. The defenses below establish provenance (where did this come from?), integrity (has it been altered?), and hygiene (is this the component I meant to use?).
The Supply-Chain Trust Boundaries
The core defensive insight: each hand-off in the chain is a boundary where a component can be substituted or tampered. Establish provenance and verify integrity at every hand-off.
| Boundary | Substitution / tamper risk | Primary defense |
|---|---|---|
| Source resolution (name → package) | Dependency confusion, typosquatting | Namespace scoping, install-time verification, name allowlist |
| Dependency download | Compromised registry, MITM | Lockfile pinning by hash, signature verification |
| Transitive closure | Vulnerable or malicious deep dependency | Transitive audit, depth limits, SBOM diff |
| Build | Tampered build, injected step | SLSA provenance, isolated/ephemeral builders |
| Artifact publish | Substituted artifact | Sigstore signing, provenance attestation |
| Consumer install | Unverified artifact accepted | Signature + provenance verification at install/admission |
SBOM — Generation and Verification
A Software Bill of Materials (SBOM) is the inventory of components in an artifact. It is the foundation for every downstream defense: you cannot audit, scan, or verify what you have not inventoried.
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 · 346 lines · 167 tokens per session scan A fd443095ecc0
moai-ref-supply-chain is a skill published in the GitHub repository modu-ai/moai-adk (1,206 stars, last pushed today), licensed Apache-2.0. It adds 167 tokens to every session and 4,378 once invoked, about $0.0008 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 2026-08-30.
Other skills, from other repositories
code-reviewer
Automatic code quality and best practices analysis. Use proactively when files are modified, saved, or committed. Analyzes code style, patterns, potential bugs, and security basics. Triggers on file changes, git diff, code edits, quality mentions.
test-generator
Automatically suggest tests for new functions and components. Use when new code is written, functions added, or user mentions testing. Creates test scaffolding with Jest, Vitest, Pytest patterns. Triggers on new functions, components, test requests, testing mentions.
api-documenter
Auto-generate API documentation from code and comments. Use when API endpoints change, or user mentions API docs. Creates OpenAPI/Swagger specs from code. Triggers on API file changes, documentation requests, endpoint additions.
readme-updater
Keep README files current with project changes. Use when project structure changes, features added, or setup instructions modified. Suggests README updates based on code changes. Triggers on significant project changes, new features, dependency changes.
security-auditor
Continuous security vulnerability scanning for OWASP Top 10, common vulnerabilities, and insecure patterns. Use when reviewing code, before deployments, or on file changes. Scans for SQL injection, XSS, secrets exposure, auth issues. Triggers on file changes, security mentions, deployment prep.
version-check
Recommend which Claude Code version to run, or whether to update. Use when asked which Claude Code version is best/safe, whether to update now, whether a recent release is buggy, or what changed since the installed version.