LLM Mart Basic
@llm-mart · Joined Jun 2026
Use this skill when you need evidence-bounded memory write/read/update/delete, retention, contamination, isolation, provenance, and forgetting behavior; triggers include Agent 记忆 and Agent memory.
Use this skill when you need evidence-bounded Agent identity, tool/resource scope, approval, denial, escalation, and side-effect boundaries; triggers include Agent 权限 and Agent permission.
Use this skill when you need to test AI agent tool-call contracts, authorization, failures, and side-effect boundaries; triggers include agent tool testing.
Use this skill when you need to test AI agent goals, state, planning, recovery, and safety boundaries; triggers include ai agent testing.
Use this skill when you need AI-assisted testing workflows such as test data generation, root-cause analysis, and prioritization; triggers include AI-assisted testing and AI for QA.
Use this skill when you need to test an AI-enabled product feature for behavior, safety, and user-impact boundaries; triggers include AI feature testing.
Use this skill when reviewing AI-generated unit, functional, API, or end-to-end tests for false confidence, weak assertions, missing risks, or unsafe test behavior; triggers include AI-generated test review and functional test review.
Use this skill when you need evidence-bounded safety policy, abuse categories, refusal/redirect behavior, privacy, escalation, and Human risk decisions; triggers include AI 安全 and AI safety.
Use this skill when you need to verify API contract compatibility, consumer expectations, and schema change risk; triggers include API contract testing.
Use this skill when an API, OpenAPI, or consumer contract needs a quality review before implementation or versioning; triggers include API design review, contract readiness review, and consumer compatibility audit.
Use this skill when you need to review API error shape, status, code, and disclosure behavior against sourced contracts; triggers include API 错误契约测试 and API error contract testing.
Use this skill when you need to assess API retry and duplicate-request behavior against sourced side-effect evidence; triggers include API 幂等性测试 and API idempotency testing.
Use this skill when you need to design evidence-bounded API failure and rejection scenarios; triggers include API 负向测试 and API negative testing.
Use this skill when you need to design API pagination scenarios from ordered data and cursor or offset evidence; triggers include API 分页测试 and API pagination testing.
Use this skill when you need to design evidence-bounded API quota, burst, and recovery scenarios; triggers include API 限流测试 and API rate limit testing.
Use this skill when you need to compare API schemas with sourced request and response evidence; triggers include API Schema 校验 and API schema validation.
Use this skill when you need evidence-bounded api-security-testing analysis and validation preparation; triggers include API 安全测试 and api-security-testing.
Use this skill when you need to parse multi-format API definitions and generate Bruno collections for executable regression; triggers include Bruno collections and Bruno API testing.
Use this skill when you need to design Postman collections, environments, scripts, and Newman-ready API regression plans; triggers include Postman API testing, API testing, and api-test-postman.
Use this skill when you need to parse multi-format API definitions and generate Pytest API automation; triggers include Pytest API tests and API automation with Pytest.
A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.
None of the safety came from the model. It came from six boring habits.
Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.
Six hours in, one step left, everything green, and the incident that didn't happen
CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.
Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it
An API request routing a model's tool call through an approval gate to a remote MCP server
31 config keys, two audits, and why the first one was wrong in both directions
The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.
Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.
Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.
"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks
stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.
The most important rule wasn't about what I could change. It was about what I was allowed to display.
Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.
/init
Init
First-time Archcore setup — wire host configs, measure the authored context, compose the full first-day seed in one preview, and create it on one confirm; import converts CLAUDE.md, AGENTS.md, rule files, ADRs, and docs into native documents; refresh adds new facts or drills into one domain.
/plan
Plan
Plan a feature or initiative through a computed route — a small fix exits with no documents, one capability gets a spec and a plan, a large initiative gets an umbrella PRD with one spec per capability; start with sdd, sources (market research), iso (regulated work), or research (technical investigation) to run that path directly.
/review
Review
Review branch changes against Archcore docs, or report project health; drift runs staleness detection, deep a full documentation audit, closeout closes a finished feature, experience captures a repeated pattern.
/cite-check
cite-check
Verify that citations actually exist and that the claims they support are faithful to the cited source. Runs deterministic existence checks (Crossref / OpenAlex / Semantic Scholar / arXiv) plus a claim-faithfulness pass via the alterlab-citation-verifier skill.
/lit-review
lit-review
Run a systematic, reproducible literature review on a topic and return an APA 7.0 annotated bibliography with a documented search strategy. Invokes the alterlab-deep-research pipeline in lit-review mode.
/review-paper
review-paper
Run a full multi-perspective peer review of a manuscript, simulating an Editor-in-Chief plus three peer reviewers and a Devil's Advocate, and produce a structured editorial decision and revision roadmap. Invokes the alterlab-paper-reviewer skill.
/research-pipeline
research-pipeline
Orchestrate the end-to-end academic research-to-publication workflow (research, write, integrity check, review, revise, re-review, finalize) with mandatory integrity gates and two-stage peer review. Invokes the alterlab-research-pipeline orchestrator.
/audit-infra
Audit infra
Infrastructure-first security audit — secrets, supply chain, CI/CD, LLM/skill security, OWASP, STRIDE. Complements /audit-solana (program-level)
/audit-solana
Audit solana
Security audit for Solana programs (Anchor/native)
/benchmark
Benchmark
Benchmark CU usage and compare against baseline for regression detection
/build-app
Build app
Build web client application (Next.js, React, Vite)
/build-program
Build program
Build Solana program (Anchor or native)
/build-unity
Build unity
Build Unity project (WebGL, Desktop, or PSG1)
/cleanup
Cleanup
Initialize forked template — setup CLAUDE.md and remove config repo scaffolding
/commit-claude-config
Commit claude config
Version the Solana AI Kit config in git (un-ignores .claude/, CLAUDE.md, .mcp.json, .gitmodules and commits them)
/debug-user-tx
Debug user tx
Reproduce and debug a user-reported failing transaction against forked cluster state, mapping the failure back to source code
/deploy
Deploy
Deploy Solana program (devnet first, then mainnet)
/diff-review
Diff review
AI-powered diff review for Solana-specific issues and code quality
/doctor
Doctor
Health check for the dev environment and solana-ai-kit config — read-only, with one exact fix-it command per failure
/dream
Dream
Memory consolidation — dedupe, contradiction-check, prune, and re-rank MEMORY.md + CLAUDE.md Project Learnings. Run after major refactors
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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