LLM Mart Basic
@llm-mart · Joined Jun 2026
When GitHub Actions fails, fetch failing job logs and assign each failing job to a separate subagent that fixes its slice of the problem in parallel. Use for multi-job CI failures where jobs are independent.
Run four parallel read-only subagents that each review the same diff from a different lens — security, performance, correctness, and readability — then merge findings into one report. Use before merging large or risky PRs.
Explore a large codebase in parallel by launching multiple explore subagents that each investigate a different area simultaneously. Use when onboarding onto a new project, understanding architecture, or investigating a cross-cutting concern.
When multiple tests fail, assign each failing test file to a separate subagent that fixes it independently in parallel.
Profile a running web application's CPU performance using Cursor's built-in browser profiler. Captures call stacks, identifies slow functions, and suggests optimizations. Use when a page feels slow or janky.
Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.
Test-driven development in Python using uv as the package manager. Covers the red-green-refactor cycle, vertical slicing, and uv project setup.
Build mobile apps with React Native and Expo — navigation, platform-specific code, performance, and native modules.
Execute a user flow step-by-step in Cursor's built-in browser while documenting each action, then emit a Playwright test that replays the same flow using stable selectors derived from the accessibility tree.
Open the app in Cursor's browser at multiple viewport sizes, screenshot each, and report any layout breakage.
Perform a thorough code review focused on correctness, maintainability, performance, and best practices.
Automatically persist useful context — research, decisions, learnings, templates — to workspace files so knowledge survives across conversations.
Generate a visual changelog or PR description by taking before/after screenshots of UI changes using Cursor's built-in browser. Use when preparing a PR with visual changes.
Audit technical SEO — meta tags, structured data, Open Graph, sitemaps, robots.txt, performance, and accessibility signals.
Set up a GitHub Actions CI/CD pipeline with linting, testing, type-checking, and deployment steps.
Set up Terraform infrastructure-as-code for cloud resources, including provider configuration, modules, state management, and CI integration.
When the user keeps asking for the same check to run (lint, tests, type-check), suggest a Cursor hook to automate it.
When the user repeats the same correction or convention multiple times, suggest a Cursor rule to encode it permanently.
When the user struggles with a task that a known skill could handle, suggest installing it.
Switch the current Cursor workspace to a different project directory using the cursor-app-control MCP. Use when the user asks to switch projects, open another repo, jump to a different codebase, or move to a worktree.
A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.
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.
/log-provenance
log-provenance
Log derived files and trace provenance back to ENCODE source data
/manage-credentials
manage-credentials
Store, check, or clear ENCODE API credentials for restricted data
/quality-check
quality-check
Assess ENCODE experiment quality using audit counts and replicate counts
/search-encode
search-encode
Search ENCODE experiments by assay, organ, biosample, or target
/track-experiments
track-experiments
Track ENCODE experiments locally with publications and provenance
/browse-files
browse-files
List, search, and inspect ENCODE files by format, type, and assembly
/cite-encode
cite-encode
Generate ENCODE citations for publications, grants, and presentations
/compare-experiments
compare-experiments
Check if two ENCODE experiments are compatible for combined analysis
/cross-reference
cross-reference
Cross-reference ENCODE data with PubMed, GEO, ClinicalTrials, and bioRxiv
/download-encode
download-encode
Download ENCODE files (BED, FASTQ, BAM, bigWig) with MD5 verification
/log-provenance
log-provenance
Log derived files and trace provenance back to ENCODE source data
/manage-credentials
manage-credentials
Store, check, or clear ENCODE API credentials for restricted data
/quality-check
quality-check
Assess ENCODE experiment quality using audit counts and replicate counts
/search-encode
search-encode
Search ENCODE experiments by assay, organ, biosample, or target
/track-experiments
track-experiments
Track ENCODE experiments locally with publications and provenance
/document
Document
Record the present state by mode — decision (ADR, RFC, rule), code (spec, doc, guide, scenario), or research (a ready report or one external material); a gate picks the document type.
/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.
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