LLM Mart Basic
@llm-mart · Joined Jun 2026
Regulatory standard mapping expertise — auto-activates on ESG framework alignment and disclosure mapping tasks
Proposal writing, expectation management, change communication, and post-event reporting
Event concept development, logistics planning, vendor coordination, budget management, and timeline creation
Professional communication, email management, stakeholder coordination, follow-up systems
Executive operations, calendar management, travel coordination, expense workflows
Client advisory communication with fiduciary tone, compliance-friendly language, and plain-language financial education
Financial planning knowledge for retirement, education, estate, tax, and insurance needs analysis
Regulatory change monitor — current SEC, FINRA, IRS landscape with effective dates; flags advice impacted by recent changes
AI vendor billing-model fluency — auto-activates when normalizing, comparing, or forecasting AI spend across seats, usage, credits, and reserved capacity
AI unit-economics discipline — auto-activates when evaluating whether AI spend is worth it, pushing from tokens and requests to cost-per-successful-outcome
Weekly and monthly operating rhythm for solo founders — retros, three-bet weekly plans, honest monthly updates, and metric discipline
Scope and shipping guardrails for solo founders — cutting v1 to the one feature that tests the riskiest assumption, and a validation check before anything goes public
Worker-facing safety writing conventions — auto-activates when drafting toolbox talks, procedures, signage, alerts, or any safety material a crew has to read and act on
Safety-documentation guardrails — auto-activates on incident, inspection, recordkeeping, or regulatory work to keep drafts factual, uncertified, and never in front of a reporting obligation
Map clinical study data to FDA Substantial Equivalence and clinical performance criteria for AI-enabled medical devices
Determine the appropriate FDA premarket pathway for an AI-enabled medical device based on intended use and predicate landscape
HR operations, employment law, policy writing, compliance frameworks
Reskilling pathway mapper — given a displaced role, map AI-resistant adjacent roles and learning paths
Talent acquisition, job descriptions, DEI-inclusive language, onboarding
Insurance client relationship management, retention strategies, and cross-selling approaches
Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.
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.
/vet
Vet
Vet the staged change: run the implement-review review loop (short alias)
/learn
Learn
Extract a learning from the recent conversation and add it to the appropriate instruction file
/learn
Learn
Extract a learning from the recent conversation and add it to the appropriate instruction file
/create-pipeline
create-pipeline
Create a new pipeline from a task description. Fans out agent, skill, and hook scaffolding in parallel, then integrates into the routing system.
/d
D
Jev-first router: A/B variant of /do. One TypeSafe call replaces the manifest read; falls back to /do when unavailable or unconfident.
/do
Do
Smart router: classify requests and route to the correct agent + skill
/generate-claudemd
Generate claudemd
Generate project-specific CLAUDE.md from repo analysis.
/github-notifications
Github notifications
Triage GitHub notifications: fetch, classify, report actions needed.
/github-profile-rules
Github profile rules
`github-profile-rules` — extract programming rules and coding conventions from a GitHub user's public profile via API.
/gm-brilliant-implementation
Gm brilliant implementation
Run the complete 34-stage implementation workflow for a large, multi-system, multi-wave, or CPU-delegated 5 Star Booker GM program.
/install
Install
Verify installation, diagnose issues, and guide first-time setup
/pr-review
Pr review
Comprehensive PR review using specialized agents, with automatic retro knowledge capture
/reddit-moderate
Reddit moderate
Reddit moderation: fetch modqueue, classify content, take mod actions
/retro
Retro
Learning system interface: stats, search, graduate learnings. Backed by learning.db (SQLite + FTS5).
/system-upgrade
system-upgrade
Systematic upgrade pipeline for adapting agents, skills, and hooks when Claude Code ships updates, user goals change, or retro learnings accumulate.
/full-equity-research
Full equity research
agentii.full-equity-research — the spec 046 kit command. Use the Skill tool to run agentii:full-equity-research on this workspace.
/synthesize
Synthesize
agentii.synthesize — the spec 046 kit command. Use the Skill tool to run agentii:synthesize on this workspace.
/agent-audit
Agent audit
Audit agents spawned in the current/last run against the agent-selection taxonomy
/agent-diversity-review
Agent diversity review
Run the Agent Diversity Review gate and emit the result table
/audit-cron-arc
audit-cron-arc
Meta-analyze the effectiveness of a /loop arc — per-iteration metrics, LOC delta trend, saturation detection, and a keep/lengthen/delete recommendation.
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