LLM Mart Basic

Use when a Culture Index profile needs comparison with role requirements, team composition, and manager profile for hiring. Not for transcript prediction: use culture-interview-profile-prediction.

Use when asked to predict Culture Index traits from an interview transcript before a survey exists, including sparse or contradictory evidence. Not for interpreting completed survey results.

Use when a manager needs profile-specific communication, one-on-one, motivation, and energy guidance for a direct report. Not for general pair or team compatibility analysis.

Use when a signed new hire's Culture Index profile and team profiles need a first-90-days plan. Not for manager coaching: use culture-manager-coaching.

Use when implementing pool/slab/arena allocators, tuning jemalloc/mimalloc/tcmalloc, writing a Rust GlobalAlloc, or benchmarking allocator performance and fragmentation.

Use when customer feedback, NPS, churn, email feedback, call transcripts, or voice-of-the-customer analysis needs a report over a time window.

Use when the user asks to cut, trigger, or start a release candidate for a release branch. Not for full releases, hotfixes, or non-release-candidate workflow dispatches.

Use when the caller supplies a falsifiable out-of-happy-path invariant and a finite budget. Restores via bounded patches or reverts. Not for normal feature delivery or universal retries.

Use when a build, QA pass, demo, user complaint, or abandoned attempt leaves the next pass needing lessons rather than code. Not for changelog extraction. Not for session handoff: use handoff.

Use when a human-curated dbt model index must guide BigQuery SQL for a warehouse question. Not for discovering undocumented models or executing warehouse changes.

Use when a user asks to tighten verbose-but-correct prose without a full rewrite. Cuts in place to load-bearing density, preserving every load-bearing claim.

Use when debugging RelWithDebInfo or -O2 release builds, using -Og for debuggable optimization, split-DWARF, GDB scheduler-locking, reading inlined frames, or understanding "value optimized out".

Use when the user has a fork and wants it resolved and applied, not explored: "decide this", "choose the path", or "decide and fix it".

Use when a user wants to record why one world won over the others, as a decision-diary entry. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a current decision needs pressure-testing until the rationale is clear to a skeptic. Not for tasks requiring source or remote-system changes.

Use when an imperative program needs pre-conditions, post-conditions, and loop invariants proved automatically by SMT in Dafny or Why3, short of a tactic prover.

Use when asked to deduplicate a skill tree, fold overlapping skills, or cut the skill count: analyze, gate per family, then fold. Not for prompt-doctrine cascades: use cascade-dedup.

Use when the user asks to research a topic and produce a thorough sourced report. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user wants the finished-system contract for a piece of work: behavior, protocols, allowed, forbidden, and impossible states with a state-space proof. Not for runtime verification.

Turns a song (audio file + lyrics) into a hand-painted watercolour music video (MP4) in the style of PDoomVideo, following its pipeline end to end: measure the beat grid and time the lyrics, scaffold the p5.brush engine, design the story, characters and sets from the lyrics, pain

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

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.

Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.

Execute the implementation plan by processing and executing all tasks defined in tasks.md

Execute the implementation planning workflow using the plan template to generate design artifacts.

Create or update the feature specification from a natural language feature description.

Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

Cancel active execution loop and cleanup state

Start task execution loop

Smart entry point for new features with auto ID and branch management

Show current feature status and progress

Switch active feature

Cancel active execution safely and optionally remove the spec

Generate technical design from requirements

Submit feedback or report an issue for Ralph Specum plugin.

Show help for Ralph Specum plugin commands and workflow.

Start task execution loop

Index codebase components and external resources into searchable specs

Create new spec and start research phase

Run or resume an optional prototype

Update spec files methodically after execution (requirements -> design -> tasks)

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runs anywhere. uses anything

Open-source dots for the web: an AI agent with its own browser, one that does not get blocked.

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Turn PDFs, books and papers into interactive learning webpages｜将复杂材料转化为可追溯、可测验、可做笔记的学习网页

PhysicsOS 是一个面向初高中物理学习的公益可视化智能体，通过 AI 理解题目并结合物理引擎，将抽象物理过程转化为可交互、可观察、可计算、可验证的真实物理场景。

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