LLM Mart Basic
@llm-mart · Joined Jun 2026
Configure, authenticate, repair, and verify the Codex to Claude Code Bridge; safe stages run by default and sensitive stages require separate approval.
Safely manage Codex Rig role-agent shims: doctor, status, install, or remove; one action only.
Audit Codex configuration, workflow, and prompt-efficiency (instruction cost, value-per-token) drift; emit ranked gaps and measurable gates.
Calibrate skills/role cards for leaks/gaps with recall, precision, and confidence-accuracy checks.
Apply selected review fixes; bare PR targets use current online items, while PR +review adds the latest matching artifact.
Close PRs at an evidence gate or review local diffs/PRs with specialists and JSON artifacts.
Implement changes with a linear plan-build-verify workflow and measurable quality gates.
Investigate code debugging and root-cause narrowing; use measurable gates before fixes.
Build/extend grounded Kaggle Jupytext notebooks for training, EDA, inference, or resume workflows, grounding schema and submission format through the authenticated kaggle CLI.
Optimize a measurable metric with bounded iterations, guardrails, and regression gates.
Assess SemVer release readiness with gates/artifacts; never tag, publish, upload, or force-push.
Research docs, papers, or state of the art; provide source-backed recommendations and caveats.
Dry-run active plugin cache drift; refresh/reinstall only with approval; keep shims separate.
Adversarial review — drills to bedrock, treats claims as unproven until evidence. NOT for: plan design (foundry:solution-architect), test coverage (foundry:qa-specialist), config formatting (foundry:curator). TRIGGER: "challenge this", "devil's advocate", "poke holes in". SKIP: w
Content specialist — blog posts, slide decks, social threads, talk abstracts. Reads approved outline, applies four-beat arc. NOT for in-code docs/README/FAQs (foundry:doc-scribe), release notes (oss:release). TRIGGER: "write a blog post", "create slides", "draft a thread". SKIP:
Config quality reviewer. Scope: agents/skills/rules (*.md) — verbosity, duplication, cross-refs, roster overlap; applies fixes. NOT for hooks (foundry:sw-engineer), ADRs (foundry:solution-architect), adversarial challenge (foundry:challenger). TRIGGER: "audit this agent", "review
Docs specialist — docstrings, API refs, README, standalone FAQ/comparison tables. NOT for CHANGELOG (oss:shepherd), linting (foundry:linting-expert), implementation (foundry:sw-engineer), narrative content (foundry:creator). TRIGGER: "write docs for", "add docstrings to", "update
Python static analysis — ruff, mypy, pre-commit, lint/type fixes, type annotations. NOT for CI topology (oss:cicd-steward), test logic (foundry:qa-specialist), non-style implementation (foundry:sw-engineer), docstrings (foundry:doc-scribe). TRIGGER: "is this clean", "lint issues"
Perf engineer — CPU/GPU/memory/I/O bottlenecks, DataLoader throughput, PyTorch tuning. Profile-first, measures before changing. NOT for refactoring (foundry:sw-engineer), architecture (foundry:solution-architect), DataLoader correctness (research:data-steward). TRIGGER: "why is t
QA specialist writing/fixing tests. Black-box tester: public API surface, expectations from docs not implementation. NOT for linting (foundry:linting-expert), implementation (foundry:sw-engineer), test perf (foundry:perf-optimizer), non-Python frameworks. TRIGGER: "write tests fo
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.
/deps-audit
Deps audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
/refactor-clean
Refactor clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
/tech-debt
Tech debt
Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.
/full-review
Full review
Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices
/pr-enhance
Pr enhance
Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report
/implement
Implement
Execute tasks from a track's implementation plan following TDD workflow
/manage
Manage
Manage track lifecycle: archive, restore, delete, rename, and cleanup
/new-track
New track
Create a new track with specification and phased implementation plan
/revert
Revert
Git-aware undo by logical work unit (track, phase, or task)
/setup
Setup
Initialize project with Conductor artifacts (product definition, tech stack, workflow, style guides)
/status
Status
Display project status, active tracks, and next actions
/context-restore
Context restore
Restore saved project context and decisions to resume a session
/context-save
Context save
Save project context, decisions, and progress for a later session
/data-driven-feature
Data driven feature
Build features guided by data insights, A/B testing, and continuous measurement
/data-pipeline
Data pipeline
Design and implement batch and streaming data pipelines with ingestion, orchestration, dbt transformations, data quality checks, and monitoring
/cost-optimize
Cost optimize
Reduce cloud costs across AWS, Azure, and GCP through rightsizing, reserved and spot capacity, storage tuning, and cost monitoring
/migration-observability
Migration observability
Migration monitoring, CDC, and observability infrastructure
/sql-migrations
Sql migrations
SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server
/smart-debug
Smart debug
AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.
/deps-audit
Deps audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
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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