LLM Mart Basic
@llm-mart · Joined Jun 2026
Phase-1 (S9, the FINAL comic-author step) — assemble the LOCKED storyboard + locked per-panel blueprints into ONE schema-valid `comic.json` (the comic-ir/1.0 contract boundary handed to comic-director / run_comic.py). Project the page_order + each panel's condition{} + render fie
Phase-1 Layer-2 of a comic — turn a LOCKED skeleton (intent_spec + the pre-locked beat/shot list) into an approved outline_spec: a 6-column beat table that binds every ARIS capability to a STORY COST, plus the continuity-motif contract the storyboard layer will be held to. NOT fr
Phase-1 step of comic-author — the DETERMINISTIC compiler (搬运工原則) that turns ONE gate-approved panel_spec + its status:locked blueprint into the EXACT fixed-section bake string for the spiral engine via the shipped scripts/build_prompt.py (+ the canonical scripts/_validate.py vet
Phase-1 comic-author step — turn a LOCKED, user-approved outline into the page-first storyboard that IS the authoring source of truth: a fixed page order BEFORE any prose, the MOTIF STATE TABLE (the master per-panel continuity ledger), a fixed 9-field per-panel spec, and one dedu
Phase-1 (S2) of the comic-author suite — compile the project's ART_BIBLE.md into an EXECUTABLE convergence target, not aesthetic prose. The bible is the ONE visual dialect read verbatim into every bake prompt AND into every visual reviewer's rubric (`style_consistency`/`identity_
Generate a publication-grade method / architecture / pipeline / workflow figure (a paper or README 'Figure 1') as an AUDITABLE object, not a one-shot prompt. A deterministic JSON blueprint LOCKS the content; an image model (gpt-image-2, baked by the agent via mcp__codex__codex —
End-to-end Pipeline A in ONE slash-command — turn a fuzzy story idea into a cross-model-audited, image-based movie + a clickable viewer. Chains comic-author (Phase 1 — intent→style→outline draft→provisional storyboard→assets→final locks→blueprints→prompts→comic.json) → the zero-c
Analyze architecture for consistency between ADRs and AD, completeness, and quality issues. Use when validating generated or refined architecture artifacts, before feature development, during architecture review, or periodically to detect drift.
Generate a full Architecture Description (AD.md) from accepted ADRs using multi-agent DAG orchestration. Use when accepted ADRs exist and you need to produce or update unified architecture documentation.
Review, accept, reject, or defer Change Decision Records (ChDRs) discovered by change-init. Interactive one-ChDR-at-a-time workflow that validates inferred decisions against their git/issue evidence before promotion to project memory.
Mine git history for Change Decision Records (ChDRs) by detecting commit messages that link to issue trackers, clustering the commits into change stories, and inferring the decisions behind them. Use when bootstrapping project memory from an existing repo's history (brownfield),
Promote accepted Change Decision Records (ChDRs) from drafts to project memory at .adlc/memory/chdr/, write OKF-style frontmatter, and regenerate the boot-facing .adlc/memory/chdr.md index that team-boot injects at session start. Use after /change-clarify has accepted ChDRs.
Analyze evaluation results and close the loop. Specification failures create local CDRs to fix agent rules; generalization failures go to evaluator backlog.
Refine, cluster, and accept draft criteria into the published goldset. Isolates 20% holdout split and publishes goldset.md + goldset.json.
Generate executable graders and configs from goldset. Generates Python graders / metrics and auto-runs unit tests to verify grader correctness.
Initialize evals/{system}/ directory structure for evaluation system following EDD principles (Standalone). Choose PromptFoo or DeepEval based on tech stack, generate security baseline.
Extract eval criteria from product specs and production failure traces (bottom-up error analysis). Writes proposed criteria to .adlc/drafts/evals/.
Run evaluations and validate evaluator quality (SLA compliance, TPR/TNR, statistical accuracy). Executes PromptFoo or pytest DeepEval.
Read-only analysis of PDR↔PRD consistency, PDR quality, cross-PDR conflicts, and staleness. Outputs a structured markdown report with severity-assigned findings. Use after /product-implement or periodically to detect drift.
Refine and validate Product Decision Records through targeted clarification questions. Review PDR completeness, detect conflicts, approve decisions, and update status to Accepted. Use before /product-implement.
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.
/explain-issue-fix
Explain issue fix
Explain how tasks in an issue were implemented with detailed breakdown
/find
Find
Search and locate tasks across all orchestrations using various criteria.
/five
Five
Apply the Five Whys root cause analysis technique to systematically investigate issues
/fix-github-issue
Fix github issue
Analyze and fix a GitHub issue with comprehensive testing and verification
/fix-issue
Fix issue
Fix a specific issue or problem with the given identifier or description
/fix-pr
Fix pr
Fetch unresolved comments for current branch's PR and fix them
/future-scenario-generator
Future scenario generator
Generate and analyze future scenarios with plausibility scoring, trend integration, and uncertainty quantification.
/generate-api-documentation
Generate api documentation
Auto-generate API reference documentation
/generate-linear-worklog
Generate linear worklog
You are tasked with generating a technical work log comment for a Linear issue based on recent git commits.
/generate-test-cases
Generate test cases
Generate comprehensive test cases automatically
/generate-tests
Generate tests
Generate comprehensive test suite for $ARGUMENTS following project testing conventions and best practices.
/git-status
Git status
Show detailed git repository status
/hotfix-deploy
Hotfix deploy
Deploy critical hotfixes quickly
/husky
Husky
Verify repository is in working state by running CI checks and fixing issues
/implement-caching-strategy
Implement caching strategy
Design and implement caching solutions
/implement-graphql-api
Implement graphql api
Implement GraphQL API endpoints
/init-project
Init project
Initialize new project with essential structure
/initref
Initref
Build reference documentation by creating markdown files and updating CLAUDE.md
/issue-to-linear-task
Issue to linear task
Convert GitHub issues to Linear tasks
/issue-triage
Issue triage
Triage and prioritize issues effectively
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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