LLM Mart Basic
@llm-mart · Joined Jun 2026
Show reading and writing progress dashboard — how many sources read, chapters completed, word counts, and coverage gaps.
Use when preparing, auditing, releasing, PDF-hardening, or rebuttal-hardening academic manuscripts, datasets, artifacts, reviewer packets, or claim registers involving multiple refs, local assets, human labels, agent-assisted drafts, wide tables, figure provenance, submission PDF
Stop repeated failed writing, coding, manuscript, rebuttal, or restructuring revisions when the same issue has gone through 3+ unsatisfactory edits, vague feedback such as still wrong/weird/unclear/weak/越改越乱, version contamination, or possible gap/claim/evidence/venue-fit drift.
Review the user's own manuscript, paper, thesis chapter, rebuttal, or release packet with clean-room anti-contamination controls and, when needed, an unfamiliar-reader comprehension gate. Use for internal review, readiness checks, reviewer simulation, or claim-evidence self-audit
Check thesis text for British English consistency and safe mechanical spelling fixes.
Use when AI-assisted thesis or manuscript edits risk claim drift, scope creep, loss of intended use, experiment-role promotion, or repeated revisions that fail to converge; provides author-intent control, lightweight or strict contracts, drift audits, revision escalation, and hum
Fact-check claims encountered during reading — dates, names, events, citations. Use when encountering historical facts or disputed claims.
Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consoli
Use when the user wants Codex to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, wants Codex to review past sessions, learn preferences, consolidate memory/skills, run dry-run/run/adopt/status for SkillOpt-Sleep, or schedule background self-op
Use when the user wants Cursor to learn from recent local sessions, asks for an offline sleep or dream cycle, wants to consolidate recurring work into a Cursor skill, or requests SkillOpt-Sleep status, harvest, dry-run, run, scheduling, review, or adoption. Drives the validation-
Use when the user wants the dsh agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, skill/memory consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate
Multi-agent communication for AI coding tools. Agents message, watch, and spawn each other across terminals. Use when setting up hcom, troubleshooting delivery, or writing multi-agent scripts.
Use when contributing to Tel-Agent - picking an issue to work on, setting up the repository for the first time, starting or finishing a task, opening a pull request, or asking "what can I work on" / "how do I start" / "is my change ready to submit". Covers the full path from a fr
Imported from dpro-at/tel-agent/docs/brand/agents/README.md.
Build and operation support for n8n-cli. Guides remote status checks, imports, dry-runs, apply, and linter execution. Auto-build recommended for new sessions.
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task sha
Delegates work to a subagent or spawned session by writing only the task, while a PreToolUse hook pages the parent transcript through Jev and appends the chunks that task needs. Use before writing any Agent tool prompt or create_session prompt, and when asked to delegate this, ha
Distill Opus-level reasoning into optimized instructions for Haiku 4.5 (and Sonnet). Generates explicit, procedural prompts with n-shot examples that maximize smaller model performance on a given task. Use when user says "down-skill", "distill for Haiku", "optimize for Haiku", "m
Build and audit deterministic verification gates — a check that blocks a pipeline and can be shown to go red. Use when writing a calibration gate, CI check, validation script or pre-publication check for a numeric or empirical result; when a plausible-but-wrong value would surviv
Break out of a locked problem frame by picking one disciplined move — reframe, provocation (Po), random stimulus, SCAMPER, inversion, structured analogy, constraint play, or family traversal — and committing to it before evaluating. Use when stuck, when options feel narrow or obv
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.
/story-setup
Story setup
为当前项目部署或检查 ZCode 网文 Skills、Commands、Hooks 与 AGENTS.md。
/story-short-analyze
Story short analyze
短篇网文拆文,分析故事核、情绪线、结构和反转。
/story-short-scan
Story short scan
短篇网文扫榜,分析盐言、七猫、黑岩、点众等平台趋势。
/story-short-write
Story short write
短篇网文写作,从目标情绪、反转和小节大纲到正文。
/story
Story
网文工具箱路由入口。根据模糊意图分发到合适的网文 Skill。
/coder-eval-code-review-full
Coder eval code review full
Review the codebase across critical quality axes
/coder-eval-code-review-wf
Coder eval code review wf
Workflow-based 8-axis codebase review — per-axis sub-workflows, adversarial verify, deterministic scoring + rendering
/coder-eval-code-review
Coder eval code review
Run a multi-model code review on uncommitted changes or a described set of files
/coder-eval-create-plan
Coder eval create plan
Create a structured, phased implementation plan for a feature or change in the coder_eval codebase, executable from a fresh session by /coder-eval-implement-plan
/coder-eval-implement-plan
Coder eval implement plan
Implement an approved coder_eval plan phase by phase with risk-scaled per-phase review, then a final code review
/coder-eval-review
Coder eval review
Generate per-task review.json (summary + tags) for a completed run
/evolve
Evolve
Evolve skill files by integrating validated lessons from real usage
/init
Init
Initialize .autocontext/ in the current project for knowledge persistence
/review
Review
Interactively review and curate accumulated project lessons
/setup
Setup
First-run configuration for the autocontext plugin
/status
Status
Show knowledge stats for the current project
/event
Event
Create event materials (flyers, posters, signage) with your organization's branding
/newsletter
Newsletter
Create an HTML email newsletter with your organization's branding
/onepager
Onepager
Create a single-page fact sheet or program overview
/preview
Preview
Launch interactive preview for document editing
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