LLM Mart Basic
@llm-mart · Joined Jun 2026
角色设计与对话创作专家。负责角色设定、语言风格档案、动机链、人物弧线、 对话质量、角色关系设计。被 story-long-write(Phase 2,4)和 story-short-write(Phase 2,3)调用。 也可审查角色一致性和对话质量。
事实一致性与伏笔状态检查专家(只读)。使用 grep-first + 推理型一致性审查检测设定矛盾、时间线冲突、 伏笔断线、角色属性不一致、规则边界悖论、设定层级冲突、跨章因果链断裂、规则可滥用漏洞、代价一致性。输出 S1-S4 分级冲突报告。 被 story-review、story-long-write(Phase 5)、story-short-write(Phase 4)调用。 不做任何创作判断。
叙事文本创作与去AI味专家。负责正文写作(三维度揉进、感知/反应)、 情绪弧线执行、开篇/收尾、去AI味(禁用词替换、句式去套路、节奏调整)。 被 story-long-write(Phase 4-5)和 story-short-write(Phase 3-4)调用。 也可执行完整去AI味流程和格式合规检查。
故事架构与世界观创作专家。负责题材选择、核心梗设计、世界观构建、大纲排布、 钩子/悬念/反转等叙事工程、情绪弧线设计、范围控制审查。 被 story-long-write(Phase 1-3)、story-short-write(Phase 1-2)调用。 也可审查已有内容的结构问题。
故事项目结构化查询 agent(只读)。响应关于角色状态、伏笔进度、设定出现位置、 时间线节点、写作进度的查询。使用 grep + read 从项目文件系统中检索信息, 返回结构化 JSON 摘要。 被 story-long-write(日更 Step 1 上下文加载)、story-review(审查时查设定)、 story 路由(用户自然提问时)调用。 不做任何创作判断或修改。
小说写作资料研究 agent。接收研究查询,优先使用 CDP (agent-browser) 搜索并提取完整正文, WebSearch/webReader 作为兜底。输出带来源引用的结构化 Markdown 参考文件。 被 story-long-write(Phase 4)、story-review、story skill 路由调用。
Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments,
Analyze a finished coder-eval run and write analysis.md — cluster failures into systemic patterns, diagnose prompts, criteria, config, environment and cost, and recommend concrete fixes. Use when the user wants to know why a run failed, what to fix, or what a run says about their
Generate and run a coder-eval activation suite for a Claude Code skill — does the agent actually engage it when it should, and leave it alone when it shouldn't? Use when the user asks whether a skill triggers, wants to test skill activation, or worries a skill has silently stoppe
Generate a GitHub Actions workflow that runs a coder-eval suite as a CI gate or on a schedule, using the published composite action — with the agent runtime, credentials, JUnit output and a score floor wired correctly.
Set up coder-eval in this repository — scan for what is worth evaluating (Claude Code skills, an MCP server, a CLI), then scaffold a task directory with one real, passing-or-failing task and the exact command to run it.
Review coder-eval task YAML that already exists — find criteria that cannot fail, prompts that give away the answer, fixtures with no cleanup, and near-duplicate tasks, each with a severity and a concrete fix. Read-only. Use when the user wants existing tasks reviewed, linted, au
Turn a natural-language description into one or more coder-eval task YAML files — minimal prompts, weighted success criteria that check output content, validated with `coder-eval plan`. Use when the user wants to write, add, or generate an evaluation task.
Run Google Antigravity (Gemini) as the agent under evaluation in Coder Eval — installation, authentication, model and skill configuration, and how its telemetry maps to sandboxed, weighted scoring.
Configure and run the default Claude Code agent in Coder Eval — the full agent-config surface, direct vs. Bedrock authentication, permission modes, sandbox isolation, skills/plugins, early stop, and token telemetry.
Run OpenAI Codex as the agent under evaluation in Coder Eval — installation, authentication, task configuration, and how Codex telemetry maps to sandboxed, weighted scoring.
Imported from uipath/coder_eval/docs/agents/HARNESS_PARITY.md.
Genera post LinkedIn cringe (italiano di default, ma funziona in qualunque lingua), calibrati su livello di cringe (1-10), registro (credibile / parodico / surreale deadpan alla Lynch) e moduli cringe scelti da un catalogo di 37, con la possibilità di partire da un fatto reale (u
Analizza i commenti di un post LinkedIn (tipicamente un post cringe generato con la skill linkedin-cringe) e produce un report markdown con le statistiche - quanti ci hanno creduto e quanti hanno colto lo scherzo, top ten per gradimento, toni, categorie di commentatori, cringe-me
Il Cringiometro. Dato l'URL (o il testo) di un post LinkedIn, ne misura il livello di cringe da 1 a 10 con la scala e il catalogo dei 37 moduli della skill linkedin-cringe, dice quali ganci ha preso, il registro, il sapore-AI e la lead-gen, e produce un report markdown più un'imm
/secular-trends
Secular trends
Secular technology trends analysis — technology adoption cycles, disruption risk, strategic positioning
/turnaround
Turnaround
Turnaround vs stagnation analysis — performance inflection detection, operational metrics, leadership impact
/valuation-methods
Valuation methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction
/competitive-positioning
Competitive positioning
Porter-style competitive positioning analysis — strategic group mapping, differentiation analysis
/peer-bench
Peer bench
Peer benchmarking — multi-ticker financial comparison, growth/value matrix, z-score ranking
/sector-overview
Sector overview
Sector overview — TAM estimation, competitive concentration (HHI), regulatory landscape
/supply-chain
Supply chain
Supply-chain map — supplier/customer dependency, geographic concentration, bottleneck identification
/currency-analysis
currency-analysis
Currency Analysis — macro strategy analysis
/macro-regime
macro-regime
Macro Regime — macro strategy analysis
/rate-cycle
rate-cycle
Rate Cycle — macro strategy analysis
/3-statement
3 statement
3-statement integrated financial model — IS/BS/CFS triangulation, 5 historical + 5 forecast years
/audit-xls
Audit xls
Audit an Excel workbook — formula errors, hardcoded cells, calculation arc cross-validation
/comps
Comps
Trading comps analysis — peer-group selection, trading-multiple triangulation, implied-valuation range
/dcf
Dcf
DCF valuation model — 5-10 year projection, WACC construction, sensitivity tables
/earnings-preview
Earnings preview
Earnings preview presentation — 4-6 slide deck with consensus estimates, historical surprises, forward catalysts
/lbo
Lbo
LBO model — sources & uses, debt schedule, exit-multiple analysis, sponsor IRR sensitivity
/pitch-deck
Pitch deck
Investment thesis pitch deck — 12-16 slide presentation with sourced footers
/sotp-valuation
Sotp valuation
Sum-of-the-parts valuation — segment-level multiples, conglomerate discount analysis
/xlsx-financials
Xlsx financials
XBRL-to-Excel — proper number formatting, frozen headers, named ranges, calculation arc cross-validation
/income-strategies
income-strategies
Income Strategies — options and derivatives analysis
Make any song you can imagine
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28 views 0 likesDeterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.
31 views 0 likesDeterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…
20 views 0 likesNative Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…
32 views 0 likesAgent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.
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16 views 0 likesProduction-grade MCP server for MikroTik RouterOS with secure AI-native network automation.
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