OpxSkills

Perplexity agent skills

Perplexity search and research workflows. These are SKILL.md agent skills associated with Perplexity — reusable workflows, not prompt snippets. Compatibility may be inferred from the skill text unless a hands-on test is recorded.

All modelsInstall guideRanked Claude skillsSkill directory

coding

Agent Device Evidence

Records iOS/Android native MP4 evidence for test/repro flows extracted from an Expensify GitHub PR or issue. Use when the user asks to "record the flow for PR #X", "capture mobile evidence for issue #Y", or "produce screenshots/videos for <PR or issue URL>". Mobile-native only - declines mWeb and Desktop.

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productivity

Analog Flow Orchestrate

Canonical reference doc for the Analog A1-A9 track — spec extraction → topology → netlist → corner sweep → layout → per-block PV (DRC/LVS) → post-layout resim → hardmacro → HIL. Used when an agent wants to walk the A1-A9 plan step-by-step. For unified one-command execution (deterministic runner + AI fall-through + final audit aggregated), prefer the slash command /vibe-ic-analog (v1.6.13+) which merges this skill's workflow with analogoneshotrunner.py.

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coding

Antigaslighter

Verify a named artifact, workflow, deployment, or script against observable evidence. Read-only by default, bounded to the requested target, and explicit when evidence is incomplete.

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coding

Build And Dependency

Dev environment setup for NeMo AutoModel — container-based development, uv package management, installation options, environment variables, and common build pitfalls.

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coding

Builder Smoke Test

Smoke test the Agent Builder feature branch end-to-end against a hermetic project scaffolded by the skill (linked to the current worktree). Covers workspace reconciliation, stored agents/skills CRUD, ownership, visibility, stars, registry/library Copy flow, picker allowlists, model policy, RBAC role gating, role impersonation UI, builder defaults, infrastructure diagnostics, channels, and Studio + Agent Builder UI. Trigger when validating the agent-builder feature branch, PRs that touch packages/server, packages/playground, packages/playground-ui agent-builder routes, or builder EE code paths.

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productivity

Caura

The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a caura tool is present, whenever the user refers to past work ("what did we decide", "last time", "earlier"), or whenever any durable fact needs to be stored, recalled, superseded, or shared with the fleet. Do not use it for throwaway within-session scratch state.

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productivity

Checkpoint Promotion

Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.

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coding

Claude API

Up-to-date Claude API reference for model IDs, tool use, MCP, prompt caching, streaming, and SDK patterns so an agent stops guessing against stale docs.

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writing

Dataset Curation

Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.

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coding

Dynamic Tools

Runtime tool management with toolmanage and tools.toml format. Create, enable, disable, reload tools without restart. (/dynamic-tools, toolmanage, runtime tools)

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coding

Edit Flow

Create and edit pi-flows flows and agents from the main session. Use when the user wants to create a new flow, add or change an agent, or edit an existing flow/agent. Covers agent frontmatter, flow YAML, step types, model references, the flowagents and flowwrite tools, write locations, and how to fix validation errors.

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coding

Embedding Strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

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coding

Eval Harness First

Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.

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analysis

Evaluation Methodology

PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon.

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coding

Fern Docs

Maintain the NeMo AutoModel Fern docs site under docs/ (MDX content) + docs/fern/ (infra) — add, update, move, or remove pages; manage redirects, slugs, navigation, and version aliases; run validation and previews.

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education

Finetuning Method Selection

Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.

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coding

Frontend

Rules and best practices when working on the dashboard React frontend codebase (including the inlined Gram Elements code)

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productivity

Grounded Vault

Use when maintaining a durable Markdown knowledge store that agents compile from sources, when every number or quote in a wiki page must trace back to an immutable source, or when compiled pages need cheap drift detection against the code they describe. Teaches the raw/wiki/archive layout, per-claim provenance links, and git fingerprints for zero-token staleness checks.

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creativity

Grpo Rlvr Training

Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.

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coding

Hybrid Search Implementation

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

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coding

Immunity Agent

Runtime security for AI coding agents. Use when about to install a package, paste a secret, run a destructive command, set up a new workspace, or recover from a Prismor block.

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coding

Langchain Architecture

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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coding

Llm Evaluation

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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coding

Lora Qlora Recipes

Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.

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productivity

Memclaw

The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a caura tool is present, whenever the user refers to past work ("what did we decide", "last time", "earlier"), or whenever any durable fact needs to be stored, recalled, superseded, or shared with the fleet. Do not use it for throwaway within-session scratch state.

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coding

Memory Cleanup

Audits all memory systems in a Perseus environment, identifies stale or superseded stores (Mem0, Mempalace, old vector DBs, garbled federation artifacts), and deletes them. Keeps Perseus Mneme (the live system) as the single source of truth. Run periodically or when upgrading Perseus versions.

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coding

Momentic Explore Prompt

Generate an explore-prompt.md file that gives Momentic's explore agent (momentic ai explore diff / momentic ai explore latest) repo-specific context — which applications to test, the URLs tests must target, how to authenticate, where to save generated tests, and repo quirks. Use when setting up or improving the prompt file passed via --prompt-file.

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coding

Momentic Result Classification

Momentic is an end-to-end testing framework where each test is composed of browser interaction steps. Each step combines Momentic-specific behavior (AI checks, natural-language locators, ai actions, etc.) with Playwright capabilities wrapped in our YAML step schema. When these tests are run, they produce results data that can be used to analyze the outcome of the test. The results data contains metadata about the run as well as any assets generated by the run (e.g. screenshots, logs, network requests, video recordings, etc.). Your job is to use these test results to classify failures that occurred in Momentic test runs.

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coding

Perseus

Use when you need live project state (git branch, service health checks, recent sessions, task board, env vars, agent inbox) injected into context BEFORE the AI reads it. Perseus is a compile-before-context engine — it runs directives like @query, @services, @waypoint, @agora and hands the assistant a fully-resolved markdown briefing. Use for: deterministic session starts, multi-agent coordination, workspace audits, and anywhere you want "all the facts first."

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coding

Preference Optimization

Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.

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coding

Prompt Engineering Patterns

This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.

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other

Prompt Scanner

使用 agent-sec-cli 扫描 prompt 文本中的注入攻击和越狱尝试，返回结构化 JSON 扫描结果。当用户提到 prompt 安全、prompt 注入检测、越狱检测、提示词攻击检测，或者需要判断一段文本是否包含恶意 prompt 注入时，都应使用此技能。即使用户没有明确说"扫描"，只要涉及评估 prompt 文本的安全性，也应触发此技能。

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writing

Prysai Communication Failure Triage

Diagnose an already failed LLM interaction from the original request, visible context, actual reply or artifact, and expected outcome; propose the smallest communication repair and a controlled rerun. Use when a reply ignored constraints, answered the previous task, caused repeated rework, or remained impossible to accept. Do not use for an untried vague request, ordinary copy editing, platform troubleshooting without interaction evidence, or general prompt-template generation.

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analysis

Prysai Dialogue Brief

Turn an untried, low-risk request for LLM help into one short, copy-ready first-turn dialogue brief with a stated outcome, audience, supplied inputs, constraints, output shape, acceptance check, and stop boundary. Use when the user has a roughly defined communication goal but needs a clear first message; do not use for coaching, research, a task with files or external effects, or repairing an already failed reply.

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coding

Quantized Export

Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.

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coding

Rag Implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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productivity

Security Observability

只读查询 agent-sec-cli 已落盘的历史安全事件记录，并据此生成会话级安全复盘。仅当用户显式要求查看或审计已发生的安全事件、安全告警、安全审计记录，或要求按 session/run/trace/时间/类别筛选与统计已有安全事件，或要求复盘某次会话的安全判定时使用。不用于扫描新内容：检查代码安全性用 code-scanner，检测 prompt 注入用 prompt-scanner，审查 Skill 安全状态用 skill-ledger。不要因为对话中出现“安全”“工具调用”等字样、或为了主动自查而触发。

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analysis

Similarity Search Patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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coding

Simplicio Autoresearch

Evolutionary optimize-by-metric loop — mutate a target, evaluate against fixed criteria, KEEP if the score improves (commit) or REVERT if it doesn't (git checkout), repeat, plateau-break after N stagnated runs. Adapts Karpathy's autoresearch pattern (and the ECC autoresearch-agent) with mandatory yool guardrails (§11 caps), git-isolated branch discipline, an anti-Goodhart eval order (correctness gate FIRST, score SECOND, labeled tokenizer), a local-first mutation ladder, and a simplicio.savings-event/v1 receipt per run. Use when the user says "optimize this by <metric>", "autoresearch loop", "evolutionary optimize", "mutate/eval/keep-revert", or asks to shrink tokens/latency/bundle size/improve pass-rate against a fixed, measurable eval. Worker: scripts/autoresearch.py.

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coding

Simplicio Loop

Unified public entrypoint for Simplicio's body-of-work orchestration: core + loop in one command. Use when the user types /simplicio-loop, says \"ralph loop\", \"keep iterating until done\", \"finish all open issues\", or asks to drain a queue of work autonomously. Runtime-agnostic: binds a real stop-hook where the host supports hooks (Claude, Cursor); otherwise self-paces via the host scheduler. The older /simplicio-tasks surface is a legacy alias; never escape the loop with a false promise.

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coding

Skill Creator

Scaffold a new AI agent skill from a plain-language job description: folder, SKILL.md frontmatter, trigger description, and a first draft of the instructions.

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coding

Trace To Training Data

Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.

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analysis

Vector Index Tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

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creativity

Vision Sft

Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.

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coding

Writing Skills

Edit, tighten, and verify SKILL.md files so they trigger reliably and stay short enough for progressive disclosure.

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