Total 56,879 skills, AI & Machine Learning has 9458 skills
Showing 12 of 9458 skills
Add EAGLE-3 or draft-model speculative decoding to a Jetson vLLM server when TPOT is the bottleneck.
Report coding-agent progress, questions, decisions, blockers, tests, PRs, human replies, inbox instructions, and handoffs to the Agora coordination server. Use for every coding task when AGORA_URL is set, especially at session start, before risky or shared edits, when blocked, when asking for human or agent input, after running tests or verification, when opening or updating PRs, when polling for human replies or instructions, and before the final response.
Writes, rewrites, diagnoses, and improves any LLM prompt with minimal, high-signal edits. Use when the user wants to create a new prompt from scratch, review or fix a prompt that produces poor output, simplify or tighten instructions, restructure a long prompt, port a prompt between models, or expand an existing prompt. Covers system prompts, agent instructions, CLAUDE.md rules, SKILL.md prompt bodies, chat templates, structured-output prompts, RAG context templates, and prompt strings embedded in code. Also use when editing any file whose primary content is LLM instructions.
Optional AI SDLC architecture workflow. Use when an AI assistant needs to define system boundaries, components, interfaces, architectural constraints, alternatives, decisions, tradeoffs, risks, or validation for a feature and produce routed human and machine artifacts linked to requirements and durable decisions. Supports `--quick-flow` for focused design and `--full-flow` for strict decision, risk, and validation coverage.
AI SDLC controlled change-workspace and specification-delta workflow. Use when an AI assistant needs to create or validate an isolated proposal workspace, author and validate requirement deltas, preview canonical changes, or apply and archive an explicitly approved change with rollback evidence. Supports `--quick-flow` for assumption-driven drafts and `--full-flow` for strict owner, target, evidence, and authority checks.
AI SDLC package trust and privacy-preserving local metrics workflow. Use when an AI assistant needs to verify package origin, file integrity, harness compatibility, declared capabilities, provenance evidence, or generate reproducible aggregate run, retry, budget, coverage, and freshness metrics without collecting source, prompts, commands, or diffs. Supports `--quick-flow` and `--full-flow`.
AI SDLC reusable quality-lens workflow. Use when an AI assistant needs to challenge a requirement, design, plan, test strategy, change, or delivery artifact through pre-mortem, adversarial, edge-case, stakeholder-conflict, reversibility, abuse-case, operational-failure, or assumption lenses and finalize evidence-backed findings with ownership and traceability. Supports `--quick-flow` for selected high-value lenses and `--full-flow` for the complete applicable registry.
Tool-neutral CLI agent rules for TI MSPM0 development with Code Composer Studio, Keil/uVision, CMake/GCC/OpenOCD, SysConfig, and DriverLib. Use when an agent needs to inspect or modify MSPM0 projects, edit .syscfg configuration, avoid generated SysConfig/build files, use DriverLib APIs, validate SysConfig output, package reusable MSPM0 examples, or work on NUEDC-style MSPM0 embedded firmware.
Create, modify, run, inspect, analyze, and report Python experiments that use liblaf.cherries. Use when Codex needs to work under exp/YYYY/mm/dd/group-name/, write or edit numbered scripts in src/, run them with CHERRIES_NAME and CHERRIES_TAGS, inspect Cherries/Comet logs and generated assets, or write Markdown reports in docs/.
Use this skill to Upgrade Einstein Bots into Agentforce agents end-to-end in a single pass, orchestrating per-bot Agent Spec generation, planner reconciliation across bots, agentforce-generate authoring, and post-conversion .agent enhancements. TRIGGER when: user asks to migrate, upgrade, or convert one or more Einstein Bots to Agentforce; runs a multi-bot bot-to-agent upgrade; needs Einstein Bot metadata turned into Agent Spec handoffs and generated .agent agents; convert bots to agents; upgrade my service bots; move bots to Agentforce. DO NOT TRIGGER when: user already has an approved Agent Spec and only wants direct .agent authoring, deploy, test, or observe flows; the request is unrelated to Einstein Bot migration.
Parallel DAG-plan implementation skill. Reads a v-planning plan directory (root.md + step-<n>.md files), topologically schedules ready steps, and fans them out as parallel sub-agents. Use whenever the user invokes /v-implement, points at a plan directory produced by /v-plan, or asks to "run the parallel plan", "implement the DAG", or "fan out the steps" — even without those exact words. For linear plans (single .md file), use `implementing` instead.
Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.