「output」の検索結果

Write a single-thesis narrative newsletter essay from Ahmad's OpenMark saves. Use when Ahmad wants a thoughtful long-form piece with one clear argument, not a roundup or news recap. Trigger phrases include "essay", "long-form", "think piece", "thoughtful post", "argue that", "make a case for", "/newsletter-essay". Output is one strong thesis sentence + 3–5 narrative sections + closing call-to-think, no tables, no bullets unless necessary.

日本語の概要は準備中です。原文の説明を表示しています。

Audit a composer output against four hard checks before delivery — cite integrity, voice rules, word count, schema validity. Always invoked after the polisher (English) or humanizer-* (Arabic / Hebrew). Emits a VerificationReport Pydantic object; orchestrator branches on `overall_passed`. Target ≥0.9 score per run. Triggers automatically as the final composer sub-agent; not user-facing.

日本語の概要は準備中です。原文の説明を表示しています。

Compose a short LinkedIn-post-style thread from Ahmad's OpenMark saves. Use when Ahmad wants a tight 200–400 word post he can paste into LinkedIn, X, or a short newsletter slot — NOT a long essay or full digest. Trigger phrases include "thread", "LinkedIn post", "short post", "tweet thread", "quick newsletter", "social post", "/newsletter-thread". Output is one strong hook + 4–7 short paragraphs + ONE link. Optimized for skim-reading on a phone.

日本語の概要は準備中です。原文の説明を表示しています。

Scrub English drafts for AI tells before publishing. Use for any English LinkedIn post or newsletter draft right after composition and BEFORE export. Removes em-dash-as-pause, weak hedges, rule-of-three patterns, AI vocabulary, negative parallelisms, and inflated symbolism. Triggers on "polish", "humanize", "make this sound human", "/polisher", or after any English composer output. Leaves Arabic and Hebrew drafts untouched — humanizer-* skills handle those.

日本語の概要は準備中です。原文の説明を表示しています。

Compose a categorical news-roundup newsletter from Ahmad's OpenMark saves. Use when Ahmad wants a scannable "what happened in AI / tools / research / discourse" recap rather than a single-thesis essay. Trigger phrases include "roundup", "news roundup", "this week in AI", "weekly news", "what dropped this week", "category recap", "/newsletter-roundup". Output is bucketed by topic with 3–5 items per bucket, short blurbs, source-tagged for at-a-glance scanning.

日本語の概要は準備中です。原文の説明を表示しています。

Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a weekly digest of what Ahmad saved in OpenMark over a given time window, clustered by topic. Use whenever Ahmad asks "what did I save this week", "what's new in OpenMark", "last week's saves", "this week's bookmarks", "what happened in my feed", "weekly recap", or any temporal summary of saved content. Faster than full newsletter — output is a scannable digest, not a published post. Triggers on "this week", "last week", "what's new", "weekly digest", "recent saves", "recap".

日本語の概要は準備中です。原文の説明を表示しています。

Compose a comparison newsletter from Ahmad's OpenMark saves — A vs B vs C, side-by-side, table-driven. Use when Ahmad wants to compare tools, models, frameworks, approaches, or platforms across his saves. Trigger phrases include "compare", "X vs Y", "head to head", "which should I use", "differences between", "trade-offs between", "/newsletter-comparison". Output centers a markdown table with consistent columns, supports it with short prose blocks, ends with a clear recommendation.

日本語の概要は準備中です。原文の説明を表示しています。

Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.

日本語の概要は準備中です。原文の説明を表示しています。

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

日本語の概要は準備中です。原文の説明を表示しています。

Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

日本語の概要は準備中です。原文の説明を表示しています。

You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.

日本語の概要は準備中です。原文の説明を表示しています。

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

日本語の概要は準備中です。原文の説明を表示しています。

Engineering operating model for teams where AI agents generate a large share of implementation output.

日本語の概要は準備中です。原文の説明を表示しています。

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

日本語の概要は準備中です。原文の説明を表示しています。

Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

日本語の概要は準備中です。原文の説明を表示しています。

Installs, configures, and tunes Snort 3 intrusion detection system to monitor network traffic for malicious activity using custom and community rulesets, preprocessors, and alert output plugins on authorized network segments.

日本語の概要は準備中です。原文の説明を表示しています。

Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

日本語の概要は準備中です。原文の説明を表示しています。

Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.

日本語の概要は準備中です。原文の説明を表示しています。

Run a live investigation as an operator-driven query loop: the operator runs each query on the live system and pastes the result back, and you guide the investigation one labelled query at a time. Use alongside support-assist (which covers what to investigate) whenever someone is pasting query or command output back from a deployment, especially during an incident. Covers labelling, pacing, making each query decide the next step, version-grounding, and keeping queries cheap and correctly classified.

日本語の概要は準備中です。原文の説明を表示しています。

Scrapes pump.fun/board using Chrome browser automation to extract the top 100 trending Solana tokens and writes structured markdown for a trading agent to consume. Use this skill any time you need to: scan pump.fun for new tokens, refresh the pump.md token list, run the scheduled board scrape, collect Solana meme token data, or build/update a trading watchlist from pump.fun. Even if the user says something casual like "check pump" or "update the token list" or "what's trending on pump", use this skill. The output file path and format are configurable but default to /Users/8bit/solanaos/pump.md.

日本語の概要は準備中です。原文の説明を表示しています。

Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.

日本語の概要は準備中です。原文の説明を表示しています。

Query Google Places API (New) via the goplaces CLI for text search, place details, resolve, and reviews. Use for human-friendly place lookup or JSON output for scripts.

日本語の概要は準備中です。原文の説明を表示しています。