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A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum

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Deep security audit of a smart-contract repo OR a live on-chain contract by address - detect Solidity, model the protocol invariants and trust boundaries first, run Slither (best-effort) plus a bounded agentic pass that hunts for a path breaking each invariant, triage, adversarially verify, prove with a fuzzer, and drive each finding through the shared responsible-disclosure routing. The dedicated contract arm split out of vuln-scanner.

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Applies Geoffrey Moore's chasm-crossing strategy for B2B tech products moving from visionary early adopters to pragmatist mainstream. Use when a product has early traction but stalls before mainstream adoption, when planning a beachhead/niche strategy, when designing whole-product offerings, when positioning against established competitors, or when scaling from innovator usage to industry standard. Triggers include 'stuck between early adopters and mainstream', 'we need a beachhead', 'pragmatist customers won't buy', 'how do we go from 10 to 1000 customers'. NOT for PLG/freemium SaaS (Slack, Notion, Cursor), pure consumer apps, two-sided marketplaces, or AI-native products with bottoms-up viral adoption - their dynamics break the visionary-to-pragmatist sequence.

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Builds lead generation systems using Alex Hormozi's Core Four framework (warm outreach, content, cold outreach, paid ads), lead magnets, and Rule of 100. Use when designing customer acquisition, scaling advertising, choosing marketing channels for revenue stage, building referral programs, or fixing low lead flow. Covers lead magnets, LTGP:CAC math, More Better New scaling, and Lead Getters (referrals, employees, agencies, affiliates).

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Build an influencer marketing strategy — finding influencers, briefing, contracts, deliverables, performance tracking, micro vs macro strategy, outreach, and FTC compliance. Use when the user says "influencer marketing", "influencer strategy", "work with influencers", "influencer outreach", "influencer brief", "find influencers", "micro influencer", "macro influencer", "nano influencer", "creator marketing", "brand ambassador", "influencer campaign", "influencer collaboration", "seed influencers", "gifting influencers", "paid influencer", "influencer ROI", or wants to use influencers or creators to grow the brand.

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Turns a brief, script, scene breakdown, treatment, or story idea into ONE connected director's shotlist for Seedance 2.0 — a single editable HTML artifact with a global Style Prefix, an @-asset glossary, and named per-scene prompts (1a, 1b, 2a…) each in Style → Characters → Scene → CUT 1..N form. Use whenever the user says 'make a shotlist', 'break this script into prompts', 'generate Seedance prompts for this ad/film', 'turn this brief into a shot list', 'director's shotlist', or wants many connected scene prompts rather than one. Also use to revise an existing shotlist (edit-once-propagates: 'change the style prefix everywhere', 'rewrite scene 4', 'split prompt 6'). Each prompt targets 15s; longer scenes split across 1a/1b/1c under one scene number.

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Weekdays, light browser lane. The one routine in this kit with an outward surface. Takes the slots that are due today, whose drafts were written yesterday and passed the copy check, whose hold box is not ticked, and whose destination the member wrote into the publish allow list themselves, and hands each one to the channel the member configured. It records a receipt the instant each slot lands. It never drives a browser control to publish, never replies, sends only where you released the channel a message, spends only where you released it, and never touches a credential.

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。

Weekly, file work only, no browser at all. Reads everything the week produced, picks exactly three moves, and argues both sides of each one before the member reads a word of it, with every clause carrying a number or an observation from a file it names. It argues against its own top recommendation last, records a predicted effect and the metric that would show it so the monthly review can score it, and files each move onto the register. It sends only where you released the channel, spends only where you released it, never deploys, and never touches a credential.

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。

Weekly, browser heavy. Unions every sitemap each property declares without a browser, builds the candidate set from URLs not already in the indexing ledger, then drives the member's own search performance console to inspect each candidate and request indexing inside an account wide allowance. Spends what is left on sitemap health, which is unmetered. It inspects, requests, and views or submits sitemaps inside that console and does nothing else there, ever, and it never removes a sitemap.

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Weekdays, heavy browser lane. Selects the qualified people who have never been written to, drafts each one a first touch into a dated queue file the member reads, and composes each passing draft into the member's own mailbox as an unsent draft. It verifies by the Drafts count and never by a toast. It holds every outbound action unless you released the channel, and it never touches a credential.

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。

Weekdays, heavy browser lane. Opens the member's own signed in profile, post, and notification surfaces for every platform in the channel plan, confirms that everything published since the last run is genuinely live, reads the counts attached to each post, captures every comment, mention, quote, reply, and inbound message into a queue the member answers by hand, and drafts a suggested reply for each one. Read only everywhere and totally read only on LinkedIn. It sends only where you released the channel, never replies, spends only where you released it, and never touches a credential.

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Use when a NEW product, offer, or course is going to market — beachhead segment selection, launch messaging, channels by phase across pre-launch, launch, and post-launch, milestones, success metrics, and go or no-go criteria. Trigger on 'go-to-market', 'GTM plan', 'product launch plan', 'we are launching a new product', 'beachhead segment', 'how do we launch this course'. Also use when the user has a finished product and no route to the first customers. Not for — the day-by-day execution checklist, see `60-launch-playbook-global`; the ongoing period plan, see `00-marketing-plan-global`; one campaign inside the launch, see `02-campaign-brief-global`; positioning first, see `58-positioning-global`.

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Dung khi can tim va tiep can khach hang doanh nghiep — xac dinh ICP, dung danh sach prospect co nguon va ngay verify, cham diem Hot/Warm/Cold, va viet outreach qua Zalo, dien thoai, email. Kich hoat khi user nhac 'tim khach hang B2B', 'danh sach prospect', 'lead gen B2B', 'tiep can doanh nghiep', 'cold outreach', 'ban cho cong ty'. Khong dung cho — khach hang o NUOC NGOAI thi dung skill 29-xuat-khau-b2b; tai lieu ban hang va quan ly pipeline thi dung skill 71-sales-enablement; khach hang le B2C thi dung skill 09-insight-khach-hang.

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Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

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Build an immersive scroll-scrubbed "fly through the world" landing page for any industry or brand using Higgsfield. As the visitor scrolls, a pre-rendered camera flies from outside each scene into its interior, then flows on to the next scene with NO cuts — one continuous connected flight (Emons-style isometric diorama world, or any art direction you pick). The skill interviews the user for the topic, the story beats/sections, and brand kit, then generates cohesive scenes + seamless camera clips with Higgsfield and wires a portable, framework-agnostic scroll-scrub engine. The video chain renders through Monid by default (Seedance 2.0, pay-per-clip USD — capability re-checked each build, see Step 4) with Higgsfield credits as the fallback biller. Use when the user wants a "3D world" / "browse-through-the-industry" hero, a scroll cinematic, a diorama landing, or to turn a business into a scrollable world.

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Your board of directors. Puts a business idea in front of a three-member board, each a sub-agent reading the idea through one published framework: the Offers lens (from Alex Hormozi's $100M Offers), the Monopoly lens (from Peter Thiel's Zero to One) and the Product lens (from Walter Isaacson's biography of Steve Jobs). Each member scores the idea, names the risks that would kill it and votes fund, fund with conditions, or pass. Use when the user says "test my business idea", "would this work", "pressure-test this", "what would a board say", "is this a good business", or starts the founder pack on a new idea.

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。

Teach a skill or concept at the user's current level, optionally preserving a lightweight multi-session learning record.

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。

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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 when a tutorial module plan exists but each module still needs a verifiable teaching loop.

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。

Teach the user a new skill or concept, within this workspace.

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。

Audits .NET test mock usage by tracing each mock setup through the production code's execution path to find dead, unreachable, redundant, or replaceable mocks. Use when the user asks to audit mock usage, find unused or unnecessary mock setups, check if mocks are needed, reduce mock duplication or over-mocking, simplify test setup, or review whether mock configurations like ILogger/IOptions should use real implementations instead. Supports Moq, NSubstitute, and FakeItEasy.

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Use when user asks about 藏传佛教, 噶举派, 大手印, phyag chen, 拙火, tummo, 那洛六法, 苦行, 闭关, 道歌, mgur, 米拉日巴, 玛尔巴, 上师瑜伽, 出离, 暇满, 中阴, 气脉明点, 觉受, nyams, 本觉, rig pa, or wants teaching in 米拉日巴尊者 Milarepa's voice. Triggers include "米拉日巴"、"密勒日巴"、"Milarepa"、"道歌"、"十万歌集"、"大手印"、"拙火"、"那洛六法"、"玛尔巴"、"噶举"、"白教"、"山洞修行"、"苦行"、"上师瑜伽"、"中阴"、"明空" — invoke whenever user's question touches Tibetan Kagyu / Mahāmudrā / yogi practice or asks about Milarepa's life and teachings, even without explicit request.

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Use ONLY when the user says they do not know which master or which teaching mode to use — 不知道问谁, 该找哪位祖师, 该用哪个模式, 有哪些法师, which master should I ask, help me choose. This is a router, not a teacher: it names a destination and stops. If the user asks an actual doctrinal or practice question, do NOT invoke this — let the matching master skill answer directly.

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Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).

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。