Results for “e-e-a-t”

Extracts and analyzes firmware images using binwalk to identify embedded filesystems, compressed archives, bootloaders, kernel images, and cryptographic material. Covers entropy analysis, recursive extraction, filesystem mounting, and string analysis for credential and configuration discovery.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting mu

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

Applies the ArchiMate enterprise architecture framework to structure analysis, break down complex problems, and align stakeholders with a common vocabulary.

Escreve microcopy de interface — mensagens de erro, estados vazios, tooltips, rótulos de botão, onboarding e notificações, com foco em clareza e acessibilidade.

Builds an external attack surface management (EASM) program using Shodan, Censys, and ProjectDiscovery tools for asset discovery, subdomain enumeration, service fingerprinting, and exposure scoring.

Orchestrator + builder for distinctive, production-grade frontends that don't look AI-generated. Derives stack and design context from product-soul/PRD/specs, then runs the anti-slop chain — explore distinct directions, lock a DESIGN.md system, build from golden examples with mandatory polish + every interactive/empty/loading/error state, then review. Load when the user asks to build a UI, design a frontend, build a landing page or dashboard or web app, beautify or redesign a page, make a UI look premium/playful/editorial, says "build me a frontend", "make this not look AI-generated", "design this interface", "give this real polish", or "frontend design". Routes to design-direction, design-system, design-review.

Build and scale developer-led adoption through ecosystem programs, community, and partnerships, focusing on what drives adoption.

Optimize post-signup onboarding, user activation, first-run experience, and time-to-value with structured audits, flow design, and measurement plans.

Generate comprehensive email marketing strategy with 90-day implementation roadmap. Analyzes business type (local-business, saas, ecommerce, creator, agency) to deliver industry-specific segmentation plans, automation sequences, content calendars, KPI targets, platform recommendations, and week-by-week rollout schedule. Use when building email program from scratch or restructuring existing strategy.

AI-era curl for agent web fetching, structure discovery, and deterministic structured extraction with token-budgeted output.

Multi-source search engine powered by AIsa API. Combines Tavily web search, Scholar academic search, Smart hybrid search, and Perplexity deep research — all through a single AIsa API key. Includes confidence scoring and AI synthesis. Use when: the user needs web search, research, source discovery, or content extraction.

Expert product analytics advisor for Senior PMs. Use when defining success metrics for a PRD, designing an A/B experiment, setting up an analytics tracking plan, analyzing post-launch impact, or when data exists but there's no clarity on what to measure. Produces structured metrics frameworks that connect to product decisions, not dashboards.

Diagnoses social threats and rewards across five domains to improve organizational, team, and product experiences.

Use this skill when a user needs KOL or influencer research, creator email lookup, similar-creator discovery, outreach-list building, influencer prospecting, or a contact table from TikTok, Instagram, or YouTube profile URLs. It uses AIsa's WaveInflu APIs to find verified creator emails, match similar YouTube or TikTok creators, enrich each recommended profile with contact emails, and return an outreach-ready Markdown table without inventing missing data. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

Provides operational frameworks and tools for turning strategy into execution, scaling processes, and building the organizational engine.

Design motivation systems for products and teams using Autonomy, Mastery, and Purpose (AMP) to drive intrinsic engagement.

Design and improve product user onboarding (first-time user experience) to drive activation and early retention. Produces an Onboarding & Activation Pack (aha moment spec, first 30 seconds + first mile plan, onboarding journey map, experiment backlog, measurement plan). Use for Growth teams.

Prioritize assumptions using an Impact × Risk matrix and suggest targeted experiments for each.

Guides setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

Evaluate any project, product, or system's claims against its actual implementation — scoring each claim for truth, identifying architectural gaps, assessing competitive positioning, proposing creative solutions, and producing an actionable roadmap. Load when the user asks to evaluate claims, reality-check a project, assess what this project actually does vs what it says, validate product claims, or score a system's credibility. Also triggers on "is this real", "does this work as claimed", "evaluate this project", "assess the gap between claims and reality", "how credible is this", "investor assessment", "score these claims", or "what's real vs marketing".

Translates high-level ideas into detailed Epic-level Product Requirements Documents (PRDs) that serve as the single source of truth for engineering teams.

Erstellt CE-/Konformitätsakten mit technischer Dokumentation, EU-Konformitätserklärung, Einbauerklärung, Anleitung, Prüfprotokollen und offenen Nachweisen für Robotikprodukte.

Analyze ELF binaries for exploitation vectors using checksec, ROPgadget, and pwntools for buffer overflow and ROP chain development during authorized security testing and CTF challenges.

Generate new feature ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints, then prioritize the best five.

Advanced web search via Tavily through AIsa API. Supports search depth, topic filtering (general/news/finance), time ranges, domain inclusion/exclusion, and LLM-generated answers. Use when: the user needs web search, research, source discovery, or content extraction.

Evaluate SaaS unit economics and capital efficiency to determine if a business can scale efficiently or needs correction.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment) to analyze and improve user engagement, retention, and re-engagement strategies.

Query the Open Targets Platform GraphQL API for target-disease associations, tractability and safety data, genetics/omics evidence, and known drugs. Use when identifying or prioritizing therapeutic drug targets, assessing target druggability/safety, or gathering target-disease evidence for drug discovery. Part of the AlterLab Academic Skills suite.

Map every stakeholder before engaging anyone, using an equity-aware process that captures not just obvious sponsors but gatekeepers, impacted communities, and overlooked voices.

Drafts stipulated ESI protocols for federal e-discovery. Produces court-ready agreements governing preservation, collection, processing, review, and production of electronically stored information under FRCP Rules 26, 34, 37 and Sedona Principles. Trigger when formalizing ESI procedures between parties during the discovery phase.

Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).

Discover hidden content, directories, subdomains, and API endpoints with ffuf — the fastest web fuzzer. Use when someone asks to "find hidden directories", "fuzz URLs", "discover API endpoints", "subdomain enumeration", "content discovery", "ffuf", "brute force paths", or "find hidden files on a website". Covers directory fuzzing, parameter fuzzing, subdomain discovery, virtual host enumeration, and recursive scanning.

This skill should be used when the user asks to "swap tokens", "trade ETH for USDC", "exchange tokens on Uniswap", "buy tokens", "sell tokens", "convert ETH to stablecoins", "find memecoins", "discover tokens", "research tokens", "tokens to buy", "find tokens to swap", "what should I buy", or mentions swapping, trading, researching, discovering, buying, or exchanging tokens on any Uniswap-supported chain. Supports both known token swaps and token discovery workflows (discovery uses keyword search and web search — there is no live "trending" feed). Generates deep links to execute swaps in the Uniswap interface.

Full-stack task execution pipeline — discovers tools via semantic search, routes to the right agent, then executes via GODMODE. Chains manus-tool-discovery → manus-orchestrator → godmode-system in sequence.