Search: "expos"

Scans Android APKs for Firebase security misconfigurations including open databases, storage buckets, authentication issues, and exposed cloud functions. Use when analyzing APK files for Firebase vulnerabilities, performing mobile app security audits, or testing Firebase endpoint security. For authorized security research only.

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distribution", "compartmental model", "population PK", "popPK", "NONMEM", "nlmixr2", "Pharmpy", "Monolix", "exposure-response", "Emax", "EC50", "indirect response", "effect compartment", "TMDD", "PBPK", "bioequivalence", "RSABE", "ABEL", "allometric scaling", "first-in-human", "MABEL", "drug-drug interaction", "DDI", "ICH M12", "concentration-QTc", "therapeutic drug monitoring", "MIPD", and "dosing regimen".

Selects bounded, graph-informed source slices with Trailmark and delegates focused code analysis or patch-proposal work to a smaller subagent. Use when offloading function-, class-, caller-, callee-, call-path-, entrypoint-, or line-focused code tasks to constrained or locally hosted models without exposing the full repository.

Configures mewt or muton campaigns, analyzes surviving mutants, and investigates bugs exposed by testing gaps. Use when setting up mutation testing, reviewing campaign results, identifying equivalent mutants, or finding bugs from surviving mutations.

High-performance code-intelligence engine for AI agents and IDE, supports 257 languages, multi repositories, based on graph, with access via CLI, MCP Server, and API. AI coding agents teammate - expose only needed information, cutting token usage up to 50x. 100% local. Discord: https://discord.gg/39MFHu3J5d

Strategic-risk analysis skill for AI agents producing decision-ready memos on geopolitics, sanctions, trade, regulation, and strategic risk with explicit evidence boundaries, uncertainty, scenarios, actor incentives, trade-offs, and watch-next indicators. Use for a country risk brief, policy memo, sanctions or export-control exposure assessment, trade or regulatory implications memo, geopolitical scenario brief, strategic implications note for leadership, stakeholder and incentives analysis, a red-team challenge to an existing policy or risk view, or a decision briefing pack for founders, operators, investors, NGOs, compliance teams, policy teams, or leadership. Do not use for a simple news recap, encyclopedia-style overview, academic literature review, legal advice, intelligence-style certainty, decorative analysis, or unsupported quantitative forecasting.

Assess and classify legal risks using a severity-by-likelihood framework with escalation criteria. Use when evaluating contract risk, assessing deal exposure, classifying issues by severity, or determining whether a matter needs senior counsel or outside legal review.

A JetBrains IDE plugin that exposes an MCP (Model Context Protocol) server, giving AI coding assistants full programmatic control over the debugger. Set breakpoints, step through code, inspect variables, and evaluate expressions—all driven autonomously by your AI assistant.

Use this skill when an agency operator wants more than this week's headline across the book: a multi-period trend per client, each client benchmarked against the book's own average, and which single publisher shows up across multiple clients so a concentration risk in one partner is visible book-wide. This is the premium extension of the free `agency-portfolio-rollup` skill, not a replacement for it. Trigger on: "How has the whole book trended over the last few months?", "Which clients are underperforming the book average?", "Is any one publisher overexposed across our clients?", "Give me a benchmarked view of the book, not just this week".

Premium pack. Use this to prepare a full quarterly business review for a brand across its affiliate networks: quarter-over-quarter performance, top and declining partners, reversal and pending exposure, and a narrative with recommended actions. Trigger on: "prep the QBR for Acme", "quarterly business review for this brand", "build the Q3 review deck notes".

Use this skill when a publisher or agency wants to actually apply to a batch of Awin brand programmes they have not yet joined. The joinable set is read from the Awin API; applying is a click Awin exposes no API for, so it is carried out in the operator's own authenticated Awin session through Claude-in-Chrome. For every programme the skill surfaces the advertiser's terms for review, lets the operator drop rows, and submits only after one explicit batch confirmation that stands as informed acceptance of the terms shown. Trigger on: "apply to these Awin brands", "join these Awin programmes for me", "submit my Awin applications", "apply to my Awin shortlist", "join [brand] on Awin".

Use this skill when an agency operator wants the list of partner applications waiting on a decision for a brand: who has applied to the programme and is sitting in the pending queue. The output is a read-only queue; the approve or decline happens in the network dashboard. How the queue is read depends on the network. Some advertiser networks expose application status through the API. Awin does not: its advertiser API returns only joined publishers with no application status, so Awin's pending queue is read from the operator's own authenticated Awin session (new UI, app.awin.com) through Claude-in-Chrome, read-only. Awin Classic (ui.awin.com) accounts have no equivalent and are detected and stopped. Trigger on: "Which partner applications are pending on Acme?", "Acme's application queue", "Who's waiting for approval on [brand]?", "Show me pending partners for Acme".