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概要と使いどころ
Find and score legitimate external authority and backlink opportunities as a dry-run register, using relevance, platform rules, risk, target-page fit, and explicit user approval gates. Use this skill when the user asks where links to a target site could be placed effectively, but never post, submit, DM, email, open PRs, or edit external accounts without separate explicit approval.
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。
Validate proposed or existing backlinks for relevance, editorial legitimacy, link attributes, spam/toxicity risk, platform compliance, anchor quality, target-page fit, and monitoring evidence. Use this skill when the user asks whether an external link opportunity or placed backlink is safe and useful, and reject toxic, irrelevant, paid-undisclosed, fake, or spam placements.
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。
Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.
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。
Manage an engagement buffer that ingests, prioritizes, rate-limits, deduplicates, and tracks state for incoming engagement items across platforms. Generates periodic digests and enforces cooldown periods. Composes with du-dum: du-dum sets the observation/action cadence, this skill manages the queue between beats.
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。
Start and maintain a fire using friction, spark, and solar methods. Covers site selection, material grading (tinder/kindling/fuel), fire lay construction (teepee, log cabin, platform), ignition techniques (ferro rod, flint & steel, bow drill), flame nurturing, and Leave No Trace extinguishing. Use when needing warmth, light, or a signal in a wilderness setting, when boiling water for purification, when cooking foraged food, or in an emergency survival situation requiring heat or morale support.
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。
Record insect sightings with location, date, habitat, photography, behavior notes, preliminary identification, and citizen science submission. Covers GPS coordinates, weather conditions, microhabitat description, macro photography techniques, behavioral observations, preliminary identification to order using body plan, and submission to citizen science platforms such as iNaturalist. Use when encountering an insect you want to document, contributing to citizen science biodiversity databases, building a personal observation journal, or supporting ecological surveys with georeferenced photographic records.
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。
Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.
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 and implement multi-stage CI/CD pipelines using GitHub Actions with matrix builds, dependency caching, artifact management, and secret handling. Create workflows that span linting, testing, building, and deployment stages with parallel execution and conditional logic. Use when setting up automated testing and deployment for a new project, migrating from Jenkins or CircleCI to GitHub Actions, implementing matrix builds across platforms, adding build caching, or creating multi-stage pipelines with security scanning and quality gates.
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。
Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.
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。
Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
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 working with iOS/macOS Keychain Services (SecItem queries, kSecClass, OSStatus errors), biometric authentication (LAContext, Face ID, Touch ID), CryptoKit (AES-GCM, ChaChaPoly, ECDSA, ECDH, HPKE, ML-KEM), Secure Enclave, secure credential storage (OAuth tokens, API keys), certificate pinning (SecTrust, SPKI), keychain sharing across apps/extensions, migrating secrets from UserDefaults or plists, or OWASP MASVS/MASTG mobile compliance on Apple platforms.
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 the user needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
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。
Multi-platform review aggregation and thematic analysis for any business
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 writing or debugging Linux drivers: platform, I2C/SPI, device-tree, char-device, IRQ, DMA, regmap, PM, sysfs, udev, NACK, -EREMOTEIO, or probe failures.
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 the user asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.
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 asked to implement under non-negotiable constraints: performance budgets, platform limits, legal or API rules. Not for remote, credential, publish, deploy, or irreversible changes.
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。
Integrate one exact-head Proto UI pull request through either the ordinary independent-review path or a separately explicit current-user connected-operation decision. Preserve source, DCO, live platform enforcement and all deferred criteria; never manufacture approval or acceptance.
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 these skills when you need to discover and explore data assets in the Knowledge Catalog. It allows you to search for entries, lookup specific metadata, and explore aspect types to understand your data platform's assets.
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 and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.
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 company & contact data. Turn your agent into a prospecting platform. Get contact information, roles, tech stack, business events, website changes, intent data. Build lead lists, research prospects, identify talent. 150M+ companies, 800M+ professionals, 50+ data sources.
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。
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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 for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.
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。
Multi-platform review analysis — pulls Yelp/Google/TripAdvisor reviews, identifies recurring complaints, sentiment patterns, response rate, and rating trajectory
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。
Generate personalized review responses for negative, neutral, and positive reviews — empathetic, professional, conversion-focused with platform-specific tone
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。