「fusion」の検索結果

Use when testing JWT-based authentication for algorithm confusion, alg:none bypass, weak HMAC secrets, missing expiration, kid parameter injection, and token storage in localStorage. Trigger on: Authorization: Bearer tokens, JWTs in cookies, any base64url encoded header.payload.signature pattern, OAuth2 access tokens, API authentication tokens, SSO tokens, JWKS endpoints. Detects RS256→HS256 confusion, public key as HMAC secret, unverified kid values used in file reads or SQL queries, and JWT cracking with short secrets.

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。

Supply-chain testing via package-manager dependency confusion: when internal package names resolve to attacker-controlled public registries, leading to malicious install and script execution. Use for npm/pip/gem/Maven/Composer/Docker manifest review and authorized red-team supply-chain exercises.

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。

Orchestrate a whole-registry sweep of Gaia — fan out 13 parallel audit agents across every skill, run adversarial verification, surface Semantic Fusion candidates, propose new generic skill references, and synthesize a publish-ready HTML report under docs/meta/reports/. Use this skill for broad, systemic analysis across many skills at once: "run a meta sweep", "sweep the meta", "full meta audit", "audit the whole registry against META.md", "widespread nomenclature issues", "find all skills missing a GitHub link", "produce a meta report", "check for evidence type mismatches across the registry", "find skills that document a third-party tool with no attribution", or explicitly types /gaia-meta-sweep. This is the registry-wide macro companion to /gaia-meta-audit (a prioritized queue, single-pass) and /gaia-audit (fixes one skill). Use it when you need the full surface — 13 audit dimensions, adversarial verification, fusion proposals, and a durable findings artifact — not just a queue.

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Threat intelligence fusion skill for collecting, normalizing, deduplicating, enriching, and operationalizing IOCs and TTPs from OTX, MISP, abuse.ch, VirusTotal, Shodan, GreyNoise, Censys, CISA KEV, NVD, vendor blogs, and internal telemetry. Use to build STIX/TAXII feeds, ATT&CK-mapped actor profiles, prioritized blocklists, and detection-ready intel packages.

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Generate and edit images with diffusion models: prompting, seeds, CFG, img2img, inpainting, conditioning, and API integration. Use when adding AI image generation to apps or workflows.

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。

Expert in pan-Asian cuisines and modern fusion techniques across Chinese, Japanese, Thai, Vietnamese, and Korean cooking

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Stage and verify Fusion exports, drawing PDFs, flat-pattern DXF, viewport images and local renders. Use for engineering deliverables, not automatic publication or release.

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Prepare qualified Fusion CAM setups, operations, toolpaths and quarantined NC candidates using explicit manufacturing assets and authority. Never controls physical equipment.

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Prepare, execute and verify authorized Fusion CAD changes using resolved entities, explicit units and state-bound plans. Use for reviewed parameter, sketch, feature, assembly or configuration operations.

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。

Prepare, submit and reconcile reviewed Fusion Automation jobs with exact sources, scoped authorization and explicit budgets. Not for arbitrary model-generated cloud 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。

Inspect an explicit Fusion design, parameters, geometry, references, configuration and health without editing it. Use for model understanding or engineering audits.

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。

Configure and diagnose Fusion interoperability, transport selection, scoped profiles and qualification. Use for setup or connection problems, not design edits.

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。

Prepare a traceable local Fusion engineering review draft with source identities, artifacts and unresolved assurance gates. Not for sending, publication or release 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。

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

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Use when writing or reviewing Go code in fusionaly-oss — adding routes, handlers, domain contexts, background jobs, the manager CLI, wiring the app, or writing tests. Covers the cartridge framework, matcha for the manager, Phoenix Contexts, lifecycle/shutdown, and the test conventions.

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Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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This skill evaluates infusion site health and rotates sites appropriately to prevent lipohypertrophy or infection. It is triggered when clinicians inspect pump sites for irritation or lumps or review site rotation practice at visit.

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。

Adjusts intravenous bisphosphonate dosing and infusion duration for patients with hypercalcemia of malignancy and renal insufficiency (creatinine clearance <60 mL/min). Triggered when a clinician encounters renal impairment and asks how to modify zoledronic acid or pamidronate infusion rates.

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Interprets the saline infusion test (SIT) to assess the probability of primary aldosteronism when a patient has a positive aldosterone-to-renin ratio (ARR) and requires confirmatory testing. Triggers include evaluating post‑infusion plasma aldosterone concentration (PAC) after a positive ARR, hypertension work‑up, or when deciding whether to proceed to adrenal venous sampling (AVS).

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。

Expert-level mass transfer covering molecular diffusion, convective mass transfer, interphase transfer, mass transfer coefficients, and design of mass transfer equipment.

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Expert-level nuclear physics knowledge. Use when working with nuclear structure, radioactive decay, nuclear reactions, fission, fusion, binding energy, nuclear models, radiation, or nuclear applications. Also use when the user mentions 'radioactive decay', 'half life', 'binding energy', 'fission', 'fusion', 'alpha decay', 'beta decay', 'gamma decay', 'nuclear reaction', 'cross section', 'Q value', 'nuclear reactor', or 'radiation dose'.

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 and prevent public-over-private name resolution in npm, PyPI, and Maven.

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。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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。