「completion」の検索結果

Super Base Context — project doc loader & synchronizer. Triggers even without explicit "load docs"/"sync docs." READ MODE — load architecture/modules/coding/decision docs when work references project state: code work (write/modify/remove); design (features/APIs/schemas/architecture); review (modules, past solutions); tech selection; ADR research; migration/refactor planning; performance/security analysis; tests; code reviews; any spec/plan/design/ADR referencing project. WRITE MODE — NET DELTA vs. last doc sync: additions/modifications/REMOVALS/deprecations of previously-synced code or decisions (docs revert: delete BCU file/ADR), AND rollbacks. User signals: sync docs/done/ready to commit/pre-commit/record decision. Auto-fires before final task-completion reply. WRITE MODE does NOT fire on: brainstorming, generic/read-only Q&A, in-session try-and-undo with no net change, already-synced.

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Front-door router voor het werken aan een regelrecht-dossier. Gebruik dit aan het begin van een dossier, of bij twijfel over waar te beginnen — het triageert de situatie en routeert naar de juiste skill (regelrecht-stelselanalyse voor desk-review/corpus-completion, regelrecht-audit-products voor live expert-validatie) en de juiste volgorde. Bevat het routeringsprincipe: feitelijke defecten routeren naar de desk, oordeels-/praktijkvragen naar de workshop. Dossier-agnostisch.

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Generates CVE request packages from pentest findings. Reads cve-candidates.json (auto-generated at pentest completion) or findings.json directly, then produces for each qualifying vulnerability: MITRE CVE form data, GitHub Security Advisory draft, full disclosure report, and vendor notification email. Invoke manually after a pentest engagement when you have true-positive findings that warrant CVE IDs.

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Deep API security assessment beyond surface scanning. Covers the full OWASP API Security Top 10 (2023): Broken Object Level Authorization (BOLA / IDOR), Broken Authentication, Broken Object Property Level Authorization (mass assignment + excessive data exposure), Unrestricted Resource Consumption, Broken Function Level Authorization (BFLA / vertical privilege escalation), Unrestricted Access to Sensitive Business Flows, Server-Side Request Forgery via API parameters, Security Misconfiguration, Improper Inventory Management (shadow/zombie/deprecated endpoints, v1/v2 drift), and Unsafe Consumption of third-party APIs. Works across REST, GraphQL, gRPC, SOAP, and MCP servers. Discovers APIs from OpenAPI/Swagger specs, GraphQL introspection, gRPC reflection, .well-known endpoints, JS bundles, and traffic capture. Uses kiterunner, ffuf, schemathesis, restler-fuzzer, openapi-fuzzer, graphql-cop, clairvoyance, batchql, inql, jwt_tool, postman, mitmproxy, and manual http(action="request", ...) payloads. Every technique includes actual payloads, commands, and verification logic. Chains from /pentester or /codebase when API endpoints are discovered, chains into /web-exploit when classic injection points are found in API parameters, chains into /post-exploit when RCE is achieved, and chains into /ai-redteam when an LLM/AI endpoint is discovered (chat APIs, completion endpoints, RAG search, agentic tool-use, MCP servers).

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Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints

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Official OpenAI SDK patterns for TypeScript/Node.js — client setup, Chat Completions, Responses API, streaming, structured outputs, function calling, embeddings, vision, audio, and production best practices

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Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints

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Official Mistral AI TypeScript SDK patterns — client setup, chat completions, streaming, function calling, structured outputs, embeddings, vision, Codestral FIM, and production best practices

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Check whether the work is actually finished rather than finished-looking — what was promised versus what really runs. Several models compare the task against the code, and every completion claim has to survive a command that was actually executed. Use before calling something done, before a PR, at the end of a session, or on "is this done", "did I finish", "check if done", "what did we skip".

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Execute a project from a folder of raw materials. Use when the user drops messages, screenshots, documents, drafts, and references into a project directory and wants Codex to identify the real goal, build a plan, do the work, drive the project to a submission-ready state, verify completion, and return a clear final summary of what was required, what was completed, and how it was done.

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Land a PR by monitoring conflicts, resolving them, waiting for checks, and squash-merging when green; use when asked to land, merge, or shepherd a PR to completion.

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在宣称工作完成、已修复或测试通过之前使用，在提交或创建 PR 之前——必须运行验证命令并确认输出后才能声称成功；始终用证据支撑断言

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End-of-story completion review. Reads the story file, verifies each acceptance criterion against the implementation, checks for GDD/ADR deviations, prompts code review, updates story status to Complete, and surfaces the next ready story from the sprint.

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OpenUI generative UI with OpenAI SDK backend. Streaming chat completions with gpt-5.5 (or any current OpenAI-compatible model).

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Product-led growth and customer-base growth for B2B SaaS. Use this skill for self-serve funnel work (activation, time-to-value, free trial vs freemium, PQL definition, in-product upgrade moments, sales-assist triggers), for the self-guided interactive product demo that runs in front of the login wall (demo portfolio and flow design, gated vs ungated policy, demo seed data and privacy, keeping a published demo in sync with the shipping product, step-level demo analytics), and for growing revenue from existing customers (adoption, expansion, churn-save, the self-serve cancel flow and failed-payment recovery, renewal marketing, net revenue retention, and champion tracking: watching the people who bought and used you at each customer, confirming a job change from evidence, and running both plays a move fires, the seat they left and the account they joined). Also triggers on: PLG, product-led growth, activation, aha moment, time to value, free trial, freemium, PQL, product qualified lead, onboarding, self-serve, customer marketing, expansion revenue, upsell, cross-sell, churn, retention, NRR, renewal, cancel flow, cancellation flow, save offer, exit survey, pause subscription, dunning, failed payment recovery, involuntary churn, people keep canceling, champion tracking, job change signals, job change alerts, our champion left, our champion changed jobs, customer alumni, former customers who moved to a new company, interactive demo, self-guided demo, product tour, click-through demo, demo automation, demo library, should our demo be gated, ungated demo, demo without a sales call, our demo is out of date, demo shows the old UI, put a demo on our homepage, demo completion rate, demo drop-off, try before you talk to sales.

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Use when a task needs the judgment of an Etcher/Engraver — compensating a resist pattern for expected undercut before etching fine detail, calculating etch time from a known etch rate rather than judging completion visually, verifying a functional engraving's line width/depth against its actual accuracy requirement, or re-validating process parameters on a new material batch before production.

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Use when a task needs the judgment of a Cooling/Freezing Equipment Operator — calculating freezing time for a new product thickness via the thickness-squared relationship rather than assuming linear scaling, verifying freezing completion by actual core/thermal-center temperature rather than surface temperature or elapsed time, recognizing a temperature plateau as expected latent-heat removal rather than a malfunction, or matching belt speed/residence time to the calculated freezing time requirement.

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Programmatic form completion — web forms, PDF AcroForms, and data mapping — use when automating repetitive form filling.

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Manage the user's Google Tasks: lists, task details, creation, updates, and completion.

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Generate a pre-launch readiness checklist for feature releases. Use this skill when: - A feature is nearing completion and you need a launch readiness checklist - You want to ensure all pre-launch items are covered (docs, training, support, rollback) - You are planning a phased rollout and need a structured go-to-market plan

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Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.

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Use when maintaining vailá File Manager Frame A buttons, previewed local operations, CLI parity, or interactive SSH transfer with status-file completion.

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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Use this skill when configuring MCAE (Account Engagement / Pardot) lead scoring models, grading profiles, score decay rules, or automation rules that fire on score/grade thresholds. Covers: scoring point values per activity, score decay for inactivity, Profiles for fit grading, combined MQL definitions (Score + Grade), and automation rules vs completion actions. NOT for defining the MQL model before build — use admin/lead-scoring-requirements. NOT for Engagement Studio nurture programs — use admin/lead-nurture-journey-design.

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