Results for “inputs”

Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.

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Read the project's knowledge record before answering from memory or from the files. Use before answering about the project's state, a number, a date, a decision, a source, a deadline or what was agreed - even a casual 'what's the situation with X' or 'is that quote still current'; before changing a recorded value; and whenever the opener or a grounding line names entries. After finding relevant IDs, prefer kpop context to read them with their declared dependencies and checks. Uses pull for concise readings or when the checked reader is unavailable, affects for changed inputs, and search when no ID is known. A missing id is not absence. Not for showing the record's page (the hub skill). Requests come in any language.

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Load this skill whenever the project contains forms, inputs, selects, checkboxes, radio buttons, text areas, or any validation flow. Under no circumstances create a form without visible labels, error identification, and keyboard accessibility. Absolutely always associate every input with a label and provide clear, accessible error messages.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Build automated threat intelligence enrichment pipelines in Splunk Enterprise Security using lookup tables, modular inputs, and the Threat Intelligence Framework.

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Create or update the project constitution from interactive or provided principle inputs.

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Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.

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Drafts one service catalogue entry (description, scope in and out, owners and support groups, service levels exactly as provided, request path, dependencies, charging, lifecycle) from the service team's notes, questionnaire answers or existing documents, with UNKNOWN and a question list for every field the inputs do not cover. Never invents a service level, owner or price. Use when the user asks to "write the catalogue entry for this service", "turn these questionnaire answers into a service description", "tidy this request catalogue item" or "check this draft entry for empty fields". Do not use for a how-to or known-error article, use knowledge-article-drafter instead; for operating procedures, use runbook-drafter. Drafts for human review; never approves, authorises or signs off.

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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.

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Builds a DRAFT board paper skeleton from the sponsor's inputs: purpose, recommendation as the sponsor states it, options including do nothing, risks, financials exactly as provided, authority to decide and the decision sought, with every claim tagged evidenced, asserted or UNKNOWN so the sponsor sees what still needs support. Use when the user asks to "draft a board paper", "structure the committee paper", "prepare the decision paper for the investment committee", "skeleton the board memo" or "what goes in the paper for the audit committee". Do not use for a pre-read or briefing pack with no resolution sought, use executive-briefing-pack instead; to record a decision already taken in a discussion, use decision-memo-builder. Drafts for human review; never approves, authorises or signs off.

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Assembles a DRAFT capital expenditure request pack from the requester's inputs: business need, options including do nothing, costs exactly as provided, benefits as stated, risks, assumptions, budget status and the approvals route read from the organisation's own delegation of authority, with UNKNOWN for every missing figure, date or name. Use when the user asks to "prepare a capex request", "draft the business case for this equipment", "fill in the capital request form", "structure this investment proposal" or "what approvals does this spend need". Do not use for a board or committee decision paper, use board-paper-skeleton instead; for an IT change approval, use change-request-pack; to pressure-test a finished business case, use cfo-reviewer. Drafts for human review; never approves, authorises or signs off.

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Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.

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Extract bounded, source-linked context from large files or many inputs. Use when context is too large, files are oversized, or a task needs concise excerpts.

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Record provenance for an Agentic MIKE+ step via the mike-plus MCP server — a manifest.json tying the model copy, inputs, edits, run QA status, result files and figures together, each file with a sha256. Use at the end of every run/compare/plot task (successful or failed) so the work can be audited, re-compared or reproduced later. No license required.

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Validates whether an API request is correct based on provided inputs (method, URL, headers, body, auth, query params).

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Validate Claude Ads contracts, scoring inputs, run bundles, capabilities, source freshness, safety, installation, uninstall, or release readiness. Use for ads validate, ads status, ads next, stale claims with missing tool access, maturity checks, ownership-manifest uninstall, preserving unrelated ads-* skills, checksum verification, preflight, QA, or release audits. Missing access must demote stale evidence for the run and block dependent release-current claims before recovery guidance.

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Guide for optimizing MSBuild incremental builds. Only activate in MSBuild/.NET build context. USE FOR: builds slower than expected on subsequent runs, 'nothing changed but it rebuilds anyway', diagnosing why targets re-execute unnecessarily, fixing broken no-op builds. Covers 8 common causes: missing Inputs/Outputs on custom targets, volatile properties in output paths (timestamps/GUIDs), file writes outside tracked Outputs, missing FileWrites registration, glob changes, Visual Studio Fast Up-to-Date Check (FUTDC) issues. Key diagnostic: look for 'Building target completely' vs 'Skipping target' in binlog. DO NOT USE FOR: first-time build slowness (use build-perf-baseline), parallelism issues (use build-parallelism), evaluation-phase slowness (use eval-performance), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, search, target details); falls back to dotnet build /bl, binlog replay with diagnostic verbosity.

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Canonical patterns for writing custom MSBuild targets. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing custom target authoring anti-patterns, reviewing MSBuild target definitions for correctness, diagnosing broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets), fixing targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn), fixing query targets that return stale results due to Outputs vs Returns misuse, fixing missing Inputs/Outputs causing unnecessary rebuilds, fixing missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, hooking into the build pipeline, the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.

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在授权渗透测试中挖掘不安全反序列化（insecure deserialization）。当目标把用户可控数据交给 unserialize/Marshal/pickle/ObjectInputStream/BinaryFormatter/YAML.load 等还原成对象时使用——典型场景：cookie/token/viewstate/隐藏字段里出现序列化数据、导入文件触发对象还原、想借 gadget chain 拿 RCE。适用目标类型 Web / REST API（PHP/Ruby/Python/Java/.NET）。触发场景包括用户说"测下反序列化""这个 cookie 是不是序列化对象""能不能 gadget chain 打 RCE""phar/Marshal/pickle 打一下"。输出：识别出的序列化落点 + gadget 可用性判定 + 带外/回显证据的 finding（含 killed 记录）。

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Test normalized skills in a bounded temporary staging copy before production. Creates a provenance-marked copy under platform temp, rejects symlinked inputs, runs smoke checks with a minimal environment, and uses ls-debug-reproducible-failures on failure; no writes return to the repo until user approval. Use when validating a framework-compliant skill end-to-end on a supported platform.

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TanStack Pacer best practices for execution control in React — debouncing, throttling, rate limiting, queuing, and batching. Use when implementing search inputs, scroll handlers, API rate limits, task queues, bulk operations, or any scenario requiring controlled execution timing with reactive state.

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Use when integrating Google Gemini API into projects. Covers model selection, multimodal inputs, streaming, function calling, and production best practices.

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Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

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