「reviews」の検索結果

Analyze sales pipeline, historical revenue, conversion rates, and assumptions to produce forecast scenarios. Use for sales reviews, RevOps planning, and founder revenue forecasting.

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Evaluate and tier suppliers using a weighted scorecard per SCOR DS Supplier Management practices. Score Quality, Delivery, Cost, Responsiveness, and Sustainability. Classify suppliers as Strategic, Preferred, Approved, or Conditional. Use during vendor reviews or procurement decisions.

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Format recurring KPI snapshots, exception flags, and owner follow-ups for weekly operations reviews. Use when the work is routine packaging of operational metrics and exceptions. Do not use for root cause analysis, process redesign, or full performance diagnosis.

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Build customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes.

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Build competitive intelligence briefs following the CI cycle — Plan, Collect, Analyze, Disseminate. Profile competitors, create feature comparison matrices, analyze pricing, build positioning maps, and identify win/loss patterns. Use when preparing for product launches, sales enablement, or strategic reviews.

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DevSecOps skill for securing CI/CD pipelines, infrastructure as code, containers, Kubernetes, cloud deployments, secrets handling, dependency management, SAST/DAST/SCA, release gates, supply-chain controls, SBOMs, policy-as-code, and secure SDLC workflows. Use for pipeline hardening, deployment risk reviews, security automation, and remediation planning.

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White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.

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Evaluate and review GO-CAM (Gene Ontology Causal Activity Model) activities / annotons — molecular-function typing, has-input, causal relations, complexes, evidence — and check their consistency with gene annotation reviews. Use when reviewing cached GO-CAMs under gocams/, filling a GoCamReview YAML (gocams/<id>/<id>-review.yaml), or grounding a module in GO-CAM models.

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Review computational protein or gene function predictions and fill PredictionReview YAML files using the COR/CNN/LSP/UNC/PLI/NPI/REP biological-validity taxonomy. Use for ProtNLM, DeepECTF, BioReason/GO-GPT, InterPro2GO, PANTHER/IBA, CLEAN, GloEC, MAPred, ProteinInfer, or other predicted EC/GO annotations, including audits of the evidence and reasoning in existing prediction reviews.

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Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".

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Frappe Drive guidance for files, folders, sharing, permissions, storage, previews, document workflows, and file-linked business processes. Use when work touches Frappe Drive or file management in the Frappe ecosystem.

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Generate web, mobile, and desktop prototypes, slides, dashboards, and editorial layouts from a single prompt using brand-grade design systems. Use when the user wants /web-prototype, /mobile-app, or /dashboard interactive HTML previews exportable to HTML, PDF, PPTX, or MP4 without Figma.

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Full legal plugin suite for commercial contract review, privacy/DPA analysis, and AI governance vendor reviews. Use when the user invokes /commercial-legal:review, /privacy-legal:dpa-review, /ai-governance-legal:vendor-ai-review, renewal tracking, DSAR responses, or EU AI Act triage.

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Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

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Use this skill whenever the user wants to see how their branch will look on the live Gaia website before merging — "preview this branch", "show me what this looks like", "deploy a preview", "trigger a design preview", "can I see the site with these changes", "preview the skill profile", "check how this renders on the site", "spin up a preview deployment". This skill dispatches a Cloudflare Worker preview deployment for the current branch via GitHub Actions — no local build tooling required. Ideal for containerized environments, design reviews, and confirming that newly curated skills or profile pages render correctly before a PR is merged to main. The preview does NOT regenerate docs; it deploys `docs/` from the branch as-is. If your change depends on regenerated Class S artifacts, run `gaia dev docs` and commit those first — the preview then reflects them.

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Phase 1 of the Gaia evidence verification pipeline. Use this skill whenever you need to gather, consolidate, or refresh raw evidence for named skills in the registry. Trigger phrases: "collect evidence", "populate the data lake", "gather sources", "run ev-collection", "Phase 1", "compile the evidence index", "refresh the unified lake", "aggregate skill evidence", "build the evidence database". Also invoke as the first step before running ev-star-verification, ev-adversarial-audit, or ev-link-validation — nothing downstream is meaningful without a fresh, compiled index. Covers GitHub repo links, stargazer signals, YouTube showcases, arXiv papers, peer reviews, benchmark results, blog/newsletter posts, and self-attestation artifacts.

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Code review by several models at once — Claude sub-agents, OpenAI Codex, and a cheap third reviewer via OpenCode — reconciled into one report. Reviews whatever is named: a diff, a branch, specific files, one function, a legacy module, a whole repo. Use for any review request, a second or third opinion, a cross-check before a PR, or "code-review", "multi", "consensus review".

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Audit essays, reports, literature reviews, dissertations, proposals, and source-backed assignments for high-score academic quality. Use when the user asks whether a paper can get a high grade, wants teacher/rubric feedback addressed, needs a pre-submission review, or needs checks for strict rubric compliance, academic standards, prompt/rubric alignment, argument quality, comparative analysis, evidence hierarchy, table/figure integration, citation specificity, conclusion strength, academic language, source-claim integrity, or evidence boundaries.

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 wants their way of working with Claude assessed — "review my session", "how am I using Claude Code", "audit my setup / workflow", "am I following best practices", "coach me", "what should I improve" — or pastes a Claude.ai/Cowork/Tag transcript and asks how they could have worked better. Do NOT use for reviewing code changes (that is code review) or for teaching a product from scratch (use academy-learn); this skill only reviews how the human works with Claude.

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。

Perform structured code reviews focusing on correctness, readability, security, and maintainability. Use this skill when reviewing pull requests, evaluating code changes, or establishing review standards for a team.

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Reviews game audio code for Wwise/FMOD integration safety, custom DSP plugin correctness, and audio middleware usage. Use when the user asks to review a Wwise plugin, check an FMOD DSP effect, audit game audio middleware integration, or asks "is my game audio code safe?". Trigger on phrases like "review my Wwise plugin", "check FMOD DSP effect", "is my game audio code safe", "review my audio middleware integration", or when you see AkPluginInfo, FMOD_DSP_DESCRIPTION, IMetaSoundSource, or Execute/process callbacks in game audio code.

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。

Reviews audio DSP code for numerical correctness. Use when the user asks to review, audit, or check DSP code for correctness issues — filters, feedback loops, fixed-point arithmetic, accumulation loops, or any numeric computation in audio code. Trigger on "check this filter for NaN", "why does my reverb blow up?", "is this numerically stable?", "why do I hear DC in my output?". Pairs with audio-dsp-review (realtime safety); this skill covers numeric correctness. Flag issues proactively.

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。

Reviews VST3 plugin implementations for spec compliance, host compatibility, and correctness. Use when the user asks to review a VST3 plugin, check bus arrangements, audit parameter handling, or verify their process() callback is safe. Trigger on phrases like "review my VST3 plugin", "check VST3 bus arrangement", "is my VST3 process safe", "audit my IAudioProcessor", or when you see IComponent, IAudioProcessor, IEditController, ProcessData, or Vst:: namespaced types in the code.

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。

Reviews YUP audio plugin code for YUP-specific correctness issues: AudioProcessor lifecycle, AudioParameterBuilder and AudioParameterHandle usage, editor gestures, AudioBusLayout handling, CLAP/VST3 wrapper contracts, state recall, and MIDI safety. Use when the user asks to review a YUP plugin, check a processBlock, audit parameter smoothing, or asks "is this YUP code safe?". Trigger when you see yup::AudioProcessor, yup_audio_plugin, AudioParameterBuilder, AudioParameterHandle, AudioProcessorEditor, or createPluginProcessor in the code.

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。