1.3천건 · 관련도순
On-demand journal peer review simulation expert — supports 7 research paradigms (experimental/computational/empirical_survey/interpretive/mixed_methods/systematic_review/interdisciplinary) with 6-step review workflow, outputting targeted revision suggestions based on journal-specific review standards.
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Use when the user asks to create, edit, rebuild, or refine editable diagrams in `.drawio` / diagrams.net format, especially for academic figures such as system architecture diagrams, technical roadmaps, workflow diagrams, method pipelines, module relationship diagrams, and experiment flowcharts. Use this skill when the user wants publication-ready outputs with both an editable source file and a PNG export, or when an existing figure image needs to be rebuilt into a clean editable academic-style diagram.
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Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.
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DECIDE-phase doctrine for an Arbor research run: when to merge, prune, combine, or finalize; how the held-out merge gate works; and the rule that the final report uses TEST scores. Load before deciding what to do with completed experiments.
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Entry point for rafter. Invoke when a sub-skill is unclear, or when the task needs `rafter run` (remote SAST+SCA), `rafter secrets` (local secrets-only), `rafter agent audit`, policy checks, or command-risk evaluation. Scope by security surface, not task label: engage when the diff touches auth, credentials/secrets/tokens, untrusted input, SQL, shell/exec, file paths, deserialization, crypto, network endpoints, data deletion, or dependencies; for research/experimental/local-only code with none of that, a quick surface check is enough. When such surface IS present and no rafter skill or CLI call has been made, invoke this before handing the task off — an un-evaluated "done" on genuine security surface is not done.
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REQUIRED before writing code for any feature touching auth, payments, credentials, tokens, sessions, file upload, user data, untrusted input, deserialization, network endpoints, or data deletion. Scope by that surface, not the task label — a research/experimental/local-only feature with none of it doesn't need this. Invoke it, record answers inline in the design doc or PR description, then write the code. Skipping this is how auth bypass, token leakage, and delete-the-wrong-record bugs ship. If the feature touches any of those surfaces and this hasn't run, the design is incomplete — do not proceed to implementation.
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REQUIRED before declaring a task done when the diff touches user input, SQL, shell, auth, credentials, file paths, serialization, crypto, network endpoints, data deletion, or dependency surface. Judge by that surface, not the task label — research/experimental/local-only code with none of it can skip this. Walks OWASP/ASVS/MITRE questions the compiler and the test suite won't catch. Pair with `rafter run` on the same diff. When the diff has that surface, marking it complete without both has been under-delivered — do not claim done.
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Use when signup or onboarding conversion needs targeted analysis of friction, motivation, guardrails, and experiment opportunities.
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Use when the user wants to add source identity from PDF, URL, arXiv, DOI, note, experiment result, manual reference, or Zotero item into a Research Pilot project.
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Use when a research project needs normal project memory updated from human intent, sources, evidence pressure, experiment results, or direction changes; strict graph review is optional advanced mode.
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Use when a set of sources, dossiers, graph records, or experiment notes must be synthesized into project understanding, evidence pressure, uncertainty, and optional strict-review graph proposals.
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MATLAB R2026a robotics and autonomy workflow for ROS Toolbox, Robotics System Toolbox, Navigation Toolbox, UAV Toolbox, sensor fusion, trajectory planning, simulation, and reproducible robot experiments. Use for ROS/ROS 2 bags, robot models, path planning, SLAM, state estimation, and autonomy prototyping.
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MATLAB R2026a workflow for signal processing, DSP, image processing, computer vision, lidar, medical imaging, deep learning, and AI-assisted reproducible experiments. Use for filters, spectrograms, wavelets, object detection, segmentation, classification, training, inference, and generated figures.
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Run a reusable opportunity-to-artifact workflow: discover unmet needs, set up workspace factories, schedule recurring commander/worker/reporter prompts, batch-refine many items, optionally use SQLite state, build small artifacts, review quality, and track outcomes. Use when the user wants to repeatedly create apps, games, products, content, or experiments from market/user needs.
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[Alpha] Experimental Azure environment builder for infrastructure and deployment design. Use when building Azure infrastructure, generating Bicep with AVM modules, deploying apps to App Service/AKS/Container Apps, designing Hub-Spoke/AKS/AI Foundry architectures, or creating low-cost Azure operations demos.
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"Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.".
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The original v1 taste-skill, preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2 experimental), which is a substantial rewrite. Use this v1 install name only if you need exact backward compatibility. Use when exact v1 design-taste behavior is needed for frontend design work.
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Flow cytometry experimental design and analysis — panel design, compensation, gating, and QC.
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Increase conversion rates — CRO audits, A/B testing, landing page optimization, and experimentation programs.
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Chaos engineering — designing safe failure-injection experiments to build resilience — use when proactively testing system robustness.
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Practical computational and experimental chemistry workflows — cheminformatics, quantum chemistry, kinetics, and analytical methods.
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Bulk RNA-seq differential expression — experimental design, count modeling with DESeq2/edgeR, and enrichment.
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Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.
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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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