“research artifacts”的搜索结果

Regenerate and commit Class S site-served artifacts after registry changes, then sync documentation across the main repo and the adjacent wiki. Use this skill when: you've changed registry/nodes/, registry/named/, or named-skills.json and need to regenerate docs/graph/* so the live website reflects the update; CI Guard E (docs-cohesion.yml) is failing because Class S artifacts are out of date; someone says "sync the docs", "regenerate docs", "run gaia dev docs", "update the site graph", "commit the Class S artifacts", "website is stale", or "update the wiki"; or you're finishing a registry PR and need to ensure docs/graph/gaia.json, docs/graph/gaia.gexf, docs/graph/gaia.svg, and docs/graph/named/index.json are committed alongside the source change. Also covers updating README.md, docs/index.html, and mirroring changes to the adjacent wiki repo at ../gaia-wiki.

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Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to evidence IDs and locators; review extraction candidates; migrate v0.2 PaperRecord JSON; validate or verify a v0.3 PaperPackage; generate research extensions; export reviewable JSON or Markdown; or safely update a compatible 13-field literature workbook.

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Design custom laboratory parts as adjustable build123d models and export STEP, STL, and DXF fabrication files. Check mating dimensions against standards, test geometry, and visually review holders, mounts, microfluidic parts, and other research fixtures before fabrication.

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• Designing holders for microplates, cuvettes, and tubes
• Creating mounts and adapters for optical tables
• Modeling microfluidic chips and molds

Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.

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• Compare repeated experiments to improve model training
• Improve an agent system's measured success rate
• Test prompt changes against a fixed evaluator

Research new security advisories for coding agents and MCP, the protocol connecting agents to tools. Update AgentSec Triage records with source evidence and regression tests, then validate generated artifacts and synchronize the approved feed with dependent guides and pages.

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• Refreshing threat records from recent security advisories
• Checking affected versions and fixes against primary sources
• Testing detector behavior for accepted threat records

Review econometric code, proofs, data workflows, and outputs with multiple specialist agents. Assess identification—the assumptions that let data establish an effect—alongside estimation, numerical stability, and reproducibility, then consolidate findings into prioritized tasks.

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• Checking estimation code and standard errors
• Examining assumptions behind identification arguments
• Reviewing proofs and derivations before submission

Create HTML design artifacts, including interface mockups, interactive prototypes, and slide decks, then preview and verify them. The skill also guides importing design references and building reusable design systems with shared styles and components.

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• Creating interface mockups
• Exploring user flows with interactive prototypes
• Creating slides and exporting them to PowerPoint

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

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Run a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say "preflight". Also trigger on phrases like "check my paper before arXiv", "verify my references", "scan for AI artifacts", "scan for LLM residue", "is my submission ready", or "review .tex/.bib before submit".

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Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.

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Codex-native batch Amazon keyword research using LaunchFast MCP. Use when the user wants a ranked comparison across many keywords and optionally wants HTML and CSV artifacts written to disk.

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Initialize a missing research Workspace from the repository template without overwriting existing Run state; use before Pipeline binding when the target under `workspaces/` has no valid core artifacts.

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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.

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Performs a gap analysis between two artifacts (a current state and a desired state) and produces a plain-language, stakeholder-readable report indexed by stable gap IDs. Use when the user wants to compare, evaluate, audit, or reconcile one artifact against another. Does not investigate runtime bugs — use investigate. Does not assess module-level architecture — use architectural-analysis. Does not research open-ended options with no second artifact to compare against — use research.

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Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".

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Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.

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Detect AI/LLM-generated text patterns in research writing. Use when: (1) Reviewing manuscript drafts before submission, (2) Pre-commit validation of documentation, (3) Quality assurance checks on research artifacts, (4) Ensuring natural academic writing style, (5) Tracking writing authenticity over time. Analyzes grammar perfection, sentence uniformity, paragraph structure, word frequency (AI-typical words like 'delve', 'leverage', 'robust'), punctuation patterns, and transition word overuse.

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Use NotebookLM through MCP or a local REST API to run cited Q&A, generate Studio artifacts, and manage high-volume research batches.

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Manually trigger artifact regeneration, versioning, and chore commit. Use when you need fine-grained control over when registry artifacts sync (outside the automated sync-artifacts.yml workflow).

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Use when a task needs the judgment of a Computer and Information Research Scientist — designing an ablation to attribute an empirical gain, budgeting an NSF CISE proposal against page and content limits, deciding what reproducibility artifacts to release with a paper, screening a draft for mathiness or terminology misuse, or scoping the consent/ethics review needed before running an experiment on real users.

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Draws an interactive HTML Architecture Viewer for any code flow the user names. Walks through seven research-driven steps (flow, name, repo research, lens colors, evidence flags, lens+mechanism per stop, build) and verifies links, claims, and generated artifacts before delivery.

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Structure AI work artifacts under .aiwork/{YYYY-MM-DD}_{slug}/ folders using the project's protocol (intent, triage, research, spec, prd, areas, plan, review, notes, docs/). Use when the user asks to plan, capture an intent, make a spec, triage, research a task, write a report, code review, summarize findings, save the work, document decisions, record this, write that down, persist findings, or references any path under .aiwork/. Also use when starting a non-trivial task that needs a plan or spec before implementation, or when finishing one and capturing notes.

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根据品牌资料、截图或现有代码制作HTML落地页、幻灯片和可交互原型，比较页面布局与视觉方案，并检查显示效果和交互行为。

• 根据品牌资料制作落地页
• 制作产品操作流程原型
• 用HTML制作演示文稿

Review codebases, APIs, and tools for vulnerabilities using source evidence and bounded local checks. Produces supported findings with impact, priority, and focused fixes, and can create structured reports for explicitly requested full audits.

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• Review security concerns in specific code
• Investigate a suspected vulnerability
• Audit a codebase and produce reports