“evidence-based”的搜索结果

Convert single-plane, single-channel two-photon TIFF images and timestamped behavioral position CSV files into NWB, a neuroscience data format. Preserve metadata, check clock alignment against acquisition evidence, and report format validation findings and source-to-output comparisons.

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• Converting two-photon TIFF images to NWB
• Combining imaging and behavioral position records
• Checking clock alignment using synchronization pulses

Review supplied writing through twelve DHDNA dimensions, a framework for describing reasoning and expression. Receive observations supported by quotations, optional scores, and comparisons between samples, with clear limits separating textual patterns from psychological traits.

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• Reflect on reasoning patterns in your writing
• Compare argumentation across selected texts
• Review how a text expresses assumptions and uncertainty

Investigate a GitHub issue or reported problem, check whether it is a bug, and test a proof of concept. Explain the behavior changes and supporting evidence so the user can decide whether to proceed with the full fix.

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• Checking whether a reported issue is a real bug
• Testing a proposed fix before full implementation
• Comparing performance before and after a change

Provide shared writing guidance and journal requirements for other installed Nature skills. This supporting reference package covers manuscript structure, evidence-based claims, and journal-specific formats; it is used through another skill rather than as a standalone workflow.

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• Referencing shared guidance through a Nature writing skill
• Consulting journal-specific submission requirements
• Reviewing manuscript length and repeated claims

Reconstruct how an AI coding agent produced a feature, pull request, or report using local ax records. Connect sessions, commits, skills, and tool activity into an evidence-based sequence with key decisions, checks, and a repeatable recipe.

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• Tracing how a shipped feature was built
• Reconstructing development from a commit
• Finding the workflow behind work done on a known date

Understand a codebase before searching for bugs or vulnerabilities. This skill examines functions line by line, documents assumptions and state changes, and traces calls across modules to build an evidence-based picture of system behavior.

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• Understanding a codebase before a security audit
• Tracing data and state changes across function calls
• Documenting assumptions for an architecture review

Analyze Antigravity coding session artifacts to explain scope changes, repeated rework, and underlying causes. Produce an evidence-based report with confidence levels, prompt assessments, recurring problem areas, and prioritized recommendations for improving future development sessions.

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• Reviewing sessions with repeated rework
• Separating scope changes from agent and codebase problems
• Finding files and subsystems associated with recurring friction

Turns supplied AI agent evaluation results into reports that distinguish autonomous success, human-assisted success, failure, timeout, and invalid runs. Makes metric denominators, retries, timing, costs, and comparison conditions explicit to support evidence-based decisions.

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• Separating autonomous and human-assisted success rates
• Reporting evaluations with retries and invalid runs
• Comparing agents or releases under matched conditions

Structure an evidence-based comparison of shortlisted accounting packages using 57 fields for capabilities, costs, and evaluator judgments. Request empty templates in CSV, SQL DDL, JSON Schema, or Notion mapping while keeping the final decision with the business.

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• Prepare a common comparison template for accounting packages
• Define shared tests for vendor demonstrations
• Organize quoted costs for first-year and three-year comparisons

Evaluate job offers by separating guaranteed pay, conditional bonuses, and estimated equity value. Compare terms, develop an evidence-based counteroffer, draft emails and call scripts, rehearse recruiter conversations, and prepare deadline extensions, acceptance, or decline messages.

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• Comparing pay and terms across job offers
• Drafting a counteroffer email
• Rehearsing a salary negotiation call

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

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• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Turn past classrooms and chat history into a reusable personal skill for course creation. It examines varied examples, checks recurring patterns and exceptions, and asks for corrections before saving instructions that reflect the user's workflow and quality expectations.

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• Reviewing patterns across past course creation
• Turning preferred workflows into reusable instructions
• Checking inferred preferences against examples

Investigate software failures by reproducing the problem, reducing the failing example, and locating its root cause. Receive an evidence-based diagnosis that separates symptoms from causes, with fixes applied only when authorized.

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• Investigating a crash
• Diagnosing intermittently failing tests
• Tracing incorrect output

Review open pull requests against the actual release branch, inspect code changes and failed checks, and produce an evidence-based table recommending direct integration, selective reuse, deferral, or closure, with contributor credit.

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• Reviewing the open pull request queue
• Selecting contributions for an upcoming release
• Checking conflicts against a local release branch

Review a set of GitHub issues against their full discussions and current code. Produce a table separating completed work, small fixes, design work, and deferred items, with evidence, confidence levels, and next actions for each issue.

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• Review a batch of issues or an entire milestone
• Find issues already addressed by current code
• Separate small fixes from design work and deferred items

Review and refine source-code comments and documentation attached to functions or other declarations. The skill removes redundant explanations, checks outdated TODOs, and preserves concise, evidence-based reasons and constraints that maintainers cannot readily infer from the code.

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• Cleaning up redundant code comments
• Reviewing TODO and FIXME markers
• Checking documentation when changing code

Audit GitHub Codespaces startup time and spending, then identify up to three evidence-based improvements to development containers, machine sizes, prebuilds, or idle timeouts, with validation results and expected impact.

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• Investigating slow Codespaces startup
• Reviewing Codespaces spending
• Trimming unused development container components

Audit GitHub Actions workflows and run history to identify wasted automated checks and runner time. Get up to three evidence-based improvements aimed at reducing runtime or cost while preserving required validation.

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• Investigate long GitHub Actions runs
• Review CI costs and unnecessary workflow runs
• Improve caching and cancellation of overlapping runs

Investigate high CPU usage in Java applications with Arthas. Identify busy threads, inspect method call paths, and produce an evidence-based diagnosis with specific methods to examine next.

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• Investigating sudden CPU spikes in Java applications
• Finding busy threads behind slow application responses
• Narrowing down methods for further tracing

Assess a company’s management through public statements, delivered commitments, crisis decisions, and use of company funds. Produce evidence-based scores for integrity, strategy, capital allocation, and governance, alongside a verdict on long-term trust.

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• Check whether executives delivered on past commitments
• Review acquisitions, buybacks, dividends, and new ventures
• Assess ownership, compensation, and related-party dealings

Inspect npm packages and dependency files for malicious installation scripts, credential theft, and self-spreading behavior. Use GuardDog scanning, source inspection, known-malicious version checks, and isolated execution to produce an evidence-based assessment.

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• Vetting a new npm dependency before adoption
• Reviewing dependency files during code review
• Checking lockfiles against known-malicious versions

Diagnose oh-my-codex installation and skill discovery problems by checking configuration, hooks, plugin caches, and optional Team runtime checks. Produce an evidence-based report and carry out confirmed repairs with user approval, then verify the results.

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• Investigate installation or skill discovery failures
• Check for duplicate hooks and legacy settings
• Inspect plugin cache version conflicts

Audit deployed Vercel projects for cost and performance improvements using production metrics. Examines usage, settings, and relevant code, then produces ranked recommendations supported by verified files and documentation matching the framework version.

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• Investigate Vercel usage and cost drivers
• Find routes with measured performance problems
• Assess caching opportunities using traffic data

Have a different AI model review code, analysis, or documents against the original skill's stated requirements. Receive evidence-based findings and a comparison of agreement and disagreement, then decide which recommendations to accept.

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• Getting a second opinion on substantial code changes
• Reviewing authentication and other security-sensitive changes
• Checking work before a bulk operation