“feedback”的搜索结果

Run a structured sprint retrospective, a review of a short work cycle, using team feedback and delivery results. Identify shared themes and produce two or three prioritized improvements with owners, deadlines, and measures of success.

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• Facilitating a team retrospective
• Grouping survey responses and team feedback
• Turning problems into assigned improvement actions

Monitor an open GitHub pull request over time, respond to review feedback, and address automated check failures. Apply and push fixes, then report whether it appears ready to merge or what still blocks progress.

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• Monitoring a pull request until it appears ready to merge
• Addressing review feedback as it arrives
• Handling automated check failures during review

Review code changes or a pull request for bugs, effects on existing behavior, test gaps, and project standards. Receive findings supported by the code and a clear account of review coverage, with local fixes available when explicitly requested.

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• Reviewing a pull request before release
• Checking changes for bugs and test gaps
• Applying review findings locally

Refine a working feature through browser feedback from the user. Collect observations through typed comments or live speech and drawing, apply focused usability fixes, and save the changes in local commits.

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• Refining usability before release
• Adjusting a page while checking the changes
• Giving feedback through speech and on-page drawing

Group requests from Plain support threads, public reviews, and Slack into shared themes. Create or update one Linear issue per theme with representative quotes, mention counts, and sources, leaving priority and ownership decisions to people.

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• Group customer requests across support, reviews, and Slack
• Track mention counts and supporting quotes for each request
• Update existing Linear issues without creating duplicates

Enable PUA always-on mode from Codex CLI using a short command alias. It preserves existing configuration fields, restores feedback frequency when it is zero, and requires command output as evidence before reporting completion.

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• Enable PUA always-on mode in Codex CLI
• Restore feedback frequency from zero to five
• Update PUA settings while preserving other fields

Disables PUA's always-on mode from Codex CLI and sets feedback frequency to zero. The alias updates the local configuration while preserving unknown fields, then reports completion with evidence from the executed command.

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• Disabling PUA's always-on mode in Codex CLI
• Setting PUA feedback frequency to zero
• Using a Codex equivalent of Claude Code's /pua:off

Investigate recurring problems by mapping feedback loops, the cycles through which effects influence their causes. Identify underlying structures, match known patterns, and assess where changes could help and what unintended effects they might produce.

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• Investigating recurring incidents
• Understanding coordination problems as teams grow
• Assessing unintended effects before changing a policy

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Ask external AI command-line tools to independently review a development phase plan. Collect feedback by reviewer in REVIEWS.md, then feed the results into GSD planning to support revisions.

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• Getting independent AI reviews before implementation
• Comparing feedback from multiple AI reviewers
• Saving structured review feedback in a document

Review a Figma design, screenshot, or written description for usability, visual hierarchy, consistency, and accessibility. Receive specific findings, explanations, and prioritized improvements, with feedback tailored to the design stage and intended users.

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• Reviewing an early screen design
• Checking navigation and onboarding usability
• Finding inconsistent colors, spacing, and typography

Review legal essays using IRAC: issue, rule, application, and conclusion. Get feedback on missed issues, rule accuracy, reasoning, and organization, plus recurring patterns across practice sessions, without rewritten essays or model answers.

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• Get structured feedback on a legal practice essay
• Practice with a generated hypothetical legal problem
• Identify gaps in applying legal rules to facts

Get structured feedback on a legal memo, brief, paper, or exam essay without having it rewritten. Review organization, reasoning, clarity, and citation form, with three prioritized improvements and a record of recurring writing patterns.

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• Reviewing the structure of a legal memo
• Checking how rules connect to facts and counterarguments
• Prioritizing improvements before revising a draft

Build a custom fuzzer, a tool that tests code with many generated inputs, using LibAFL in Rust. Combine input mutation, execution feedback, scheduling, and target execution components when standard fuzzers offer insufficient control.

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• Build a fuzzer for a nonstandard target or architecture
• Implement custom input mutation and feedback strategies
• Prototype a new fuzzing research technique

Review Canva slides, posters, and flyers for layout, wording, visual priorities, readability, and accessibility. Get concrete fixes ranked by importance and organized by page, while keeping the original design unchanged.

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• Reviewing slides before a presentation
• Checking poster and flyer readability
• Improving wording and visual priorities in social posts

Organize unresolved feedback on GitHub pull requests, group changes by file or behavior, and apply selected fixes locally. Run relevant tests and summarize completed work and remaining issues, with replies and thread resolution requiring explicit user direction.

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• Organizing unresolved pull request feedback
• Applying selected review fixes locally
• Testing changes and summarizing remaining issues

Design, implement, tune, and test action combat for web games with clear attack and defense timing. Define reliable hit outcomes, give defensive moves distinct purposes, and verify that visual feedback matches combat results in repeatable scenarios.

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• Tuning attack, guard, and dodge timing
• Checking hit detection and combat feedback
• Designing boss phases and defensive choices

Design and implement sound feedback for Three.js or web games, from action cues to music transitions. Define which sounds take priority, respect volume controls, provide visual alternatives, and check browser behavior and audio performance.

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• Give attacks, hits, and blocks distinct sound cues
• Keep critical sounds audible during busy combat
• Change music smoothly as game states change

Register AI agents on Starknet as NFTs, unique digital ownership records, using ERC-8004. Manage agent details, collect authorized feedback, and request or review third-party assessments through the standard’s identity, reputation, and validation registries.

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• Register an AI agent on Starknet
• Update agent details and check ownership
• Submit feedback and review reputation scores

Search YouTube by keyword and collect linked video and comment datasets through the BrowserAct API. Gather titles, views, comment text, likes, and reply counts to support audience feedback research and topic monitoring.

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• Gathering viewer feedback for a specific topic
• Researching audience discussions around competitor videos
• Collecting comments about trending search terms

Collect Reddit posts and comments through the BrowserAct API using keywords, date filters, and sorting options. Results include votes, authors, timestamps, and reply relationships, providing structured source data for competitor research and reviews of community feedback.

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• Collecting Reddit mentions of competitors
• Gathering comments about a brand’s reputation
• Researching user feedback on products and topics

Collect customer reviews from Walmart using a product item ID. Retrieve ratings, review text, dates, verified purchase status, and selected product variants in structured form to prepare customer feedback research and product comparisons.

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• Collecting reviews across multiple pages
• Gathering customer feedback on competing products
• Preparing review comparisons by color or size

Extract seller details from Amazon, eBay and other marketplace profile pages. Collect names, ratings, review counts, positive feedback, membership dates and return policies to support seller research, while preserving each platform's rating format.

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• Comparing seller ratings and review counts
• Checking a seller's return policy and membership date
• Finding seller information from a product listing

Collect Amazon product reviews by supplying an ASIN, Amazon's product identifier. BrowserAct returns ratings, review text, publication dates, reviewer details, and verified purchase status without an Amazon login, providing data for feedback research and competitor comparisons.

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• Gathering reviews of competing products
• Collecting feedback to identify product improvements
• Preparing comparisons by verified purchase status