“feedback”的搜索结果

Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.

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Resolve PR review feedback. Use when addressing review comments, resolving review threads, or fixing code-review feedback.

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Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.

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Handles PR review comments and feedback resolution. Use when user wants to resolve PR comments, handle review feedback, fix review comments, address PR review, check review status, respond to reviewer, verify PR readiness, review PR comments, analyze review feedback, evaluate PR comments, assess review suggestions, or triage PR comments. Fetches comments via GitHub CLI, classifies by severity, applies fixes with user confirmation, commits with proper format, replies to threads.

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Classical control and system dynamics for engineered systems — feedback and closed-loop control, transfer functions and block diagrams, poles/zeros and stability, time-domain response (rise time, overshoot, settling time), PID controllers and tuning, and frequency response (Bode plots, gain and phase margin). Traces feedback control from the flyball governor to modern controllers. Use when analyzing feedback loops, designing or tuning a PID controller, assessing stability from poles or margins, or characterizing transient response of a dynamic system.

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Retired. Reporting feedback and sharing a session are documented by the CLI itself: run `deepline feedback send --help` and `deepline sessions send --help`. Kept only so installed CLIs that still list this skill keep syncing.

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Review a pull request end-to-end using GitHub CLI. Covers diff analysis, commit history review, CI/CD check verification, severity-leveled feedback (blocking/suggestion/nit/praise), and gh pr review submission. Use when a pull request is assigned for review, performing a self-review before requesting others' input, conducting a second review after feedback is addressed, or auditing a merged PR for post-merge quality assessment.

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Build feedback collection for AI outputs. TRIGGERS - Use when user needs help with ai-feedback-collector related tasks.

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Build AI customer feedback analysis systems. TRIGGERS - Use when user needs help with ai-feedback-analyzer related tasks.

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Design AI feedback loops for continuous improvement. TRIGGERS - Use when user needs help with ai-feedback-loop related tasks.

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Design 360-degree feedback programs with surveys. TRIGGERS - Use when user needs help with 360-feedback related tasks.

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AI-DLCへのフィードバックを送信するスキル。 フィードバック内容のヒアリングとGitHub Issue作成を案内する。 Use when the user says "AIDLCフィードバック", "aidlc feedback", "フィードバック送信".

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Feedback loop implementation for continuous CTI improvement. Consumer feedback, analyst retrospectives, source quality tracking.

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Use when designing, configuring, distributing, or troubleshooting Salesforce Surveys and Feedback Management. Triggers: 'create a survey', 'NPS score', 'survey invitation', 'survey response report', 'customer feedback form', 'guest user survey access', 'SurveyInvitation', 'SurveySubject', 'SurveySettings', 'sendSurveyInvitation', 'survey not linked to case', 'deploy a survey between orgs'. NOT for choosing between CSAT, NPS, and CES as your CX metric — use architect/customer-effort-scoring. NOT for third-party survey tools like SurveyMonkey or Qualtrics.

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Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.

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Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on

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Tracks iteration progress toward task completion goals. Monitors quality trends, detects plateauing, and recommends when to stop iterating. Activate on 'convergence tracking', 'iteration progress', 'quality trend', 'stop iterating', 'progress monitoring'. NOT for iteration detection (use dag-iteration-detector) or feedback synthesis (use dag-feedback-synthesizer).

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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.

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When shaping feedback — situation-behavior-impact, growth-vs-corrective split, cadence design, ladder-of-inference checks. Triggers on 'how do I give this feedback', 'perf review shape'.

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Address GitHub PR review feedback at line, file, and PR level — fetch, evaluate, fix, commit, and resolve. Use when given a PR URL, a review-comment URL, or a PR-level comment URL. Triggers: GitHub discussion URLs, "fix this review comment", "address PR feedback", "resolve PR comments", PR URLs with #discussion_, #issuecomment-, or #pullrequestreview- fragments.

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Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.

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Synthesises exit interview notes or leaver survey responses into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim evidence, breakdowns by broad group only for groups that meet a minimum group size, referred items and open questions for the people team, without naming or identifying any individual. Use when the user asks to analyse, summarise, code, theme or report on exit interviews, leaver feedback, attrition interviews or departure surveys, or says "what are our leavers telling us", "theme these exit interviews" or "summarise the leaver survey". Do not use for customer or product feedback, use customer-feedback-theme-synthesis instead; for redacting one document with no theming, use document-deidentification-pass. Drafts for human review; never approves, authorises or signs off.

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Synthesises a batch of customer feedback (survey answers, reviews, ticket and chat comments) into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim quotes cited to record codes, contradictions and unverified claims, and the follow-up questions to answer next. Produces no sentiment score or percentage positive as fact; ratings appear only as distributions with n. Use when the user asks to "theme this feedback", "what are customers saying", "summarise these survey answers", "analyse these reviews", "find the top complaints in these comments" or "code this voice of the customer data". Do not use for exit interviews, use exit-interview-synthesis instead; for sorting a live queue and drafting replies, use ticket-triage-pack; for project retrospectives, use lessons-learned-synthesis. Drafts for human review; never approves, authorises or signs off.

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