Results for “quality-check”

Audit public skill or plugin bundles for ClawHub, Claude, Hermes, AgentSkill, AgentSkills.so, and GitHub release risks. Use when: checking Suspicious/upload flags, dangerous local behaviors, quality/security mismatches, legacy auth surface, or non-runtime files before publishing.

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

Transforms source materials (URLs, PDFs, DOCX, Markdown, text, screenshots) into a single-file HTML web article that is visually polished, offline-accessible, and shareable, using a structured editorial workflow with checkpoints and quality reviews.

Monitor paid-ad account pacing, delivery, performance, creative fatigue, tracking, policy, and data quality across supported platforms for daily or weekly checks, anomaly review, budget pacing, post-launch verification, or campaign monitoring.

Optimize any form that is NOT signup or account registration — including lead capture, contact, demo request, application, survey, quote, and checkout forms. Use when the goal is to increase form completion rate, reduce friction, or improve lead quality without breaking compliance or downstream workflows.

Activate when the user is drafting any academic economics content from scratch (e.g., outlines, abstracts, introductions, data/methods/identification sections, results narratives, conclusions, referee responses, table/figure captions) and needs economics-specific structure, phrasing options, and quality checks to produce publication-ready prose.

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

Manage your Radarr (movies) and Sonarr (TV series) media library from the terminal. Search and browse movies/series, add new content, check calendars, view queue and download history, inspect quality profiles, and trigger searches. Use when the user mentions Radarr, Sonarr, the *arr stack, adding a movie or series, checking the library, finding something to watch, what's in the queue, download history, upcoming releases, media server setup, or movie/TV automation.

Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts. Handles copy, sequences, landing pages, strategy docs, titles, charts, recruiting evaluations, or anything else that needs a quality gate. Recursively iterates until all scores hit 90+ (max 3 rounds). Use when asked to: "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "which version is better", "expert score", "evaluate this copy/strategy/page", or when another skill needs a quality gate on its output. Also triggers on: "score this landing page", "expert panel these email variants", "rate this headline", "panel these charts".

Audit images across AEM Edge Delivery Services pages for SEO and performance, checking alt text quality, dimensions, lazy loading, fetch priority, file naming, and decorative image handling, and generate per-page reports with fix instructions.

Creates comprehensive GitHub pull requests with automated quality validation and security scanning, then hands off to /github:review-pr for CI monitoring and reviewer-comment triage. This skill should be used when the user asks to "create a PR", "submit a pull request", or needs to merge completed work with full compliance checks.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Orchestrates the full academic research pipeline (research, write, integrity check, review, revise, re-review, re-revise, final integrity check, finalize), coordinating alterlab-deep-research, alterlab-paper-writer, and alterlab-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Use when the request mentions academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, or complete paper workflow. Part of the AlterLab Academic Skills suite.

Post-draft pass that expands a drafted article to capture the FULL keyword family — keywords the page already or could rank for, plus the Ahrefs Content Gap (keywords competitors rank for but we don't) — by weaving the worthwhile ones in as natural added paragraphs/sections. NOT keyword stuffing. Triggered after /draft, before /quality-check.

Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.

Audits natural-language programming artifacts such as SKILL.md, AGENTS.md, CLAUDE.md, slash commands, plugin manifests, hooks, rules, and prompt files. Use when reviewing AI-agent repositories, checking manifest-vs-disk consistency, scoring skill or agent quality, adding NL artifact CI gates, or diagnosing vocabulary and version drift across Claude Code, Codex, Cursor, Gemini, and Antigravity-style projects.

Review code changes for correctness, completeness, bugs, edge cases, and quality. Load when the user explicitly asks to review code, check a PR, review a diff, audit recent changes, or verify an implementation matches requirements. Also triggers on "review this code", "check this PR", "review my changes", "code review", "did this implement correctly", "audit this diff", or any explicit request for a formal code review. Do NOT load for "review changes for context" or "review what happened" — those are requests to read code, not to perform a formal review.

Conduz a execução do actions.md em código. Atualiza checkboxes para [X], escreve progress.jsonl, gera legacy-impact.md e regression-watch.md. Use quando o usuário digitar "/reversa-coding", "reversa-coding", "executar plano" ou pedir para começar a codar a feature ativa. Último skill do ciclo forward, depois de `/reversa-to-do` (e opcionalmente `/reversa-audit` ou `/reversa-quality`).

Run condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".

Create or update a MoonSpec feature specification from a natural language feature request or referenced declarative design. Use when the user asks to run or reproduce `/moonspec.specify`, create a `spec.md`, initialize a feature directory under `specs/`, preserve the original feature request for later verification, map source design requirements, or generate a single-story MoonSpec specification with a requirements quality checklist.

Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.

Perform a static audit of an AL workspace for Business Central against the project's copilot-instructions.md contract. Reports findings by class (Compliance, Code quality, Test coverage, Permissions) and severity (Blocker, Major, Minor, Nit) with file:line citations. Never modifies code. Never invents rules. Use when asked to audit a workspace, run a compliance check, review before PR, or diagnose contract drift.

Turn "is this new customer going to stick?" into a first-90-day diagnostic. Day-30 / 60 / 90 activation tracking, a sales-to-success handoff quality score, champion engagement during the onboarding window, time-to-value milestone hit rate, and an early false-negative catch on customers already adopting. Built for CS leaders and CSMs, customizable to your CRM and your product-analytics tool. Trigger on "how's {account} onboarding going?", "first 90 day check on {customer}", "is {account} activating?", "time-to-value on {customer}", "handoff quality", "who's at risk in first 90 days?", or any first-90-day adoption question.

Lightweight scanner tracking Smart Money market concentration across all Hyperliquid assets. Flags assets accelerating up the ranks before they become crowded top-3 plays. IMMEDIATE_MOVER signal fires on 10+ rank jumps with quality filters (erratic history, velocity gate, trader count floor, max leverage check). One API call per scan, runs every 60 seconds. Use when detecting SM rotations, finding emerging opportunities early, or monitoring rank acceleration patterns.

Review a built frontend against its chosen direction, catch drift back to generic AI defaults, enforce state coverage, ethical patterns, UX heuristics, and polish, and check contrast with APCA (not the legacy WCAG ratio). Produces specific, prioritized fixes — never vibes-based feedback. Works with pasted screenshots or Playwright MCP automated capture. Load when the user asks to review a UI, audit a design, check if a frontend looks generic or vibecoded, evaluate visual quality or polish, says "review this UI", "is this design good", "audit my frontend", "does this feel like [product]", "design QA", or when frontend-design routes here. Sub-skill of frontend-design.

Auto-audit any website end-to-end — technical health, SEO basics, broken links, mobile rendering, design quality, copy clarity, and conversion readiness — producing a scored, evidence-backed report. Two modes: PROSPECT mode (fast audit of a lead's site to generate evidence for outreach; emits a portable audit_packet JSON other tools can consume) and QA mode (deep pre-launch / post-launch gate on a site you built, with pass/fail checklist). Use whenever the user asks to "audit this site", "run a website audit", "what's broken on this site", "check this prospect's website", "QA this build", "is this site ready to launch", or provides a URL asking what works and what doesn't.

Design automated evaluation pipelines for LLM and agent systems — combining deterministic checks, statistical metrics, and LLM-as-judge scoring into repeatable, CI-integrated eval suites. Load when the user asks to set up automated evals, design an eval pipeline, integrate evals into CI/CD, create an eval suite, do eval-driven development, or says "automate my evals", "CI eval integration", "evaluation pipeline", "continuous evaluation", "monitoring eval quality", "set up regression testing for my agent". Sub-skill of eval-output orchestrator.

Use for PUA/try-harder productivity coaching when the user expresses frustration, repeated failure, quality complaint, passive behavior, says retry/change approach/don't give up, asks for evidence/completion check/test before done, or wants Ding-style workplace reminders. Triggers include: try harder, stop giving up, figure it out, again??, why still failing, change approach, no evidence, run tests, done without proof, 换个方法, 再试试, 别摆烂, 别偷懒, 为什么还不行, 又错了, 证据呢, 没跑测试别说完成, 验收, 闭环, 自嗨, 置身钉外, 无招, 老板体感. Do not use for calm first-attempt requests.

Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its structure/content/quality before deciding what analysis to run. Covers tabular (.csv .tsv .xlsx .parquet), arrays (.npy .npz .hdf5 .h5 .mat .fits), sequence/genomics (.fasta .fastq .sam .bam .vcf .bed .gff .gtf .h5ad), microscopy (.tif .nd2 .czi .lif .ims .dcm .nii), spectroscopy/MS (.mzML .mzXML .mgf .fid .jdx), chemistry (.pdb .cif .mol .sdf .xyz .gro), and proteomics/metabolomics (.pepXML .mzid .mzTab). For zero-shot forecasting of a series use alterlab-timesfm; to create/configure a chunked cloud array store use alterlab-zarr. Part of the AlterLab Academic Skills suite.

Use for PUA/try-harder productivity coaching when the user expresses frustration, repeated failure, quality complaint, passive behavior, says retry/change approach/don't give up, asks for evidence/completion check/test before done, or wants Ding-style workplace reminders. Triggers include: try harder, stop giving up, figure it out, again??, why still failing, change approach, no evidence, run tests, done without proof, 换个方法, 再试试, 别摆烂, 别偷懒, 为什么还不行, 又错了, 证据呢, 没跑测试别说完成, 验收, 闭环, 自嗨, 置身钉外, 无招, 老板体感. Do not use for calm first-attempt requests.

Python language (3.10-3.14). Covers typing, async, and modern patterns. Use when writing Python applications. USE WHEN: user mentions "python", "type hints", "dataclasses", "async/await", asks about "asyncio", "context managers", "match statement", "walrus operator", "PEP 695", "type parameter", "generic" DO NOT USE FOR: FastAPI framework - use `backend-fastapi` skill instead DO NOT USE FOR: Django framework - use Django-specific skill DO NOT USE FOR: Package management - use `python-packaging` skill DO NOT USE FOR: Linting/type checking config - use `python-quality` skill

Eliminate generic-looking UI — anti-default checklist, custom hover states, precision typography, non-stock spacing, surface depth, and production-grade polish that separates professional from template. Use when asked to "make this look less generic", "UI looks Bootstrap-y", "looks like a template", "add polish", "impeccable UI", "production quality design", "make it look custom", "it looks like every other SaaS app", "remove default styles", or "design lacks personality". Do NOT use for: design system token architecture — see design-system-gen. Do NOT use for: animation timing specifics — see motion-design.

Route web-game auto-playtesting with WAI Play (waiterve/wai-play): decide whether the next move is a testability check, authoring or repairing the `GameFlowAgentAPI` bridge, running a real browser playtest, reading the five-dimension quality report, or unblocking a key node the agent cannot reach. Use when the user wants an AI agent to actually play their HTML5 / canvas / vibe-coded web game and return reproducible evidence, scores, and fix suggestions across the five supported types (survivor-like, arcade shooter, platformer, puzzle/card, visual novel). Triggers on: wai-play, WAI Play, auto-playtest, AI plays my game, web game testing agent, GameFlowAgentAPI, GameFlowIntegration, jumpToScenario, game quality score, playtest evidence. Route Unity/Unreal frame-time work to `game-performance-profiler`, engine build failures to `game-build-log-triage`, human playtest notes to `game-demo-feedback-triage`, and generic browser automation to `browser-harness`.

Reconstruct a product flow, then audit how usable it is — whether it makes sense, where users get confused or stuck, whether the copy and instructions are clear, and where friction causes drop-off. Produces a prioritized, severity-weighted findings report. Works from three inputs: frontend code in a repo, a live running app driven via a browser, or a written flow / spec / screenshots. Use this skill whenever the user asks for a UX review, usability audit, "is this flow confusing", "does this signup/onboarding/checkout make sense", "why are people dropping off", "audit this form", "where's the friction", or hands over a flow and asks whether it's user-friendly. Trigger even on casual phrasings ("is my checkout any good?") — it imposes the structure ad-hoc UX opinions miss. Audits flow usability and friction; for visual polish and interface quality the impeccable audit/critique skills apply instead.

Sets up and audits the Claude Code harness for a project: CLAUDE.md, .claude/rules, skills, subagents, settings.json permissions, hooks, verification loop. Commands: kickoff (scaffold new setup), check-playbook (score an existing one), scouting-report (last scorecard), adjust (fix bloat/misplaced instructions), drill (turn advisory prose into real hooks/permissions/CI), decline (record an accepted risk), gameplan (work order with acceptance criteria before building), watch-film (check a diff against that order for scope creep/weakened tests/false claims). Use for setting up Claude Code, or on: "Claude ignores my CLAUDE.md", "it's huge and still misses things", "it said done but ran nothing", "it changed files I didn't ask about", "it weakened a test to pass", "rule, skill, or hook?", "is my setup any good". Not for code quality (code-audit), test coverage (test-assessment), one-off prompt wording (genie-proof-prompts), new skill authoring (skill-creator), or compacting a conversation (handoff).

Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only. (gstack) Voice triggers (speech-to-text aliases): "quality check", "test the app", "run QA".