「judge」の検索結果

Maintainer-only. Use when triaging inbound GitHub issues and pull requests on skyf0xx/hedgehog — "triage the issues", "check the PRs", "review inbound", "what's in the queue". Reads each item read-only, judges it for security and for whether it is real, then fixes and closes or comments and closes. Not part of the Hedgehog discipline a consuming project copies; this only applies to the Hedgehog repo itself.

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Executes a QA specification (specs/qa/QA-<N>-<slug>.md) written by /plan-qa: drives every step with the plan's drivers, verifies each Expected line mechanically ([assert]) or by evidence-backed judgment against the plan's written criterion ([judge]), pauses at named operator handoffs, and writes the results artifact QA-RESULTS-<N>-<slug>.md. Use only when the user asks to run or execute a QA plan — never trigger automatically.

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Define how unsupervised work is judged before it runs. Use when work will run without a human watching, optimizing toward something.

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One committed verdict on something that exists, cited. Use when something exists and a claim about it needs a verdict.

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Plan synthetic routes and judge whether a proposed molecule can actually be made, using AiZynthFinder's Monte-Carlo tree search over template-derived reactions and a purchasable building-block stock. Use this skill to configure expansion and filter policies, choose a stock file, run route search over a candidate set, and read the returned trees — solved fraction, route depth, and which building blocks a route bottoms out in. Also trigger on AiZynthFinder, retrosynthetic tree search, synthetic accessibility, SAscore, RAscore, building-block stock, reaction template, or route scoring.

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Use when editing a draft, checking for AI voice patterns, reviewing prose quality, running readability analysis, auditing consistency, or validating SEO requirements on finished content.

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Use when a user needs SCI/SCIE/ESCI/SSCI journal-submission help: evidence-backed candidate-journal discovery from a title, abstract, keywords, or manuscript text; public-metric and risk lookup for a known journal; or a pre-submission compliance check of a draft against a chosen journal's Author Guidelines and same-journal conventions. Also for Chinese requests such as 选刊、投稿期刊推荐、期刊查询、中科院分区、投稿前检查、期刊要求核对. This skill produces candidates, evidence, checks, and uncertainty; it does not judge research quality, predict acceptance, choose a uniquely best journal, draft prose, or simulate peer review.

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REQUIRED before declaring a task done when the diff touches user input, SQL, shell, auth, credentials, file paths, serialization, crypto, network endpoints, data deletion, or dependency surface. Judge by that surface, not the task label — research/experimental/local-only code with none of it can skip this. Walks OWASP/ASVS/MITRE questions the compiler and the test suite won't catch. Pair with `rafter run` on the same diff. When the diff has that surface, marking it complete without both has been under-delivered — do not claim done.

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Use when a report or article needs to be split into individual claims before it is graded, cited or used as evidence — the user asks "what does this report actually claim?", "split this into claims", "separate the facts from the judgements", or another skill needs a claim list. Turns one document into five to twelve atomic claims, each typed (observation, attribution, assessment, actor claim, victim disclosure) and anchored to the verbatim sentence it rests on, so every extraction can be checked against the source. Preserves the source's own hedging. Does not grade. Invoked by /quality-of-information-check; its output feeds /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments and /threat-actor-profiling.

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Use when validating, benchmarking, or monitoring LLM application performance. Keywords: RAG evaluation, LLM-as-a-judge, CI/CD gating, trajectory scoring, test suites, prompt quality.

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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"Apply dual-process theory to diagnose whether judgments arise from fast intuitive (System 1) or slow analytical (System 2) processing and identify resulting cognitive biases. Use this skill when the user needs to explain why quick decisions go wrong, design choice architectures that account for cognitive defaults, audit decision processes for heuristic errors, or when they ask 'why do people misjudge probability', 'how to reduce snap-judgment errors', or 'when does intuition fail'.".

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Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.

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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.

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Set up LLM-as-judge evaluation for ORC workflows

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Comprehensive social media operations for B2B SaaS brand building and engagement. Use this skill to develop LinkedIn strategy, decide whether to run a LinkedIn newsletter and how to scope it, capture consented registrations from LinkedIn Events, check LinkedIn Live eligibility, manage Twitter/X presence, create YouTube content, engage on Reddit communities, decide whether and how to answer on Quora, build community, develop influencer and creator partnerships, vet a creator before paying them, start or fix a B2B podcast, book podcast guests and guest appearances, judge whether a podcast is worth appearing on when you cannot audit its numbers, write show notes and transcripts, run social listening (what are people saying about us, share of voice, brand monitoring, validated sentiment, alert thresholds, routing signals to the right owner), and execute organic social campaigns. Also triggers on: LinkedIn, LinkedIn newsletter, LinkedIn Events, LinkedIn Live, event registration form, subscribe to our newsletter on LinkedIn, marketing consent checkbox, Notify Employees, Twitter/X, YouTube, Reddit, community, influencer, creator partnerships, influencer vetting, engagement rate, fake followers, engagement pods, FTC disclosure, sponsored post disclosure, media kit, social media strategy, organic social, employee advocacy, podcast, podcast strategy, podcast guesting, how do I know if this podcast has real listeners, the host says they get 50k downloads, is this podcast worth going on, podcast RSS feed research, IAB podcast measurement certification, Listen Score, Apple Podcasts reviews, show notes, audiogram, episode transcript, social media account access, who has admin on our LinkedIn page, the person who ran our social accounts left, locked out of our company account, social media offboarding, YouTube channel owner, Slack primary owner, social account MFA, social media approval workflow, pause all scheduled posts, social listening, what are people saying about us, brand monitoring, brand mentions, share of voice, social share of voice vs competitors, sentiment analysis, is our sentiment dashboard accurate, listening query, boolean query, mention alerts, spike alert, CrowdTangle replacement, Reddit API access, which platforms can our listening tool see, someone on Reddit asked for a tool like ours, comment mining, voice of customer from social.

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Use when a task needs the judgment of a Court Reporter — producing a realtime or CART feed during a deposition or trial, triaging a QC pass on a draft transcript before certification, drafting or applying an errata sheet under FRCP 30(e), reading back testimony from steno notes on a judge's request, or handling a sealed-record or in-camera confidentiality flag.

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Use when a task needs the judgment of a bailiff or court officer — deciding whether an in-custody defendant appears in visible or concealed restraints, running courtroom entry screening and staffing it for expected volume, handling a disruptive defendant or spectator under the judge's authority, moving a jury note between jury room and bench without an ex parte violation, or planning custody/security for a jury sequestration.

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Shared in-workflow quality gates for code/PR skills — the gate model, trust tiers, and the three primitives (evidence-before-done, independent judge, anti-gaming). DO NOT TRIGGER directly — referenced by consumer skills via relative path.

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Judges whether a user's input suits the Vibe Creating style of video-prompt writing, and when it does, distills single-scene prompts, multi-shot descriptions, emotional imagery, or mixed input into prompts that are easier for a video model to generate from — while preserving any user-specified dialogue, voiceover, music, sound effects, and other hard constraints. Use when a user wants to turn an idea, story, feeling, or rough/over-specified prompt into a strong text-to-video prompt (Seedance, Sora, Kling, Veo, Runway, etc.), or asks to "rewrite", "improve", "clean up", or "vibe-ify" a video prompt. Do NOT use for long narrative films that need precise word-for-word dialogue sync, industrial shot lists meant to be executed verbatim, or functional/UI demos and step-by-step tutorials.

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Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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Timeboxed ideation on a topic where lens-driven proposers generate structurally different ideas, blind critics cull them, and pairwise judges rank the survivors on a tournament ladder instead of 1-10 scores, evolving the leaders until the clock runs out. Use when the user wants to brainstorm, explore ideas, discover features, generate options, or think through possibilities for a specified duration. Triggers on requests like "brainstorm X for 30 minutes", "ideate on X", "spend an hour thinking about X", "what features should we build", "explore options for X". Not for executing a task over time (timeboxed-iterating) or optimizing a measurable metric (autoresearch).

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Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.

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