「evidence」の検索結果

Apply evidence-first engineering discipline to any agent-built software project: fact discovery before action, evidence-calibrated status reporting, explicit change boundaries and stage gates, controlled confirmation for risky or external actions, sensitive-information redaction for anything shared publicly, and resumable continuity after interruptions. Use when users ask an agent to take over an existing codebase, deliver a feature across stages, verify whether work is actually complete, judge release readiness, or keep conclusions honest about what is proven versus assumed. This foundation skill is domain-neutral; vertical suites (for example mini-program engineering) build on it by adding domain facts and platform rules.

日本語の概要は準備中です。原文の説明を表示しています。

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

日本語の概要は準備中です。原文の説明を表示しています。

Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Generate or redesign evidence-based README files for apps, libraries, CLIs, monorepos, and agent skills. This skill should be used for /readme-write, /readme, generate readme, write readme, improve readme, 帮我写 README, 美化 README, README を書いて, README 작성해 줘, напиши README, or their equivalents in any language. Confirm reader and visual preferences before generation; every README includes a source-backed flowchart, and every language edition keeps identical content apart from the language. Git-driven updates are routed to the companion readme-update skill.

Investigate a live, stalled or completed Anabasis run and turn findings into an evidence-bound fix proposal. Also answers whether a campaign is climbing: how the climb is going, whether the batteries are getting harder, why a difficulty decision keeps repeating, how to climb faster, why the controller chose its action. Also the narrow read: reviewing one campaign run or recorded case through its outcomes, whether a live run is still producing useful evidence. Reads evidence; does not launch the next experiment or stop a run.

日本語の概要は準備中です。原文の説明を表示しています。

Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

日本語の概要は準備中です。原文の説明を表示しています。

Audit recent high-relevance papers from a target venue to identify claim-relevant experimental evidence gaps after core P0 results exist. Use for venue completeness and reviewer-risk analysis; not for redefining the research question or running experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Direct a claim-driven research experiment project from post-Idea intake through method realization, prioritized evidence collection, Human Gates, and Evidence Freeze. Use for scientific planning, state transitions, queues, budgets, or recovery; not for running code or independently auditing results.

日本語の概要は準備中です。原文の説明を表示しています。

Publish one prepared Agent evidence packet as an additive Issue comment, including historical or closed Issues, under exact current authorization. Use after read-only Issue inspection and separately authorized evidence preparation. Do not reproduce, select work, upload assets, edit human text, claim, review, close, or change Issue metadata.

日本語の概要は準備中です。原文の説明を表示しています。

Analysis of Competing Hypotheses — structured technique for evaluating multiple explanations against evidence. Use when facing ambiguous attribution or multiple plausible scenarios. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Runs on ungraded evidence when asked, labelled as such, unweighted, and capped at Moderate confidence.

日本語の概要は準備中です。原文の説明を表示しています。

Focused single-target audit of one Gaia skill, named skill, or real-skill catalog item. Use this skill when: auditing a specific skill by ID or name, a skill looks overpromoted, evidence feels thin or dead, a contributor's skill seems outdated or superseded, the rank doesn't match the source quality, evidence is mapped to the wrong Evidence Type, a skill at 3★+ is missing a working GitHub link, someone asks "does this skill deserve its stars?", or you want the smallest possible correction with validation — not a bulk sweep. This is NOT for bulk curation passes (use /gaia-curate for that) or meta-level registry sweeps (use /gaia-meta-audit). It is specifically for "fix this one skill, get it right."

日本語の概要は準備中です。原文の説明を表示しています。

Produce the rendered evidence a human-gated frontend PR owes its reviewer: capture each changed surface (plus the before state for anything modified, plus mobile width), then generate one self-contained local HTML page the founder can open and review without checking anything out. Use when a PR is gated by the "Frontend changes are HUMAN-GATED" section of CLAUDE.md, or when the user says "/design-gate", "screenshot evidence for this PR", "prep this for founder review", "build the design evidence page", "what does this look like before and after". The page comes from a template script, not hand-written markup. Output is session-local and never committed. Do NOT use for non-gated changes: typo/grammar/factual fixes in an unchanged block, dead link targets, alt/ARIA fixes with no visual change, cache-bust bumps, or refactors with no rendered diff.

日本語の概要は準備中です。原文の説明を表示しています。

Quality review of test files and manual evidence documents. Goes beyond existence checks — evaluates assertion coverage, edge case handling, naming conventions, and evidence completeness. Produces ADEQUATE/INCOMPLETE/MISSING verdict per story. Run before QA sign-off or on demand.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of an Administrative Law Judge, Adjudicator, or Hearing Officer — applying a mandatory sequential/structured evaluation framework in required order, weighing conflicting medical or vocational opinion evidence using specific regulatory factors, writing findings that explicitly address significant contrary evidence in the record, drafting a credibility/consistency finding tied to specific record citations, or checking whether a draft decision would survive substantial-evidence review.

日本語の概要は準備中です。原文の説明を表示しています。

Inspect Caveman Cloud's eval-gated experiment lifecycle and block unsafe execution. Use when the user asks to start, approve, cancel, promote, or roll back a Caveman experiment, or asks what action an experiment's evidence supports. Read evidence first; do not execute lifecycle mutations until server-authoritative transition and evidence gates ship.

日本語の概要は準備中です。原文の説明を表示しています。

Use when recording, tallying, or triaging stochastic review findings — the append-only JSONL ledger where Tier 2 (advisory, unevidenced) reviewer output goes instead of blocking language. Triggers on "log this finding", "ledger add", "tally the findings", "triage findings", "is this finding recurring", "retire this noise", or whenever a reviewer produces an unevidenced concern that should be measured for recurrence rather than chased. For the review itself see code-review-and-quality or skill-library-review; for verifying formal claims with deterministic evidence see adversarial-claims-reviewer.

日本語の概要は準備中です。原文の説明を表示しています。

Read-first Figma gateway via Figma MCP: analyze designs, implement Figma-to-code for 1–3 screens, extract design tokens, produce a Figma Evidence Packet for planning handoff, screenshot fallback. Advanced operations (Code Connect, canvas writes, design-system/library patterns) are gated references loaded only on explicit intent. Triggers: 'figma', 'design link', 'implement this design', 'design tokens', 'produce a Figma Evidence Packet'.

日本語の概要は準備中です。原文の説明を表示しています。

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

日本語の概要は準備中です。原文の説明を表示しています。

医学证据等级排序与编排层 skill。当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / evidence grading / quality of evidence / 系统评价排序 / 临床证据排序 / 证据金字塔"时触发。本 skill 不直接调用 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按 ChatGPT 推荐的证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE 自动评级,自动剔除 editorial / letter / comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp),无 BioMCP 时走自建 slow-path 兜底。

日本語の概要は準備中です。原文の説明を表示しています。

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

日本語の概要は準備中です。原文の説明を表示しています。

How to MEASURE this repo instead of eyeballing it — interpret the loop-station context-budget trend (history.jsonl, thresholds, the tokenizer-switch gotcha), read a session trace, use the eval ledger (cost-per-success), and pick the right evidence tool (app-shots, smoke-deployed, db-counts, --check modes). Use when asked "is the harness bloating", "how big is CLAUDE.md really", "did this agent run actually succeed", "prove this works", "what evidence do we have", or when tempted to declare a capability (browser, tool) unavailable. For RUNNING the loop-station scripts, defer to the loop-station skill — this skill is interpretation + the wider evidence toolkit.

日本語の概要は準備中です。原文の説明を表示しています。

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

日本語の概要は準備中です。原文の説明を表示しています。

Drafts one anonymised DRAFT case study (situation, approach, outcome as evidenced, optional lessons) from engagement notes, status reports, closing reports, emails and metrics extracts the user supplies, with a fact trace to the notes, an evidence grade on every result, quotes only where the user supplied them with speaker role and permission, an anonymisation log and a confidentiality checklist that must be completed before publication. Use when the user asks to "write a case study from these notes", "turn this engagement into a customer story", "draft a success story for the website", "anonymise this project write-up" or "prepare a reference case for the proposal". Do not use for redacting identifiers from an existing document, use document-deidentification-pass instead; for checking whether a document's claims are substantiated, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.

日本語の概要は準備中です。原文の説明を表示しています。

Resolve complex or high-impact problems when the cause, solution path, requirements, or proof boundary is genuinely uncertain. Use when work needs evidence-led diagnosis, consequential design or implementation, or a completion audit; do not trigger merely because work has multiple steps, or for simple facts, fully specified routine changes, or pure creative generation.

日本語の概要は準備中です。原文の説明を表示しています。