3.5천건 · 관련도순
Upload Kleros evidence, policies, or metadata to IPFS through a paid gateway. Receive a CID, a content-based file identifier, and a viewing URL. Each request uploads one file and costs $0.01 USDC on Base mainnet.
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Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.
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Canonical entry point for the full Gaia evidence verification pipeline. Invoke as /evidence-verification-pipeline or /ev-pipeline — both names trigger this skill. Use whenever you need to run the evidence phases end-to-end: collecting raw evidence from the data lake, verifying live GitHub star counts, running Phase 2B benchmark-source verification, running adversarial auditing for noise and URL errors, and checking link health via Firecrawl. Also use when someone says "run the evidence pipeline", "verify the evidence lake", "audit evidence", "run ev-pipeline", "full evidence check", "pipeline run", "prepare evidence for ingestion", or "refresh the data lake". This is the pre-ingestion step — run it before importing any evidence into the registry.
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Browse registered trading strategies and compare evidence from backtests, which simulate trades on historical data. Filter by market conditions, trade counts, risk-adjusted returns, and cost feasibility, inspect evidence age, and rebuild the evidence cache from local runs.
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Use when reviewing Java enterprise evidence for MiFID II investment services, investment activities, client classification, suitability, appropriateness, order-handling evidence, best-execution evidence, algorithmic-trading governance evidence, market-access governance evidence, transaction evidence, record keeping, monitoring, or compliance-owner handoff. This should trigger for requests such as Review a Java investment-service platform for MiFID II evidence; Assess suitability, appropriateness, order-handling evidence, or best-execution evidence; Document audit, monitoring, or clock-synchronisation gaps for algorithmic-trading governance; Assess investment-service engineering evidence before production release. Part of Plinth Toolkit
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Guides privacy audit evidence collection processes including evidence planning, sampling strategies, documentation standards, chain of custody, interview techniques, system walkthrough procedures, and evidence evaluation. Covers ISO 19011 evidence categories (records, statements of fact, observations) and ISACA audit evidence requirements for privacy compliance assessments. Keywords: audit evidence, evidence collection, sampling, chain of custody, audit documentation, interview techniques.
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REA 조사 도구를 설계하거나 변경할 때 담당 범위, 입출력, CLI 및 MCP 호출 규칙을 정리합니다. 조사 근거의 의미와 검증 방법을 포함한 설계안을 작성합니다.
Test the Senpi adapter and task engine against the real senpi binary in an isolated environment. Check task and team behavior, record evidence in a consistent location, and verify that the existing agent directory remains untouched.
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Builds an evidence map for a disputed decision, linking supporting evidence, counterevidence, qualifications, and missing facts to precise source passages. Produces a structured JSON artifact that makes the reasoning and unresolved questions available for review.
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Collect evidence from a compromised computer before shutdown or remediation erases it. Preserve memory, network connections, processes, and system state, then record integrity hashes and handling history to support incident investigation and evidence tracking.
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Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.
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Assess scientific claims by examining study design, statistics, bias, and evidence quality. Use structured appraisal frameworks to identify supported conclusions, explain uncertainties, and propose practical improvements with concerns traced to specific evidence.
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Turns a control list and an audit scope into draft evidence requests grouped by control owner, each with the control as stated, the audit period, evidence examples (commonly requested) and a proposed due date derived from the fieldwork dates. Never judges whether evidence is sufficient, never selects the sample and never sends a request; the auditor decides. Use when the user asks to "prepare the evidence requests", "draft the PBC list", "build the evidence request pack for the audit" or "what do we need to ask each control owner for". Do not use for document requests and interview questions built from an audit plan, use audit-prep-pack instead; to check claims in a report against sources, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.
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Builds an evidence map for one document, section or contested claim: every factual claim extracted verbatim and numbered, its supporting passage in the supplied sources or UNKNOWN, the strength of that support as evidenced, contradictions between sources, and the specific question or document that would close each gap, returned in the chat as Markdown tables for the author or reviewer. Use when the user asks to "check what this document actually proves", "map the evidence behind these claims", "which of these statements are supported", "build an evidence map", "what would we need to back this up" or "is this claim substantiated". Do not use for requesting evidence of security controls from control owners, use control-evidence-request-pack instead. Drafts for human review; never approves, authorises or signs off.
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Organize ISO evidence for medical devices and laboratories, including controlled documents, risk records, corrective actions, and supplier controls. Produce structured gap reports for authorized human review while keeping certification, accreditation, and regulatory processes distinct.
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Search scientific papers by topic or DOI, a paper’s identifier, and organize evidence extracted from full text. Connect claims with experiments, reported statistics, limitations, and source locations for literature reviews and evidence tables.
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Review test files and manual verification records for development tasks. Check whether expected behavior, edge cases, screenshots, and required approvals are covered, then report evidence gaps and distinguish completion blockers from advisory improvements.
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Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.
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Phase 4 of the Gaia evidence verification pipeline. Checks every URL in the evidence data lake for HTTP liveness — catches dead links (404, 403, timeout, connection errors) before evidence is ingested into the registry. Use this skill when running the full /ev-pipeline, asked to "validate links", "check evidence URLs", "run link validation", "find dead links in the data lake", "check HTTP status of evidence sources", or "verify evidence links still work". Wraps validate_sources.py as a temporary coexistence URL-health shim.
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Phase 1 of the Gaia evidence verification pipeline. Use this skill whenever you need to gather, consolidate, or refresh raw evidence for named skills in the registry. Trigger phrases: "collect evidence", "populate the data lake", "gather sources", "run ev-collection", "Phase 1", "compile the evidence index", "refresh the unified lake", "aggregate skill evidence", "build the evidence database". Also invoke as the first step before running ev-star-verification, ev-adversarial-audit, or ev-link-validation — nothing downstream is meaningful without a fresh, compiled index. Covers GitHub repo links, stargazer signals, YouTube showcases, arXiv papers, peer reviews, benchmark results, blog/newsletter posts, and self-attestation artifacts.
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医学证据等级排序与编排层 skill - evidence grading orchestrator. 当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / 系统评价排序 / 临床证据排序 / 证据金字塔 / 给 PMID 打分 / Practice Guideline 优先 / Cochrane 优先 / 反向证据交叉验证 / evidence grading / quality of evidence / evidence pyramid / GRADE assessment / Cochrane review ranking / clinical evidence ranking"时,**调用本 skill 进行医学证据自动分级,自动选择最佳信息源**。本 skill 是编排层(L2)·不直接打 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,按证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE A/B/C/D 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。跨平台无锁(Claude Code / Codex / Gemini CLI)。
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Builds and maintains a DRAFT impact log from notes, messages, status updates and completed work: one row per outcome with its evidence and evidence level, date, who benefited and the source it traces to, plus a gap list of outcomes that still lack evidence, structured so a later review, appraisal or promotion case can draw on it. Use when the user asks to "start an impact log", "add this to my impact log", "log what I achieved this quarter", "pull my wins out of these notes", "turn these messages into evidence for my review" or "what do I have evidence for so far". Do not use for a performance rating, ranking, pay or promotion decision, the skill records evidence and never judges it; for the review narrative itself, use performance-review-drafter; for a role profile built from the work, use job-description-drafter instead. Drafts for human review; never approves, authorises or signs off.
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기존 테스트와 부족한 부분을 점검합니다. 요구 사항, 업무 규칙, 결함을 식별 번호와 실행 결과에 연결하고 테스트 대장으로 검증 여부를 추적합니다.
Select, inspect, validate, and report Paperclip evaluations for Runner protocol behavior or complete product workflows. Interpret results with evidence, source revisions, costs, and failure categories, and refresh reports from retained evidence without new provider calls.
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