「scoring」の検索結果

Build investor-ready pitch scripts in multiple formats (10-min, 5-min, 2-min, 1-min elevator, investor email). Produces pitch narratives, Q&A preparation, pitch scoring rubric, and optional investor roleplay practice. Use when the user wants to create a pitch, prepare for investor meetings, craft a startup pitch, write a fundraising narrative, or practice their pitch. Triggers for "pitch deck", "investor pitch", "pitch my startup", "fundraising deck", "seed deck", "how to pitch", "investor meeting", "demo day", "prepare pitch", "pitch script", "elevator pitch for investors", "pitch practice", "practice my pitch", "investor roleplay", or any request to present a startup to investors, accelerators, or partners. Works standalone — no prior startup-design session needed, but leverages its output if available.

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Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".

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Penetration test and red team report writing methodology. Covers executive summary structuring (risk-led narrative for non-technical readers), technical finding format (title, severity, affected scope, narrative, reproduction steps, impact, remediation, references), CVSS v3.1 / v4.0 scoring with vector justification, OWASP risk rating, evidence hygiene (redacting credentials, hashing client data, time-stamping every action), screenshot and PoC artifact management, finding chain narratives, scope/limitations/assumptions documentation, retest evidence and remediation tracking, deliverable formats (PDF, DOCX, HTML, JSON for SIEM ingestion), client-customer-deliverable separation, and common report mistakes (over-CVSSing, undermining the triager, missing the 'so what'). Use at the end of an engagement when authoring a deliverable, when restructuring a draft for executive readability, or when establishing a reusable report template for a consulting practice.

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。

Bug bounty report writing for H1/Bugcrowd/Intigriti/Immunefi — report templates, human tone guidelines, impact-first writing, CVSS 3.1 scoring, title formula, impact statement formula, severity decision guide, downgrade counters, pre-submit checklist. Validation gates and the submittability/always-rejected decision are owned by triage-validation; this skill owns the written report itself (templates, tone, formulas). Use after validating a finding and before submitting. Never use 'could potentially' — prove it or don't report.

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。

Complete OKR & Strategy Execution system — from company vision to weekly execution. Covers goal hierarchy, OKR writing methodology, scoring rubrics, alignment cascading, KPI dashboards, review cadences, team accountability, and quarterly planning rituals. Use when setting goals, running planning cycles, tracking OKRs, building KPI dashboards, running retrospectives, or aligning team work to strategy. Trigger on: "OKR", "objectives", "key results", "goal setting", "quarterly planning", "KPIs", "strategy execution", "annual planning", "team goals", "alignment", "review cadence", "what should we focus on", "prioritize", "goal tracking", "north star metric".

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。

Reviews pitch decks and provides investor-ready feedback with scoring

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。

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.

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。

Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.

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。

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

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。

Data quality audit on CRM with completeness scoring and specific fix recommendations

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。

On-page SEO optimization for existing content with before/after scoring

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。

Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.

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。

Aesthetic assessment and design scoring across 6 dimensions. Use for UI critique, design review, visual quality assessment, remix suggestions. Activate on "design critique", "aesthetic review", "UI assessment", "visual quality", "design score", "remix this design". NOT for implementation (use frontend-developer), accessibility-only audits (use color-contrast-auditor), or brand identity creation.

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 or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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。

Validates agent outputs against expected schemas and quality criteria. Reports structural and content checks within the declared evidence scope. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).

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。

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).

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。

Performs claim-evidence verification for agent outputs, recording supported, contradicted, or insufficient-evidence results alongside source accessibility. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).

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。

ScepticAgent AI prompt reference. Use whenever the user asks about the prompts, wants to change analysis behaviour, modify what the AI looks for, adjust highlight categories or scoring, understand what instructions are sent to the AI, or tune the output format of analysis or highlights.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。