Results for “anum-qualification”

Detect and fix code-quality and correctness issues in AEM as a Cloud Service projects locally, without external services or network calls.

Analyze content sequences from page structure to determine whether each should be default content or a specific block, and validate block selection for AEM Edge Delivery Services imports.

Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

Scores an inbound message on budget, fit, and intent, then recommends whether to call, nurture, or skip the lead.

Logic (anao-logic)

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.

Logic (amao-logic)

Agent validation v4.3.0 — Make agents act effectively by disabling harmful actions, lowering gates, and injecting cross-run learning

Audits hidden assumptions in decisions or beliefs by classifying them into fact, convention, belief, or interest-driven, ranking by fragility and impact, then rebuilding conclusions from verified premises. Supports English and Chinese.

Use Apex enums for closed-set dispatch and routing, with safe valueOf conversion, exhaustiveness checks, and managed-package considerations.

Identify and test IAM misconfigurations that allow privilege escalation in AWS environments using Pacu, CloudFox, Principal Mapper, and manual analysis.

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md. Identifying inconsistencies, duplications, ambiguities, and underspecified items.

安室透（少年推理漫）认知与表达框架（压缩蒸馏）：三重身份张力、波本梗、服务生伪装 触发：名侦探柯南 等。虚构；禁止犯罪教唆

Apply Arrange-Act-Assert pattern for clear, maintainable tests with detailed guidance

Evaluates machine learning classifiers on their ability to distinguish signal from background in particle physics simulations, measuring how well algorithms rank signal events above background ones using the AUC metric.

Context (aaau-context)

Audits hidden assumptions in decisions by classifying them, ranking by fragility and impact, and rebuilding conclusions from verified premises. Auto-detects Chinese or English input.

`analysis-agent`/`task-agent`/`review-agent`: use for input authority, parsing, canonicalization, bounds, state checks, writable fields, rejection, or external-response changes.

Deepgram API Specification API skill. Use when working with Deepgram API Specification for agent, auth, listen. Covers 39 endpoints.

Classify AEM 6.5 LTS and AMS workflow issues by symptom, gather relevant logs and metrics, and map to runbooks or Splunk searches.

Bootstraps AGENTS.md and CLAUDE.md for AEM 6.5 LTS projects, tailored to project structure and detected add-ons.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Use when you need to annotate BAM alignments with a two-character tag based on overlaps with BED, GFF, or VCF annotation files, such as labeling reads by feature class or interval source.

Validate skill outputs against assertion manifests to detect regressions, enforce quality gates, and bootstrap starter manifests from historical runs.

Turn a new rep's start date into a ramp that ends in quota, not confusion. Builds a 30/60/90 onboarding plan with milestones at each gate, a certification bar they have to clear before they carry full load, and the manager touchpoints that catch a slow ramp early. Built for B2B sales managers and enablement, customizable to your role and motion. Trigger on "build an onboarding plan", "30/60/90 for a new AE", "how do I ramp this rep", "certification checklist", "onboarding milestones", or any new-hire ramp.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Identify underspecified areas in the current feature spec by asking targeted clarification questions and encoding answers back into the spec.

Generates four improved variations of any installed skill, scores them against a weighted rubric, and applies the winning version while preserving the original.

Use `analysis-agent` to map acceptance to validation, `task-agent` to add or run bounded tests, and `review-agent` to assess proof coverage. Skip work with no material change or already-fresh complete validation.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

娜美（少年漫）认知与表达框架（压缩蒸馏）：航海图天才、财迷喜剧壳、气候战 触发：海贼王 等。虚构

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.