Results for “spearman-rank”

Evaluates how well internal model representations (hidden states) predict token-level information importance in summarization tasks, using NDCG@10 and Spearman's rank correlation.

Evaluates the correlation between a zero-cost NAS metric (epsilon) and actual training accuracy across different neural architecture search spaces, testing the metric's ability to rank architectures without training. It probes whether output dispersion from constant weight initializations can serve as a reliable.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

OWASP Top 10 + Supabase-first hardening burndown. Use when "security audit plan", "OWASP audit", "hardening plan", or "security burndown". App-layer auth flows → audit-auth-flows. Table RLS → plan-rls-audit. Key rotation → plan-secrets-audit. App LLM attacks → audit-llm-security.

Recommend the right AI model for a task by scoring candidates across six dimensions (Reasoning, Engineering, Speed, Breadth, Reliability, Governance) and displaying a spider-chart profile.

Screens resumes with skill matching, experience evaluation, and bias-free candidate ranking

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Evaluates the robustness and alignment with human judgment of reference-based and reference-free evaluation metrics for machine translation and summarization, particularly under adversarial conditions.

Evaluate everything the PA agent manages — tasks, skills, PA network health, billing, calendar connections, and memory quality. Use when: owner asks for an evaluation, wants to know what's working and what isn't, or requests a performance report. Combines supervisor status with quality scoring.

Agent Roster

针对 Modular RAG MCP Server 项目的老师式复习 Agent。按章节带领用户系统复习项目知识点，每道题互动问答、给出参考答案，复习结束后记录掌握进度，每次开始时回顾上次进度并建议继续或复习。Use when user says '复习项目', '帮我复习', '带我复习', '开始复习', '项目复习', 'review project', 'study review', '学习复习', '复盘', or wants to systematically review and study the project.

针对 Modular RAG MCP Server 项目的老师式复习 Agent。按章节带领用户系统复习项目知识点，每道题互动问答、给出参考答案，复习结束后记录掌握进度，每次开始时回顾上次进度并建议继续或复习。Use when user says '复习项目', '帮我复习', '带我复习', '开始复习', '项目复习', 'review project', 'study review', '学习复习', '复盘', or wants to systematically review and study the project.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Evaluates image captions by converting them into scene graphs and computing an F-score over semantic propositions, measuring how well a generated caption captures the meaning of an image compared to human references.

Sets up and audits the Claude Code harness for a project: CLAUDE.md, .claude/rules, skills, subagents, settings.json permissions, hooks, verification loop. Commands: kickoff (scaffold new setup), check-playbook (score an existing one), scouting-report (last scorecard), adjust (fix bloat/misplaced instructions), drill (turn advisory prose into real hooks/permissions/CI), decline (record an accepted risk), gameplan (work order with acceptance criteria before building), watch-film (check a diff against that order for scope creep/weakened tests/false claims). Use for setting up Claude Code, or on: "Claude ignores my CLAUDE.md", "it's huge and still misses things", "it said done but ran nothing", "it changed files I didn't ask about", "it weakened a test to pass", "rule, skill, or hook?", "is my setup any good". Not for code quality (code-audit), test coverage (test-assessment), one-off prompt wording (genie-proof-prompts), new skill authoring (skill-creator), or compacting a conversation (handoff).

Computes the SQuAD metric using torchmetrics, given predictions and ground truth. Use when evaluating question-answering outputs with exact match and F1 scores.