Results for “eval-chains”

16 skills

AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for

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aniruddhaadak80

Read-only EVM client: wallets, tokens, gas across 8 chains.

Read-only EVM client: wallets, tokens, gas across 8 chains.

projectious-work

eval-gate-authoring

Turn observed run outputs into eval-spec Artifacts, paired Gates, and policy bindings. Use when creating or calibrating automated, human, or LLM-as-judge eval gates for processkit workflows.

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Three-phase vulnerability chain analysis — parallel agents find individual weaknesses, then a synthesis step identifies which combinations escalate to critical impact. Inspired by Strix (usestrix/strix) "Graph of Agents" pentesting model.

Launches long-running evaluation batches in isolated tmux sessions with pre-flight verification, monitoring, and post-flight analysis for unattended runs.

netanel-abergel

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.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Launches a model evaluation batch with parameter collection, pre-flight checks, execution, and post-run analysis for interactive or foreground runs.

Benchmarks long-context language models across 20 sub-tasks spanning 3k–200k tokens, covering retrieval, reasoning, summarization, and instruction understanding, with exact-match accuracy as the primary metric.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

sakamoto-family-smile

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles.

qfc-openclaw-skill

Interact with the QFC blockchain: manage wallets, query chains, stake, deploy contracts, handle ERC-20/NFT tokens, swap on DEX, and run AI inference.

edge-signal-aggregator

Aggregate and rank signals from multiple edge-finding skills into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.