Search Results for "evidence"
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Showing 15 open source projects for "evidence"
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1
geo-sleuth
An agent skill that finds where a photo was taken
...Specialized Python scripts collect, score, and rank geographic candidates before the AI agent evaluates the strongest matches. Results include estimated coordinates, camera direction, confidence levels, and supporting visual evidence. Geo-Sleuth integrates with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and GitHub Copilot. -
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REA: Reverse Engineer Anything
Reverse engineer anything with agents, from app behavior
REA (Reverse Engineer Anything) is an open-source, AI-powered reverse engineering toolkit that enables developers and AI agents to analyze software applications, native binaries, and runtime behavior. The platform uses the Model Context Protocol (MCP) to connect AI coding assistants with tools for inspecting compiled code, application structures, and software functionality. It supports reverse engineering of native executables, JavaScript and Electron applications, .NET assemblies, Android... -
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CyberPPT
A Codex Skill for generating high-density, editable PowerPoints
CyberPPT is a Codex Skill for converting documents, research, and business data into dense, editable, consulting-style PowerPoint presentations. It extracts facts, numbers, claims, recommendations, conflicts, and caveats from formats such as PDF, DOCX, TXT, and XLSX. The workflow builds an MBB-style evidence table before developing storylines and converging on an SCR narrative. Users select from eight fixed visual systems, after which the skill plans each slide’s hierarchy, grid, charts, palette, and information density. Image-generation blueprints guide reconstruction while native text, shapes, tables, charts, and vectors preserve core editability. ... -
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VulnClaw
Based on AI Agent + MCP toolchain + penetration Skill orchestration
...Its newer architecture uses a goal-driven solving engine instead of a fixed-round loop, helping the agent stop when the goal is reached, the search space is exhausted, or a safety budget is met. It also includes evidence checks designed to reduce hallucinated conclusions by requiring real tool output before accepting key findings. VulnClaw is intended for authorized testing, CTFs, security education, and controlled red-team environments. -
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Hiring Agent
AI agent to evaluate and score resumes
...The system can enrich that resume data with GitHub profile and repository signals when a profile is available. After the data is collected, it produces an explainable evaluation with category scores, supporting evidence, bonus points, and deductions. It can run locally with Ollama or use Google Gemini, which makes it flexible for teams that want either private local processing or hosted model access. -
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AutoCVE
Agent-driven automated CVE discovery platform for source code auditing
...Users can choose enhanced scanning, intelligent auditing, or comprehensive auditing based on speed and depth requirements. The interface records agent trees, tool calls, phase progress, sessions, findings, and supporting evidence for review. Vulnerabilities are deduplicated and stored in a structured management system, while configurable skills extend individual agents. The platform uses a React frontend, FastAPI backend, PostgreSQL database, and Docker-based deployment. -
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Loop Library
A library of practical AI-agent loops and an installable skill
...The repository contains both a public loop library website and the Loopy skill that agents can use to discover, audit, adapt, run, and prepare loops. It helps turn open-ended prompts into measurable workflows with evidence, stopping criteria, and human approval points. The skill can work with agent environments such as Codex, Cursor, and Claude Code through the Skills CLI. Overall, it is useful for teams that want safer, reusable, and more reliable agent workflows instead of one-off prompting. -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
PokeeResearchOSS provides an open-source, agentic “deep research” model centered on a 7B backbone that can browse, read, and synthesize current information from the web. Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. ... -
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TencentDB Agent Memory
TencentDB Agent Memory delivers fully local long-term memory for AI
...For personalization, it organizes memory from raw conversations into atoms, scenarios, and persona-level knowledge. The design keeps high-level memory inspectable while preserving a drill-down path back to raw evidence. It is built for OpenClaw and Hermes-style agent workflows that need lower token usage, better continuity, and no external API dependency. -
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10
Oh My Hermes
All in one plugin for Hermes Agent
Oh My Hermes is an operating layer for Hermes Agent that adds structured routing, coding workflows, long-term memory, and evidence-aware execution. It keeps Hermes as the conversational front end while deciding which workflow, model category, and specialist capability should handle each task. Work can be split into parallel lanes with isolated worktrees and typed completion states. The plugin includes a reviewed memory system that stores approved records with provenance and aging rules. ... -
11
Harness2
Recursive Agent Harnessing for an Open World
...Rather than modifying model weights, it refines the prompts, tools, memory, guardrails, and supporting components that determine agent behavior. Its task-level optimization follows a Propose–Probe–Compose process that tests alternative configurations and combines evidence from their performance. A second optimization level preserves effective configurations and lessons across successive tasks. The framework supports parallel exploration and sequential refinement without requiring ground-truth feedback. Researchers can experiment with OpenCode and Codex execution environments. It also integrates professional benchmarks for evaluating agent performance and studying improvements across different models and task categories. -
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Sprix SAGE Router
State-aware SELF/COLLABORATE/HANDOFF routing for A2A agent networks
...The system assigns remaining requirements to agents through task dependency graphs and estimates execution schedules. It learns contextual reliability and outcome information from previous execution evidence instead of relying on a single reputation score. Bounded search and replanning allow the router to compare feasible teams and revise decisions as task conditions change. -
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Harness Engineering
Field guide, and agent context bundle for harness engineering
...It treats the model and agent as fixed while strengthening the surrounding context, tools, constraints, and proof mechanisms. The repository organizes developed arguments, practical cases, source evidence, evaluations, and reusable playbooks into distinct layers. Its agent guide routes each task to the smallest relevant set of materials instead of loading the entire corpus. The playbooks help teams improve one representative workflow, review repository readiness, and compare changes over time. The project emphasizes reliability, security, maintainability, compatibility, authority, and cumulative organizational learning. ... -
14
Cangjie Skill
Distill high-value content like books, long videos, podcasts, and more
...Each accepted skill records supporting material, a reconstructed explanation, examples, trigger situations, executable steps, and limitations. Strict verification rejects generic or weak ideas that lack independent evidence, predictive value, or meaningful uniqueness. The generated package can include an overview, glossary, reference graph, long-form digest, individual skill files, and test prompts. Completed skills can be installed for compatible coding agents such as Claude Code and Cursor. -
15
Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
...The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. Its architecture highlights agent loops, tool calling, and stepwise execution, which are increasingly important patterns in modern AI automation. Overall, the demo serves as a practical reference for developers exploring autonomous research agents and multi-tool LLM orchestration.
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