TECHKNOWMAD LABS
Official@techknowmad-labs · Global / Remote-First
An AI/ML research lab focused on designing autonomous intelligence systems and applied workflows to automate, optimize, and scale internal business operations.
Agent Skills by TECHKNOWMAD LABS
Showing 26 vetted skills indexed across 1 GitHub repositories.
TECHKNOWMAD-LABSdev-lifecycle-engine
Automate a 7-phase software development lifecycle from brainstorming to merge.
TECHKNOWMAD-LABSdesign-system-forge
Generate design systems with accessibility validation and framework-aware output.
TECHKNOWMAD-LABSdiff-generator
Generate unified diffs between file snapshots and current states.
TECHKNOWMAD-LABSforum-intelligence
Analyze forum threads to detect coordination, extract arguments, and determine sentiment.
TECHKNOWMAD-LABSresearch-workflow
Guide AI/ML research from hypothesis generation to report generation.
TECHKNOWMAD-LABSskill-validator
Validate Claude Code skills for YAML frontmatter, structure, scripts, and markdown.
TECHKNOWMAD-LABSmindspider-connector
Extract trending topics, sentiment scores, and sample posts from MindSpider into Cortex evidence structures.
TECHKNOWMAD-LABSpersistent-memory
Capture observations, decisions, and errors across sessions with hybrid search.
TECHKNOWMAD-LABSsession-memory
Checkpoint session state into structured markdown files for resumption.
TECHKNOWMAD-LABSmlops-standards
Enforce production-grade MLOps standards for ML systems.
TECHKNOWMAD-LABSskill-test-harness
Run automated tests for skill scripts with fixtures and assertions.
TECHKNOWMAD-LABStdd-enforcer
Enforce TDD cycles and detect testing anti-patterns in Python test files.
TECHKNOWMAD-LABSde-slop
Detect AI-generated writing patterns in markdown files with regex analysis.
TECHKNOWMAD-LABSpre-package-pipeline
Validate, scan, and package skills into distributable .skill files.
TECHKNOWMAD-LABSmeta-skill-evolver
Orchestrates autonomous AI-skill lifecycle including generation, validation, testing, packaging, and recursive evolution.
TECHKNOWMAD-LABScontext-engineer
Manage LLM context windows with token budgeting, relevance scoring, and auto-pruning.
TECHKNOWMAD-LABSscenario-simulator
Generate diverse agent personas and run multi-round deliberation simulations for counterfactual analysis.
TECHKNOWMAD-LABSgithub-mcp
Build and deploy a GitHub MCP server using FastMCP for repository management.
TECHKNOWMAD-LABSsecurity-audit
Scan code repositories for vulnerabilities and leaked secrets using static analysis tools.
TECHKNOWMAD-LABSprompt-architect
Audit and improve prompts for AI/ML research, MLOps, and agentic systems.
TECHKNOWMAD-LABSagent-orchestrator
Orchestrate multi-agent workflows with a Directed Acyclic Graph for task dependencies.
TECHKNOWMAD-LABSagent-output-validator
Validates parallel AI agent outputs against contracts for paths, content and schema compliance.
TECHKNOWMAD-LABSmultimodal-analyst
Synthesizes insights from text, image URLs, and video URLs to flag hallucination risks.
TECHKNOWMAD-LABSintelligence-query
Decompose query topics into sub-queries and synthesize a JSON intelligence report.
Frequently Asked Questions About TECHKNOWMAD LABS
FAQPage SchemaWhat specific tasks can I perform using these capabilities?▼
You can manage the full lifecycle of intelligent systems, including recursive evolution, validation of YAML frontmatter, static security auditing, and multi-agent deliberation simulations. These capabilities enable rigorous quality control for research-driven development and repository management.
Which personas benefit most from these technical offerings?▼
These offerings are designed for research engineers, MLOps practitioners, and software architects focused on building robust, self-correcting systems. They provide the necessary infrastructure for teams requiring high-fidelity validation and structured state management in complex development environments.
What are the prerequisites for implementing these systems?▼
Implementation requires a environment capable of executing structured validation logic and managing repository-level dependencies. Users must ensure their environment supports YAML-based configuration and standard static analysis protocols to leverage the full suite of validation and orchestration features.