Search Results for "robustness" | AgentCC

backtest-expert

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

pymupdf-pdf

Fast local PDF parsing with PyMuPDF (fitz) for Markdown/JSON outputs and optional images/tables. Use when speed matters more than robustness, or as a fallback while heavier parsers are unavailable. Default to single-PDF parsing with per-document output folders.

strategy-stress-testing-skill

Strategy robustness testing, scenario-based evaluation, vulnerability identification, and adaptation planning

aiml-security

AI/ML model security testing and adversarial research capabilities. Generate adversarial examples, test model robustness, perform model extraction attacks, test for data poisoning, analyze model fairness, and support ART framework integration.

quantitative-methods

Design and execute statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python

code-refinement

Analyze and improve living code quality: duplication, algorithmic efficiency, clean code principles, architectural fit, anti-slop patterns, and error handling robustness. Use when improving code quality, reducing AI slop, refactoring for clarity, optimizing algorithms, applying clean code principles. Do not use when removing dead/unused code (use conserve:bloat-detector). reviewing for bugs (use pensive:bug-review). selecting architecture paradigms (use archetypes skills). This skill actively improves living code, complementing bloat detection (dead code removal) with quality refinement (living code improvement).

refactoringclean-codealgorithms+3

safety-critical-patterns

Guidelines from the NASA Power of 10 rules for writing robust, verifiable code. Adapted for general software development with context-appropriate rigor. Use when writing critical code paths, reviewing for robustness, improving code quality. Do not use as rigid requirements for all code - match rigor to consequence.

safetydefensive-codingassertions+3

skill-validator

Validates skills against production-level criteria with 9-category scoring. This skill should be used when reviewing, auditing, or improving skills to ensure quality standards. Evaluates structure, content, user interaction, documentation, domain standards, technical robustness, maintainability, zero-shot implementation, and reusability. Returns actionable validation report with scores and improvement recommendations.

cc-defensive-programming

Review error handling, input validation, and exception patterns using 24-item checklist. Use when auditing defensive code, designing barricades, choosing assertion vs error handling, or deciding correctness vs robustness strategy. Triggers on: empty catch blocks, missing input validation, assertions with side effects, wrong exception abstraction level, garbage in garbage out mentality, deadline pressure to skip validation, trusted source rationalization. Produce status table with VIOLATION/WARNING/PASS per item, or barricade/error-handling design recommendations.

Angry User Simulator

Simulate aggressive user behavior patterns including rapid clicking, random navigation, form abuse, tab spamming, and unexpected interaction sequences to find UI resilience issues

chaos-testinguser-simulationstress-testing+4

Negative Test Generator

Systematically generate negative test cases covering invalid inputs, unauthorized actions, missing required fields, exceeded limits, and malformed request payloads

negative-testingerror-handlinginvalid-input+4

tradeblocks-wfa

Walk-forward analysis for trading strategies. Tests whether optimized parameters hold up on out-of-sample data. Use when checking parameter robustness, detecting potential overfitting, or validating a backtest.

design-of-experiments

Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.

postel

Be conservative in what you send, liberal in what you accept.

moollmrobustnessinterpretation+2

engineer-analyst

Analyzes technical systems and problems through an engineering lens using first principles, systems thinking, design methodologies, and optimization frameworks. Provides insights on feasibility, performance, reliability, scalability, and trade-offs. Use when: System design, technical feasibility, optimization, failure analysis, performance issues. Evaluates: Requirements, constraints, trade-offs, efficiency, robustness, maintainability.

speckit.quizme

Challenge the specification with Socratic questioning to identify logical gaps, unhandled edge cases, and robustness issues.

unix-review

Evaluate codebases against Eric Raymond's 17 Unix rules and SOLID principles, with a novel AI-Readiness dimension. Performs dimensional scoring across Modularity, Composability, Clarity, Simplicity, Robustness, Data-Drivenness, Extensibility, and AI-Readiness. Use for architecture reviews, codebase audits, or assessing how well code follows time-tested engineering principles.

Test Coverage Guardian

Analyze test coverage, identify gaps, detect dead code, and improve test quality. Use when user asks to check coverage, review tests, find untested code, or improve test robustness.

structural-stability

Robustness of qualitative dynamics under perturbation

stress-test

Adversarially test a thesis or take for differentiation, robustness, and blind spots. Prevents publishing consensus as contrarian and surfaces what would change your mind.

validating-backtests

Validate trading strategies with walk-forward analysis, Monte Carlo simulation, and robustness checks

pt-fuzzing-binary-protocol

Performs authorized fuzz testing of binary formats and network protocols to uncover parser vulnerabilities, memory safety defects, and denial-of-service conditions. Use when assessing protocol handlers, file parsers, and service robustness against malformed inputs.

robustness-checks

Sequential robustness checks with confounder blocks in Stata. Use for sensitivity analysis showing how estimates change as potential confounders are added to regression models.

skill-validator

Validate skills against production-level criteria. Use when reviewing, auditing, or improving skills to ensure they meet quality standards. Evaluates structure, content quality, user interaction patterns, documentation completeness, domain standards compliance, and technical robustness. Returns actionable validation report with scores and improvement recommendations.

skill-audit

Audit Agent Skills for quality, compliance, and best practices. Evaluates skills against the Agent Skills specification across 6 categories: Specification Compliance, Instruction Quality, Tool & Integration Design, Context Efficiency, Safety & Robustness, and Formatting & Syntax. Produces scored reports with evidence-backed findings. Use when the user asks to audit, evaluate, review, or check skill quality.

architecture-audit

Perform comprehensive software architecture audits to identify improvements for robustness, changeability, and testability. Use when asked to audit, review, or analyze a codebase's architecture, or when asked to suggest structural improvements. Triggers include "architecture audit", "audit the codebase", "review architecture", "architectural analysis", "code structure review", or explicit "/architecture-audit".

evaluating-machine-learning-models

This skill allows the AI assistant to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. The AI assistant can use this skill to assess model accuracy, precision, recall, F1, AUC, confusion matrices, calibration, robustness, fairness measures, and other relevant metrics. Use when appropriate context is detected. Trigger with relevant phrases based on the skill's purpose.

clean-architecture-review

Review a codebase, PR, or module for clean architecture quality and production robustness. Use when Codex needs to detect cross-layer business logic mixing (domain/application/infrastructure/interface boundary violations), dependency direction leaks, SOLID principle problems, and KISS/over-engineering issues, then report findings prioritized with SRE-style severity levels (P0-P3) so users/agents can improve code quality for real-world production operation.