Results for “test”

AI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).

Put the user in the hot seat — one question at a time until their plan, decision, or idea actually holds up. Use whenever the user says "hot seat me", "grill me", "stress-test this", "poke holes in this", "am I missing anything", or drops a plan and wants it challenged before anyone acts on it — even if they never say the words. Also used as a sub-procedure by the whiteboard and connotation-cop skills.

Use for writing, reviewing, debugging, testing, or optimizing Python code that embeds DuckDB, executes analytical SQL, manages DuckDB connections and transactions, builds DuckDB relations, queries Parquet/CSV/Arrow/pandas/Polars inputs, or exports query results. Trigger on connection scope, parameters, replacement scans, materialization, concurrency, extensions, and query plans. Do not use for generic SQL with another engine, DuckDB CLI-only work, server-database administration, dbt-only projects, or dataframe work that does not call DuckDB.

Discovers the core attributes of the current code repository and its projects — languages, frameworks, tooling, and where things live — and writes a concise reference section directly into the project's AGENTS.md or CLAUDE.md for other skills, agents, and hooks to consume. Use when scanning, analyzing, or detecting the project's technology stack, build tools, or repository structure. Does not create or update project documentation — use project-documentation for writing feature or system docs.

Run a full pull request review and post review comments directly to the current branch's GitHub PR. Requires the gh CLI to be installed and a PR to already exist for the current branch. Use when you want review feedback posted to GitHub as PR comments. For local code review without posting to GitHub, use code-review instead. Does not write or update PR descriptions — use update-pr-description for that.

Use this skill when the user asks about FCRA adverse action procedures — what to do when a background check (MVR, criminal, drug test) returns information that may lead to denying employment or terminating a CDL driver. Covers the 5-business-day pre-adverse-action notice, FCRA Summary of Rights, final adverse action notice, state-specific add-ons (CA, NY, etc.), and how to handle disputes. Cite 15 USC § 1681 (FCRA) and 49 CFR 391.

Determines if primary aldosteronism is confirmed based on upright plasma aldosterone ≤6 ng/dL (170 nmol/L) on day 4 at 10 AM of fludrocortisone suppression test, provided plasma renin activity ≥1 ng/mL/h and plasma cortisol concentration lower than the 7 AM value to exclude ACTH confounding. Use when reviewing FST results to diagnose PA; triggers include "FST", "upright aldosterone ≤6 ng/dL", "PRA ≥1 ng/mL/h", and "cortisol drop".

Evaluate and improve interface usability using heuristic analysis. Use when the user mentions "usability audit", "UX review", "users are confused", "heuristic evaluation", "form usability", "navigation problems", "Nielsen heuristics", "cognitive walkthrough", or "usability testing". Also trigger when reviewing a design for usability issues, improving form completion rates, or evaluating information architecture and navigation. Covers Nielsens 10 heuristics, severity ratings, and information architecture. For visual design fixes, see refactoring-ui. For conversion-focused audits, see cro-methodology.

Orchestrate the full MoonSpec lifecycle from a preselected single-story feature request or active feature directory through specification, planning, TDD task generation, artifact alignment, implementation, and final verification. Use when the user asks for an end-to-end MoonSpec run and the input has already been routed to one independently testable story, or when Codex needs to coordinate `moonspec-specify`, `moonspec-plan`, `moonspec-tasks`, `moonspec-align`, `moonspec-implement`, and `moonspec-verify` without manual analyze/remediation prompts.

Verify an artifact that is about to leave the building — a partnership brief, pitch page, press release, customer email, RFP response, or any document sent to someone outside the org. Runs six checks before send — premise, confidentiality forward-test, evidence class, cold read by recipient personas, AI read, and tone metrics — and returns what to change with replacement copy. Use before sending anything outward-facing that carries customer detail, factual claims about a counterparty, or an ask.

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when the user has a plan or design doc and is about to start coding — to catch architecture issues before implementation. (gstack) Voice triggers (speech-to-text aliases): "tech review", "technical review", "plan engineering review".

Design automated evaluation pipelines for LLM and agent systems — combining deterministic checks, statistical metrics, and LLM-as-judge scoring into repeatable, CI-integrated eval suites. Load when the user asks to set up automated evals, design an eval pipeline, integrate evals into CI/CD, create an eval suite, do eval-driven development, or says "automate my evals", "CI eval integration", "evaluation pipeline", "continuous evaluation", "monitoring eval quality", "set up regression testing for my agent". Sub-skill of eval-output orchestrator.

Build empowered product teams using discovery and delivery dual-track. Use when the user mentions "product discovery", "empowered teams", "feature factory", "product roadmap", "opportunity assessment", "product vision", "product-led growth", or "discovery vs delivery". Also trigger when restructuring product teams away from output-driven models, setting product strategy, or defining what to build next based on outcomes. Covers product discovery techniques, team structure, and continuous value delivery. For customer interviews, see mom-test. For ongoing discovery systems, see continuous-discovery.

Humanize LLM output so it reads like a careful human wrote it. Subtracts AI-isms (sycophancy, tricolons, em-dash overuse, "delve"/"tapestry"/"testament", hedging stacks, tidy five-paragraph shapes), engineers burstiness and calibrated uncertainty, and preserves technical accuracy. Supports intensity levels: subtle, balanced (default), full, voice-match, anti-detector. Use when user says "humanize this", "make this sound human", "de-slop this", "rewrite without AI tone", "match my voice", "less robotic", or invokes /unslop. Also auto-triggers when text-quality is requested.

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

Deploy with unified build/test/deploy intent across providers using .agent-loom/deploy.yml and per-provider adapters. Load when the user asks to deploy, ship to preview, release to production, or run a provider-agnostic deploy flow. Also triggers on "deploy anywhere", "deploy to Vercel", "deploy with GitHub Actions", "preview deploy", or "ship this". Runs preflight before any deploy — stops on missing secrets. Pairs with ci-cd-and-automation for pipeline design. Ships vercel and github-actions adapters; extensible adapter interface.

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

Decide when and how to assess minimal residual disease (MRD) in multiple myeloma — bone marrow MRD by next-generation flow (NGF) or sequencing (NGS), plus imaging MRD by PET-CT — and how to act on the result. Trigger when a clinician asks "how to assess MRD in myeloma", "MRD-negative complete response", "PET-CT for MRD", "is bone marrow MRD enough", "when to test for MRD", "next-generation flow for myeloma", or "myeloma sustained MRD negativity". Based on EHA-ESMO 2021.

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", or "decompose conditional". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation to apply to a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

Map symbol dependencies, callers, and blast radius before editing code. Load when the user asks what depends on a symbol, what breaks if they change something, blast radius of a change, reverse dependencies, or which tests cover a function. Also triggers on "who calls this", "impact of changing", "dependency map", "what uses this", "find callers", or before any non-trivial edit when safe-change is not yet active. Pairs with codebase-understanding for broad architecture; this skill is symbol-scoped and edit-gated.

Senior SecOps engineer skill for application security, vulnerability management, compliance verification, and secure development practices. Runs SAST/DAST scans, generates CVE remediation plans, checks dependency vulnerabilities, creates security policies, enforces secure coding patterns, and automates compliance checks against SOC2, PCI-DSS, HIPAA, and GDPR. Use when conducting a security review or audit, responding to a CVE or security incident, hardening infrastructure, implementing authentication or secrets management, running penetration test prep, checking OWASP Top 10 exposure, or enforcing security controls in CI/CD pipelines.

Reviews pull requests and local diffs against Google's eng-practices code review guide (design, complexity, tests, naming, comments, style, consistency, documentation, every-line, context) and emits a ranked, principle-tagged finding list with rich local diff rendering. Optional follow-up step posts selected findings as a GitHub PR review. Invoke when the user says "googly-eyes this PR", "review by the canon", "principled code review", "Google-style review", "review this CL", or pastes a PR URL and asks for a code review. 👀

Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.

Use for writing, reviewing, debugging, testing, or optimizing Python code that inspects, edits, extracts, validates, preserves, or generates Microsoft PowerPoint Open XML presentations, primarily .pptx, using python-pptx, PresentationML/OOXML, Pillow, or supporting Python libraries. Trigger on slides, masters, layouts, placeholders, shapes, text, pictures, tables, charts, notes, themes, hyperlinks, embedded objects, macros, preservation, geometry, rendering verification, and presentation package inspection. Do not use for .ppt binary files, PowerPoint UI automation, VBA execution, slideshow execution, or presentation advice with no Python or file boundary.

The two model-touching halves of Worklink, shipped as one opt-in skill. (1) Planner: decompose a parent Chainlink issue into testable worklink:ready leaf subissues (mutates Chainlink only, never executes). (2) Ready-queue poller: discovers worklink:ready leaves and dispatches them by invoking `mimir worklink run` as a detached subprocess, up to the concurrent-claim cap — it never reimplements claim/evidence/transition. Opt-in (mimirbot yes, muninn no): `mimir skills install chainlink-orchestrator`, then set the env below to enable autonomous dispatch.

Install, configure, and use the Unity Command Line Interface (CLI) for automated production workflows, project management, and cloud integration. Use when setting up CLI-based build automation, CI/CD pipelines, Editor/module management, authentication, or localhost API calls via the experimental Unity Pipeline package. Designed for verifiable, machine-readable game production workflows where the build machine must be describable and tests must return evidence. Triggers on: Unity CLI, unity command line, unity automation, unity ci/cd, unity build script, unity pipeline, unity production workflows.

Improve agent reliability over time — diagnose why agents fail and fix the setup. Triggers on: agent keeps failing, same mistake again, agent not improving, make agent smarter, agent quality plateau, agents ignore skills, agent skips tests, fix agent behavior, agent unreliable, improve agent setup, self-improving harness, agents worse over time, tune agent instructions, agent going in circles, agent ignores AGENTS.md, repeated agent errors. Requires harness v0 and eval harness. AUTO-ROUTED from harness-engineering on symptoms. Not first setup — harness-generation first.

autonomous architecture design and refinement for mermate using iterative copilot guidance, local reasoning, repeated low-cost render validation, and final max-quality render selection. use when building, stress-testing, refining, decomposing, validating, or evolving system architectures from simple ideas, complex problem statements, markdown specifications, mermaid drafts, or ambiguous design notes. especially useful when chatgpt should act like a professional architect that thinks step by step, uses mermate repeatedly, compares intermediate diagrams, and decides when to continue refining versus when to finalize with max mode.

Triage a raw, vague issue or bug report into a structured document that names what is known, what is missing, and what to do next. Use when an incoming issue, bug report, or problem description is too vague or incomplete for investigation or planning, and recommend the right next han skill. Does not investigate root causes or trace code paths — use investigate for debugging, diagnosis, and root cause analysis. Does not plan features or build solutions — use plan-a-feature or plan-implementation for that.

High-converting web design techniques that dazzle and persuade. Covers social proof design, pricing page architecture, CTA optimization, scroll-driven persuasion, interactive demos, trust signals, and the visual craft behind sites that sell. Activate on 'sales page design', 'conversion design', 'high-converting', 'landing page that sells', 'pricing page design', 'social proof', 'dazzle', 'wow factor', 'persuasive design', 'conversion rate', 'make it sell'. NOT for copywriting (use marketing copywriting skills), not for A/B testing setup (use frontend-developer), not for paid ad creative (use marketing skills).

Cloud security posture assessment for AWS, Azure, and GCP. Tests IAM privilege escalation paths, public storage exposure, serverless attack surface, database exposure, logging gaps, container registry security, and cloud-specific attacks. Both authenticated (with cloud credentials) and unauthenticated (external) modes. Uses nuclei cloud templates, Prowler, ScoutSuite, manual IMDS/metadata probing, and deep AWS/Azure/GCP CLI enumeration. Produces: cloud architecture diagram, attack path map, findings per category, compliance mapping (SOC 2, PCI DSS 4.0, HIPAA, CIS), PoCs for confirmed exploits. Chains into /gh-export for issue filing.

Apply one logical code change with mandatory impact mapping, git snapshot, verification, and automatic revert on failure. Load when the user asks for a safe edit, verified change, one change at a time with rollback, or codespine-style edit loop. Also triggers on "safe change", "edit with verification", "don't break the build", "revert if tests fail", or any non-trivial code edit where blast radius matters. Always runs dependency-mapping first. Pairs with incremental-implementation for multi-slice work; this skill is one atomic verify cycle per invocation.

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.

Set up and verify a connection to an NI USRP radio (USRP E320, N300, N310, N320, N321, X300, X310, or X410) using Wireless Testbench. Use when connecting a USRP for the first time, configuring radio hardware, troubleshooting connection failures, or verifying a radio setup. Covers host inspection (OS, NIC type/speed/MTU), device discovery (findsdru, probesdru), UHD version checking, programmatic radio configuration, and basebandTransceiver verification. Also use when the user mentions USRP setup, radio not found, connection errors, dropped samples, or network configuration for SDR hardware.