tekhne

Create publication-quality scientific diagrams using Claude AI with smart iterative refinement. Uses Claude for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

Search and download academic papers through Sci-Hub by DOI, title, or keyword. Supports PDF download, metadata extraction, and automatic mirror detection via CrossRef integration. Use when a paper is behind a paywall and you have the DOI or title. Triggers: sci-hub, download paper, fetch paper, academic paper download, paper by DOI, paper access, paywall bypass, retrieve paper, get full text, paper download.

Search Semantic Scholar for academic papers, fetch paper details by ID or DOI, retrieve author profiles, and analyse citation graphs. Preferred over google-scholar-search — uses the official API with no scraping. Triggers: search papers, find papers, semantic scholar, paper details, paper by DOI, paper by ID, author profile, citation analysis, references, literature search, paper discovery.

Conduct a two-question session-end reflection to catch blind spots and under-investigated areas before concluding. The agent surfaces its lowest-confidence work items and identifies what the user might be missing, then offers to investigate. Based on a Reddit-post technique combining an LLM-suggested confidence audit with Sam Altman's blind-spot question. Do NOT trigger for brief single-answer queries, CI contexts, or in the middle of active work -- only when a session appears to be concluding. Triggers: 'wrap up', 'we're done', 'conclude', 'session end', 'final review', 'before we go', 'sign off', 'that's all', 'anything else', 'finished', 'reflection', 'confidence check', 'blind spot', 'what are you missing', 'rate your confidence', 'review the session'.

Use when designing solutions, adding features, or refactoring by applying KISS, YAGNI, and Principle of Least Astonishment to write simple, predictable code.

Refine vague, complex, or high-stakes prompts through Socratic dialogue — surfaces hidden assumptions, probes reasoning, and iterates toward clarity before committing to an implementation.

Apply SOLID principles (SRP, OCP, LSP, ISP, DIP) for tactical class design, method extraction, and interface definition. Use when reviewing class design, refactoring code for maintainability, or designing interfaces and abstractions.

Use when structuring Standard Operating Procedures with proper sections, organization, and markdown formatting. Covers SOP anatomy and section organization.

Review a saved standards document via a one-standard-at-a-time interview. Prompts the user for the file path, parses individual standards, then presents each one for evaluation with concrete options (Accept / Revise / Reject / Defer). Produces a summary report at the end.

Translates project coding standards into concrete linting and formatting tool configurations. Given human-readable conventions (from AGENTS.md, code review guidelines, or team standards), this skill produces ESLint, Prettier, Biome, TypeScript, and other tool configs that enforce them automatically. Covers discovery, mapping, implementation, verification, and CI integration.

Set up a new Astro Starlight documentation site from scratch. Use when creating a new Starlight project, adding Starlight to an existing Astro project, or configuring core options like sidebar, logo, social links, and page layout.

Create and override Astro Starlight UI components. Use when replacing or extending built-in Starlight components (header, footer, sidebar, page title, etc.), adding custom UI alongside existing components, accessing page route data inside a component, or applying overrides conditionally on specific pages.

Create and apply custom themes to an Astro Starlight documentation site. Use when customizing colors, typography, or spacing via CSS custom properties, using the Tailwind CSS integration, applying light/dark mode variants, or loading custom fonts.

Maintain docs/TECH_DEBT.md, the living list of code-level cleanup that isn't a risk, a shortcut, or an open decision (that's the risk-register skill's job) and isn't a future feature (that's ROADMAP.md's job). Use when a lint/format/style violation slips past an existing gate, a small known cleanup is noticed while working on something else, or a filed item is fixed and needs removing. DO NOT use for anything with a real cost or risk if left unaddressed (use risk-register instead), for aislop baseline findings (already lifecycle-managed in .aislop/baseline.json), or for anything confidential. Triggers: 'add to tech debt', 'log this as cleanup', 'track this lint violation', 'mark tech-debt item #N fixed', 'what's on the tech debt list'.

Investigate a candidate library, dependency, or file format against a fixed question set and end with exactly one cited recommendation (adopt / keep_current / reject / needs_more_research) -- never "it depends." Validates the structured output against a JSON schema before writing a human-readable finding. Use when choosing between candidate npm packages, dependencies, or file formats for a specific integration point, or when a plan or ADR decision hinges on an unverified factual claim about a library (does it escape by default, does it bundle cleanly, is it maintained). Do NOT use for one-line version lookups, purely subjective preference calls with no verifiable claim, or a technology choice already settled by an existing ADR -- supersede the ADR instead. Triggers: "evaluate this library", "investigate this dependency", "compare X vs Y", "properly evaluate this technology", "which format should we use", "is this package maintained".

Generate Terraform modules, configure providers, define variables and outputs, set up remote state backends, and write production-ready HCL (.tf files) following current standards. Use when creating new Terraform resources, building Terraform projects, writing infrastructure as code (IaC), scaffolding AWS/Azure/GCP cloud infrastructure, working with terraform plan/apply workflows, or structuring multi-environment configurations.

Comprehensive toolkit for validating, linting, testing, and automating Terraform configurations and HCL files. Use this skill when working with Terraform files (.tf, .tfvars), validating infrastructure-as-code, debugging Terraform configurations, performing dry-run testing with terraform plan, or working with custom providers and modules.

Comprehensive toolkit for generating best-practice Terragrunt configurations (HCL files) following current standards and conventions. Generates terragrunt.hcl files, root configurations, child modules, stacks, and environment setups; configures remote state backends, dependency blocks, include blocks, feature flags, exclude blocks, and errors blocks; supports DRY Terraform patterns, multi-environment layouts (dev/staging/prod), and OpenTofu engine integration. Use when creating new Terragrunt projects or resources, scaffolding multi-environment infrastructure, implementing DRY Terraform wrapper configurations, setting up terragrunt.hcl files with remote state or provider config, managing module dependencies, or building infrastructure modules with Terragrunt stacks.

Comprehensive toolkit for validating, linting, testing, and automating Terragrunt configurations, HCL files, and Stacks. Use this skill when working with Terragrunt files (.hcl, terragrunt.hcl, terragrunt.stack.hcl), validating infrastructure-as-code, debugging Terragrunt configurations, performing dry-run testing with terragrunt plan, working with Terragrunt Stacks, or working with custom providers and modules.

Design code for testability using boundary isolation, dependency injection, and testable architecture patterns. Use when designing test strategies, writing unit tests, improving test coverage, refactoring untestable or hard-to-test code, working with mocking or test doubles, or applying TDD practices.