Claude Skills by Jamie-BitFlight

This skill should be used when the user asks to "test bash script", "write shell tests", "use shunit2", "use shellspec", "create test suite for bash", or mentions unit testing, test frameworks, mocking, or test-driven development for shell scripts.

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using research-validated prompt patterns.

Configure clang-format code formatting. Use when: user mentions clang-format or .clang-format, analyzing code style/patterns, creating/modifying formatting config, troubleshooting formatting, brace styles/indentation/spacing/alignment/pointer alignment, or codifying conventions.

When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.

When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.

Use when querying, modifying, or converting JSON, YAML, TOML, XML, CSV, HCL, or INI with dasel v3. Complete reference for selectors, functions, conditionals, variables, spread operator, type casting, and format-specific patterns.

Use when exploring unknown structured data files with dasel v3 — discover schema, list keys, find nested values, sample arrays, identify data types across JSON, YAML, TOML, XML, CSV, HCL, INI formats

Use when modifying, converting, or transforming structured data with dasel v3 — in-place mutations, format conversion, batch operations, array manipulation, object construction, and merge patterns across JSON, YAML, TOML, XML, CSV, HCL, INI

Dasel v3 query patterns for Hibernate .hbm.xml mapping files — entity-table binding, Java property-to-column extraction, one-to-many set/list/bag relationship tracing, many-to-one foreign key discovery, batch scanning across 60+ HBM files. Use when querying Hibernate ORM class mappings, extracting schema metadata from Java persistence layer, or auditing entity-column relationships in enterprise legacy codebases.

Dasel v3 query patterns for InstallAnywhere .iap_xml installer definitions — use when querying action sequences, discovering variables, resolving platform conditions, navigating panels, or comparing installer variants. Files are 2.5+ MB, 65,000+ lines — too large for context reads, requires structural dasel queries.

Dasel v3 selector patterns for Maven POM XML files — use when querying dependency versions, filtering by groupId or scope, extracting module hierarchy from parent POMs, or detecting version conflicts across enterprise multi-module Java projects. Load this skill when working with pom.xml files using dasel.

Dasel v3 selectors for Spring bean factory XML — use when querying any Spring ApplicationContext XML for bean discovery, dependency wiring, JMS destination mapping, property injection extraction, or cross-bean reference tracing. Load this skill before writing dasel selectors against Spring bean XML files (applicationContext.xml, *_beans.xml, spring-*.xml).

Dasel v3 patterns for querying Tomcat web.xml deployment descriptors — use when inspecting servlet enumeration, filter chain discovery, listener listing, context parameter extraction, or init-param inspection in web.xml files

Use when installing, updating, or troubleshooting the dasel v3 binary — runs the install script, verifies installation, and diagnoses PATH and download issues

SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.

Use when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.

Fetch and report current API syntax, changelog entries, and breaking changes for a specific library or protocol version. One research angle within a parallel technical-research set — runs independently and returns a structured cited report. Invoke when a specific library name and version are the target.

Use when orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task prompts, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

Use when a task asks for architecture review, dependency graph visualization, module coupling analysis, or circular dependency detection. Auto-detects scope (git diff → PR diff → full project). Reads project config (pyproject.toml, tsconfig.json, go.mod, Cargo.toml) to establish the intra-project module namespace before parsing imports. Builds a module-dependency graph across Python, TypeScript, JavaScript, Go, Rust, and Java. Detects cycles via graphify output or an executable Python script....

Loaded automatically when reviewing Claude skills or agent definitions — covers SKILL.md structure, frontmatter validity, token budget, description quality, and agent contract compliance.

Reviews CLI application code for correctness and quality. Use when reviewing tools that use argparse, click, typer, commander.js, or similar argument parsers — covers exit codes, help flags, stdin/stdout/stderr separation, non-interactive operation, signal handling, argument validation, ANSI color safety, and dry-run support for destructive operations.

Use when reviewing AI/ML code or LLM integration — activates on prompt templates, model selection logic, token budget concerns, or evaluation harness code. Enforces prompt hygiene, model tier matching, context window management, token economics, structured output validation, temperature settings, retry logic, streaming error handling, and PII/safety rules.

Applies Node.js-specific code review patterns for async I/O, streams, security, process management, and dependency hygiene. Use when reviewing Node.js server code, route handlers, middleware, or any JavaScript file alongside package.json without TypeScript. Triggers on sync I/O in request paths, missing stream backpressure, process.exit misuse, eval/exec injection risks, wildcard version ranges, missing lockfiles, EventEmitter cleanup gaps, and unvalidated environment variables at startup.

Provides Python-specific code review rules for the dh code-reviewer agent. Activates on pyproject.toml or *.py file detection — enforces uv, ruff, ty, pytest, type annotation, error handling, and Python 3.11+ idioms including pathlib, match statements, and modern union syntax.

Provides TypeScript-specific code review patterns covering strict mode, ESM, type safety, branded types, discriminated unions, async patterns, runtime safety, and common anti-patterns. Activates on detection of tsconfig.json, *.ts, or *.tsx files during code review — loaded automatically by dh:code-reviewer.

Use when reviewing web frontend code — HTML, CSS, JSX, or browser-targeted JavaScript. Enforces accessibility (WCAG AA, aria labels, focus management), XSS prevention (innerHTML, dangerouslySetInnerHTML), performance (layout thrash, CLS, lazy loading), CSS design tokens, form labeling, and event listener cleanup. Loaded by dh:code-reviewer on *.html, *.css, *.jsx detection.

Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.

Execute large-scale automated code transformations safely and idempotently. Use when renaming symbols across a codebase, migrating API call-sites, enforcing new patterns at scale, or applying structural edits to many files at once. Triggers on: 'codemod', 'mass rename', 'migrate all usages', 'transform codebase', 'apply pattern at scale', AST-based refactoring, or any task requiring consistent edits across 10 or more files.

Use when all tasks for a feature are marked COMPLETE — runs holistic quality gates including code review, feature verification, integration check, documentation drift audit and update, and context refinement. Creates follow-up plans when issues are found.

Close a completed GitHub milestone. Args: {milestone-number}. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the GitHub milestone, updates Project V2 Status to Done for closed issues, and generates a completion summary. Use when a sprint or release is finished and needs to be officially closed.

Use when reviewing test suites for coverage, isolation, mock usage, naming conventions, or completeness. Activates on "review test coverage", "audit test quality", or "check tests for completeness" requests. Performs thorough checklist-driven review covering test isolation, mock correctness, AAA pattern adherence, and naming standards.

Use when the PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.

Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact content".

Creates a new backlog item and routes through the work-backlog-item create workflow. Use when the user asks to add a backlog item, log a task, capture a feature request, or track a work item.

Routes to the correct dh skill entry point by intent. Use when unsure which development-harness skill to invoke, starting a development workflow, or invoking /dh directly. Covers capture, groom, plan, execute, single task, quality gates, and milestone routing.

One-line definitions of development-harness (dh) plugin terminology — RT-ICA, ARL, SAM, the S1-S7 pipeline stage names, Impact Radius — each with a pointer to its canonical source file. Use when a dh skill, agent, or workflow step references a term without defining it, before guessing what the term means from its observed outputs, or when asked what a dh concept or acronym stands for.

Development harness plugin documentation index. Use when looking up SAM pipeline, backlog lifecycle, SDLC layers, task/plan schema, plan artifacts, quality gates, or dispatch schema documentation.

Use when starting a new feature, gathering requirements for an unfamiliar domain, refining a vague idea into actionable scope, or when a user request is ambiguous or underspecified. Conducts SAM Stage 1 discovery — structured requirements gathering through user discussion, asking WHO/WHAT/WHEN/WHY and never HOW. Produces the ARTIFACT:DISCOVERY document containing feature requirements, NFRs, goals, anti-goals, references, and resolved questions. Supports backlog item self-initialization via a ...

Which agent a dh workflow dispatches, and the artifact consumer invariant.

Orchestrate parallel agent dispatch as a manager — not a micromanager. Use when coordinating 2+ independent workers, running SAM task waves, relaying discoveries between worker waves, handling blockers, or synthesizing results. Covers both SAM structured dispatch (the task does the work) and ad-hoc dispatch (reference agent-orchestration for prompt template).

Research community usage patterns, real-world gotchas, and client compatibility for a specific known library, tool, or protocol feature. Use when a technical-researcher orchestrator needs community-sourced evidence about a named library or feature — bug reports, workarounds, compatibility gaps, and patterns from issue trackers and discussions. Distinct from the broad ecosystem-researcher agent — this skill targets a KNOWN entity and mines what real users have actually experienced, not what ex...

Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

Executes SAM Stage 5 — dispatches a single ARTIFACT:TASK to a fresh stateless agent session, runs quality gates, and produces an ARTIFACT:EXECUTION with implementation results and verification output. Use when Stage 4 Task Decomposition is complete and tasks are ready for execution, when re-executing a task after Stage 6 returns NEEDS_WORK, or when dispatching a task to a language-appropriate specialist agent via the development harness pipeline.

Verify claims in backlog items, skill documentation, or plugin content against primary sources. Spawns parallel @dh:fact-checker agents using mcp__Ref, mcp__exa, mcp__context7 as primary tools — training data recall is rejected as evidence. WebFetch/WebSearch are last-resort fallbacks. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Triggers on "fact check", "verify claims", "check against primary sources", or when backlog items are marked UNVERIFIED.

Classify changed prose files into review tiers before deciding SKIP. Use when reviewing a diff containing prose files.

Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.

Wrap investigation requests with evidence-chain discipline. Use when the user asks to find out why something happens, look into something, research a root cause, debug an issue, or investigate unexpected behavior. Transforms vague investigation requests into reproducible-proof investigations. Invoke with /dh:find-cause <description of what to investigate>.

Use when SAM Stage 5 Execution has completed and task results need independent verification against acceptance criteria. Dispatches a separate reviewer agent to fact-check implementation outputs and returns COMPLETE or NEEDS_WORK with specific findings and remediation tasks.

Single-verb branch-to-PR quality gate pipeline. Use when the user wants to gate, push, and open a PR for a branch in one command.

Generates one worker task prompt conforming to the CLEAR + selective CoVe task design standard and swarm-task-planner structure. Use when creating or rewriting a single task entry or task block inside a plan — providing a title and brief description as input.