Cole's AI Skills

Plugin skills from marketplace cole-medin in coleam00/skills. Commands read from the repo's manifest — 3 days ago.

Skills (39)

• ablate-ai-layer

  Measure whether a repository's AI instructions still earn their place, by running the same real task many times with the layer intact and with it stripped, then grading every rule against what actually changed. Runs both arms itself in throwaway git worktrees and never touches the working tree. Agen

• agent-browser

  Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form

• ast-grep

  Guide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search. This skill should be used when users a

• build-dark-factory

  Take a PRD and build a dark factory around it - a repository that takes work in as an issue and ships validated code out with nobody at the keyboard - one component at a time, into the user's actual repo. Covers the five components in construction order - the guidance layer, the validation harness,

• build-signal-engine

  Build a personal signal engine from scratch - a system that reads every source someone cares about each day (changelogs and release notes, communities, feeds, videos, papers), makes a quick decision on every item, lets an LLM read only what survives, and delivers one short digest. Interviews the use

• drive-screen

  Take real control of the desktop - list and focus windows, type, paste, click, scroll, and screenshot - on Windows, macOS or Linux, and drive other coding-agent sessions running in terminals. Use when asked to set up the screen or the day, open and arrange a set of apps or repos, prepare or run a li

• edit-video

  Edit talking-head footage into a finished video with a script-driven pipeline the agent can verify without watching. Takes one recording or a folder of numbered clips, cuts it tight (no dead air, no breaths, no clipped words), places cut points with forced alignment instead of trusting Whisper times

• hooks-create

  Author a working Claude Code hook from a plain-English description of what it should guarantee or do. You describe the behavior ("never let the agent edit my migrations", "don't stop until the tests pass", "log every command"); this skill picks the right lifecycle event, writes the hook script, and

• opportunity-scan

  Scan how you actually work with your coding agent and surface what to encode next. Point it at ONE run's artifacts to find what would have prevented a specific failure (the reactive loop — 'that went wrong, what should change in the AI layer?'), or at a window of session logs to find recurring patte

• piv-commit

  Creates a new git commit for all uncommitted changes with an atomic, conventionally-tagged message. Use when work is complete and ready to be committed.

• piv-create-pr

  Push the current feature branch and open a pull request, ready for review. Use after a ticket's implementation is committed on its own branch — it detects the base branch, pushes, opens the PR with a clear body (summary · what changed · validation status), and returns the URL to hand to a reviewer.

• piv-fix-review-findings

  Triage code-review findings (manual or AI), fix the ones you choose one at a time with tests, defer/log the rest, then validate — and if the work is on a PR, commit and push so the PR reflects the fixes. Use after a review has produced a list of issues or a review file.

• piv-implement-issue

  Implement the fix for a GitHub issue from its RCA artifact (created by piv-investigate-issue) — drift-check the plan, branch, implement, add regression tests, and validate. Use after the investigation artifact exists and you're ready to fix the issue.

• piv-implement

  Executes an implementation plan task-by-task with validation at every step. Use when you have a completed feature plan and want to implement it in one pass.

• piv-investigate-issue

  Investigate a GitHub issue — fan out parallel exploration, find the root cause (5 Whys, evidence-backed), and write a reviewable RCA artifact (then post a summary to the issue). The investigate step before piv-implement-issue. Use to diagnose a bug/issue before fixing it.

• piv-plan-implementation

  Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research. Accepts a tracker ticket (a Jira/Linear/GitHub key or URL, fetched from the tracker) or a free-form feature request. Use when you have a ticket or feature an

• piv-review-changes

  Performs a technical code review of recently changed files for bugs, security issues, and standards compliance, then writes a report. Use before committing, as a pre-commit quality gate.

• piv-review-pr

  Full pull-request review — fetch the PR, run the project's validation, review the diff with fresh eyes (dispatching the code-reviewer agent), categorize issues by severity, post the review to GitHub (approve / request-changes / comment), and save a report. The agentic gate that runs on an open PR be

• piv-run-full-loop

  Autonomously develops a complete feature from priming through planning, execution, and commit by chaining the four core PIV-loop skills. Use when you want a full hands-off feature build from a single description.

• piv-slice-epic

  Slice an epic (with its architecture decisions) into PIV-sized tickets with a dependency graph, then create them in your tracker (Jira via the Atlassian MCP, or GitHub/Linear/local). Accepts the epic and its architecture as one doc or as an epic plus a separate linked architecture page. Turns a larg

• piv-validate

  Runs this project's full validation suite — tests, type checks, and linting across every part of the stack — then reports overall health. Use before committing, before opening a PR, or after finishing a chunk of work to confirm zero regressions.

• plan-architecture

  Interactively explore HOW to approach an intent (a PRD, epic, brief, or free-form idea) and decide the high-level architecture — the approach, stack, libraries, data shape, and risks the intent left open. A working session with a CTO/staff-engineer advisor that asks questions, proposes 2–3 options w

• plan-create-prd

  Interactive, problem-first PRD generator — interviews the user to surface the thesis (the problem, and WHY build it) and a falsifiable hypothesis, then writes a focused PRODUCT-level PRD (problem · evidence · hypothesis · users · MVP · success metrics · non-goals · open questions). Use at the start

• plan-create-stories

  Decomposes a PRD into well-formed, engineer-ready tickets — Jira issues or GitHub issues. Use after a PRD exists, to turn its phases and user stories into a structured backlog. Works for a new codebase (MVP scope) or an existing one (epic scope).

• plan-with-priorities

  Plan a product or technical decision by eliciting the user's priorities before recommending options. Use when the user wants help deciding what to build or which approach to take, especially when the goals or tradeoffs are unclear. Supports small spikes when the user needs to try alternatives before

• prime-backend

  Primes the agent with focused understanding of the backend portion of the codebase — API routes, services, data models, and database layer — without loading unrelated frontend code. Use at the start of a session when the work is scoped to API endpoints, business logic, or data access. Optionally pul

• prime-codebase

  Primes the agent with deep codebase understanding by analyzing structure, documentation, and key files. Use when starting work on a codebase, at the beginning of a session, or when you need a fast orientation before planning or implementing. Optionally pulls external task context from Jira issues an

• prime-frontend

  Primes the agent with focused understanding of the frontend portion of the codebase — components, routing, state management, and styling — without loading unrelated backend code. Use at the start of a session when the work is scoped to UI or client-side features. Optionally pulls external task conte

• rules-check-drift

  Check whether your rules file (CLAUDE.md or AGENTS.md) still matches the codebase after recent changes — run before a merge, or fold into your code-review pass. Reports stale/now-false rules, drifted architecture-map entries, and any new invariant worth adding, each with the minimal edit. Advisory a

• rules-create-global

  Set up your project's global rules, a lean and well-structured root CLAUDE.md (plus a starter .claude/), following the course methodology. Greenfield: pass your PRD and/or architecture-spec path and it derives rules from your engineering decisions (a PRD alone is product context). Brownfield: leave

• second-brain-audit

  Audit any second brain, notes folder, or agent memory for facts that have quietly stopped being true, then fix the worst one so it stops recurring. Works on a wiki, a single notes file, daily notes, or a non-markdown tool, and adapts the fix to whichever it finds. Checks every current-sounding claim

• second-brain-fix

  Work through the findings from a second-brain audit in batches, correcting stale facts and converting locations onto the state/event schema, driven by the markdown file of findings the audit left behind. Use after running second-brain-audit, when someone wants to fix the rest of their notes rather t

• second-brain-prd

  Generate a personalized, phased PRD for building an AI second brain in TypeScript from a filled-out requirements template. Memory, hooks, hybrid search, integrations, skills, a proactive heartbeat, chat, security and deployment, in build order. Use when the user wants to build a second brain, has fi

• setup-ai-tutor

  Sets up and starts the AI Tutor project locally (environment file, dependencies, database, migrations, and dev server). Use when standing up the AI Tutor for the first time on a new machine or after a fresh clone. This is sample-project specific; adapt it when you install the AI Layer into your own

• skills-create

  Author a new Claude Code skill the house way, or refactor a fat skill into a lean SKILL.md + references/. Use when you want to "create a skill", "write a new skill", "turn a prompt or command into a skill", "split a skill into references", "trim a SKILL.md", or invoke /skills-create. The meta-skill

• system-evolution-review

  Performs a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements. Use after an execution report exists to find bugs in the process, not the code.

• system-execution-report

  Generates a structured implementation report reflecting on a just-completed feature — what was done, divergences, challenges. Use right after finishing an implementation, as the input to a system review.

• worktree-create

  Create one or more git worktrees for parallel development, each on its own branch with gitignored config copied in, dependencies installed, and a health check, by fanning out a setup subagent per worktree. Use when starting isolated parallel work, running several PIV loops at once, or when the user

• worktree-merge

  Integrate any number of feature branches from parallel worktrees through one safe integration branch, validating after each merge and running the project's full check suite before touching the main line. Use when parallel worktree development is done and the branches need to be merged, or when the u

Similar plugins