Harden repeated delivery loops with evidence-based harness stripping, immutable runtime-path sprint packets, and broken-window revalidation without automatic revert. Use for Cardputer, browser, CI, runtime, or production work split into repeated sprints.
Review whether a recurring or semi-autonomous agent loop is ready to run. Use before scheduling Codex, Claude Code, Grok, Opencode, CI, issue triage, PR babysitting, dependency sweeping, changelog drafting, or other repeated agent workflows.
Design and evolve agent skills into cost-bounded loops, workflows, scripts, and memory updates. Use when creating or auditing reusable agent methods, converting prompts or skills into workflows, designing open or closed agent loops, defining supervisor/evaluator contracts, or deciding whether repeated agent work…
Build or refine small production-ready Lottie animations from SVGs, logos, UI states, loaders, and branded motion assets with a local preview harness, explicit inputs, and output verification. Use when the user asks for Lottie, JSON animation, SVG reveal animation, logo animation, or lightweight motion assets for…
Build the smallest correct code diff without overengineering, broad refactors, unnecessary dependencies, or speculative abstractions. Use when the user wants a minimal patch, small reviewable diff, stdlib-first implementation, or shortest-path fix that still preserves security, accessibility, trust-boundary…
Review frontend motion and animation code before shipping, with strict checks for purpose, frequency, easing, duration, transform origin, interruptibility, GPU-safe properties, reduced-motion support, and hover behavior. Use when a diff touches transitions, keyframes, Framer Motion, Lottie, drawers, popovers, toasts…
Review code or diffs specifically for needless complexity, replaceable dependencies, dead flexibility, and wrappers over stdlib or native platform behavior. Use when the user asks what can be deleted, simplified, inlined, replaced with stdlib/platform features, or whether a change is over-engineered.
Design or maintain a local personal AI workspace that routes requests to isolated domains and professional role profiles. Use when one personal operator spans projects, career, publishing, research, coaching, fitness, finance, and internal development without mixing all context or memory.
★not rated 16▲
+1
changed 3d agoASkillSpector: pass54 tokens
originalMIT
Execute large tasks through explicit phases with acceptance criteria, required commands, and state updates. Use when work is too large for one implementation pass but too important to leave as a vague checklist. Keep phases falsifiable, end-to-end, and small enough that each one can be verified honestly before moving…
Choose and run a compact pre-implementation, post-implementation, or ship-readiness gate for agent work. Use when a task needs ordered review lenses, proof gates, or release confidence without adopting a heavy multi-agent pipeline runtime.
Break a PRD, plan, or spec into independently executable vertical slices. Use when a product artifact needs to become agent-ready or human-ready implementation tickets, especially for client, product, and multi-track project workflows. Prefer thin end-to-end slices, explicit blockers, and issue-tracker-neutral output.
Audit a private skill, prompt pack, or assistant workflow before publishing it. Use when extracting public reusable method from client, product, team, or repository-specific agent instructions. Mark private vs mixed vs reusable content, rewrite the mixed layer, and gate release on decontextualization.
Build or refresh a compact, evidence-backed product context before marketing, positioning, SEO, analytics, sales-enablement, or launch work. Use when an agent needs to understand the product, audience, proof, conversion goal, terminology, and claim boundaries without inventing commercial facts.
Bootstrap usable project context before deep implementation work. Use when entering a new or unfamiliar repo, installing agent workflows into an existing codebase, auditing stale docs, or preparing a project for repeatable long-running agent work. Detect the stack, ask the user about the project, summarize…
Build throwaway prototypes to answer a specific product, UI, state-machine, data-model, or workflow question before committing production code. Use when the user asks to prototype, explore variants, sanity-check logic, try designs, make a playable mock, or learn quickly with code that will be deleted or absorbed.
Verify whether a software release is publicly proven across repository identity, baseline, build, package, deployment, HTTP routes, SEO metadata, browser desktop/mobile behavior, locale content, visual availability, and human approval. Use when an agent is preparing or reviewing a release and must separate build…
Use local Rabbithole for human-in-the-loop exploration of AGENTS.md, SKILL.md, SET plans, repomaps, and long repo docs. Use when the user wants to inspect, question, branch, or review dense agent-facing documents interactively before changing durable docs or workflow contracts.
Lightweight grilling session for brainstorming, product clarification, and early design shaping. Use when the user wants fast alignment but there is not yet enough repo context to ground a heavier doc-based review. Ask one question at a time, propose a recommended answer, and keep momentum high.
Recover from failed execution or verification with a bounded three-step loop: retry, focused fix-spec, then honest handoff. Use when an agent is likely to spin on the same failure, when flaky environment issues blur the signal, or when you need a disciplined stop condition instead of endless optimistic retries.
Debug repositories with a hypothesis ledger, checked locations, and loop-breaking discipline. Use when tests fail, CI fails, behavior regresses, local tooling errors, flaky bugs appear, or an investigation risks repeated searches, repeated commands, circular hypotheses, or unverified fixes.
Triage bugs, enhancements, and backlog items through a small state machine that works with or without a formal issue tracker. Use when reviewing incoming requests, preparing work for an agent, deciding whether more info is needed, or keeping a multi-project backlog healthy.
Run risky or multi-file agent work as a reviewable proposal before mutating the target workspace. Use when a task should produce retained output, inspectable diffs, and an explicit select/apply/discard decision instead of direct edits.
Design professional role agents and role-specific skill packs for product, personal, and internal AI layers. Use when adapting expert-role patterns into bounded assistants, workers, reviewers, or coordinators with explicit context, authority, evaluation, and rollout rules.
★not rated 16▲
+1
changed 3d agoA54 tokens
originalMIT
Reduce shell output token waste on git, file reads, searches, test runs, linters, logs, and other noisy developer commands with RTK-style filtering. Use when shell-heavy work is flooding the session with logs, diffs, or command output that should be compacted before it reaches the model.
★not rated 16▲
+1 yesterdayA66 tokens
originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: