Julius Skills

Seven personal agent skills for now: Caveman base, Interface Kit, Grill Me, Loop Factory, Junior to Senior, F*ck Slop, and Context Canary.

This repo is shaped by seven things:

• Caveman - 70k-star token compression without technical loss. Small mouth, big brain.
• Interface Kit - accessible, performant interfaces with strong aesthetic direction, not generic AI slop.
• Grill Me - calibrated pressure before hard critique, so challenge matches user knowledge and comfort.
• Loop Factory - spec-driven agent loop where tasks move through inbox → active → archive with a real review gate.
• Junior to Senior - adversarial senior review that treats agent output as junior work and upgrades it with codebase + web research.
• F*ck Slop - mechanical scan-and-rewrite loop that erases AI-writing tells from any text and lands it in the right register.
• Context Canary - per-turn canary signal that makes silent context degradation visible, plus a recovery protocol when it trips.

Point is control. Agents should be terse when talking, precise when building interfaces, calibrated when challenging plans, and disciplined when running build loops.

Quickstart

Install with skills.sh:

npx skills@latest add JuliusBrussee/skills

Pick the skills you want for Claude Code, Codex, Gemini, Cursor, Windsurf, Cline, Copilot, or any agent supported by the installer.

For the canonical Caveman installer, use:

curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash

Why These Skills Exist

Most agent output fails in three boring ways: too many words, UI that looks like every other generated demo, or critique that starts too hard before understanding user context.

This repo keeps three fixes close:

1. Speak less, say more. Caveman cuts output tokens while preserving exact commands, code, errors, and technical meaning.
2. Build interfaces with taste. Interface Kit starts from accessibility, performance, typography, spatial rhythm, color roles, and concrete interaction states.
3. Challenge at the right altitude. Grill Me assesses knowledge and desired pressure first, then asks one question at a time.

Skills

caveman

Ultra-compressed communication mode. Cuts token usage by dropping filler, hedging, pleasantries, and excess grammar while keeping technical accuracy intact.

Use when you want:

• shorter agent replies
• less token waste
• exact code, command, error, and API preservation
• persistent terse style until user exits with "normal mode"

interface-kit

Implementation guide for high-quality UI. Synthesizes accessibility, performance, typography, layout, color systems, motion, interaction states, and component craft.

Use when building:

• frontend components
• landing pages
• dashboards
• design systems
• polished app flows
• accessibility fixes
• UI review passes

grill-me

Calibrated interview skill for stress-testing plans, designs, and decisions. It first asks how much the user knows and how hard they want the pressure, then ramps from clarifying questions to failure-mode critique.

Use when you want:

• plan critique without getting overwhelmed
• one question at a time
• recommended answers with each question
• pressure matched to beginner, working, or expert knowledge
• softer or harder grilling on command

loop-factory

Spec-driven agent loop. Coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and must pass a review gate before they count as done. State is just which folder a spec is in. The governing rule: automate implementation and verification, not product decisions.

Use when you want:

• repeatable, reviewable agent work instead of one-off prompting
• visible task state (inbox / active / archive) with no dashboard
• generated implementation, review, and backprop prompts for either agent
• a hard review gate before anything is marked done
• to install or scaffold the loop-factory CLI into a project

Pairs with the Loop-Factory repo, which ships the CLI and native Claude/Codex adapters.

junior-to-senior

Adversarial review skill for agent-generated plans. Treats the current output as the work of a junior, then constructs a senior reviewer grounded in codebase research and web research of current best practices. Diagnoses altitude failures — plans that are foggy on the hard parts or tunneled into details with no product vision — and rewrites them into a scoped, state-of-the-art version with evidence behind every finding.

Use when you want:

• a staff-engineer-grade review of a plan before committing to it
• plans that commit on interfaces, versions, and failure modes instead of hand-waving
• best practices refreshed past the model's training cutoff via live web research
• a clear delta between the original plan and the upgraded one
• product decisions surfaced as open questions instead of silently invented

fuck-slop

De-slop pass for any text. Scans with a regex catalog of AI-writing tells — negative parallelism ("not X but Y"), puffery vocabulary, rule-of-three, false ranges, em-dash abuse, uniform cadence, hedged both-sidesing — then rewrites at the level of meaning and re-scans its own output until clean. Built as a loop because the worst tells are emergent generative habits that a rewriting model reintroduces in paraphrase.

Use when you want:

• AI-drafted text that reads like a person wrote it
• a diagnosis table of which tells were found before the rewrite
• register-aware output: academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing
• no overcorrection — no fake typos, forced slang, or invented specifics

context-canary

Early-warning system for long agent sessions. Installs a byte-stable first-line signal — the user's name, a turn counter, and an honest context self-check — so the moment the agent's hold on its instructions degrades (attention drift, compaction, truncation), the signal visibly dies. Comes with a trip protocol: checkpoint state to a file, re-anchor on project instructions, reset deliberately.

Use when you want:

• to know when a long session starts rotting instead of finding out from bad output
• a zero-infrastructure health check that runs every single turn
• compaction events surfaced the moment they happen
• a disciplined recovery path (checkpoint → re-anchor → fresh session) instead of limping on

Grounded in context-rot research (Chroma), lost-in-the-middle (Liu et al.), and instruction-drift findings — sources linked in the skill's references.

Interface Kit Standard

If a repo has DESIGN.md, it wins. Otherwise UI work should still have a point of view:

• Accessibility first: contrast, keyboard navigation, focus states, semantics.
• Performance before decoration: stable layout, lazy assets, transform-only motion.
• Typography matters: readable type scale, sane line length, tabular numbers for data.
• Layout uses stable spatial rules: 4/8px spacing, responsive constraints, no overlap.
• Components have complete states: hover, focus, active, disabled, loading, empty, error.
• Avoid generic defaults: no faceless hero, no purple-blue gradient template, no stock-looking polish.

Caveman Ecosystem

This repo sits next to broader Julius agent stack:

Repo	What
caveman	Output compression for agents.
caveman-code	Terminal coding agent built around token efficiency.
cavemem	Cross-agent memory.
cavekit	Spec-driven build loop.
cavegemma	Fine-tuned model experiments for terse agent output.

Skill Reference

Run:

node scripts/list-skills.mjs

Verify frontmatter:

node scripts/verify-skills.mjs

Contributing

Skills should be:

• Small enough to read quickly.
• Triggered by clear user intent.
• Progressive: load only the extra files needed.
• Specific about workflow and validation.
• Free of repo-specific assumptions unless the skill says so.

License

MIT.