Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/resciencelab/tryskills/boldernpx skills add ReScienceLab/TrySkills --skill boldergit clone --depth 1 https://github.com/ReScienceLab/TrySkillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00051 | $0.01484 |
| Opus 5 | $0.00026 | $0.00742 |
| Sonnet 5 | $0.00010 | $0.00297 |
| Haiku 4.5 | $0.00005 | $0.00148 |
Grade A, and why
bolder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
72% identical to bolder — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Increase visual impact and personality in designs that are too safe, generic, or visually underwhelming, creating more engaging and memorable experiences.
MANDATORY PREPARATION
Invoke /impeccable — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /impeccable teach first.
Assess Current State
Analyze what makes the design feel too safe or boring:
-
Identify weakness sources:
- Generic choices: System fonts, basic colors, standard layouts
- Timid scale: Everything is medium-sized with no drama
- Low contrast: Everything has similar visual weight
- Static: No motion, no energy, no life
- Predictable: Standard patterns with no surprises
- Flat hierarchy: Nothing stands out or commands attention
-
Understand the context:
- What's the brand personality? (How far can we push?)
- What's the purpose? (Marketing can be bolder than financial dashboards)
- Who's the audience? (What will resonate?)
- What are the constraints? (Brand guidelines, accessibility, performance)
If any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.
CRITICAL: "Bolder" doesn't mean chaotic or garish. It means distinctive, memorable, and confident. Think intentional drama, not random chaos.
WARNING - AI SLOP TRAP: When making things "bolder," AI defaults to the same tired tricks: cyan/purple gradients, glassmorphism, neon accents on dark backgrounds, gradient text on metrics. These are the OPPOSITE of bold—they're generic. Review ALL the DON'T guidelines in the impeccable skill before proceeding. Bold means distinctive, not "more effects."
Plan Amplification
Create a strategy to increase impact while maintaining coherence:
- Focal point: What should be the hero moment? (Pick ONE, make it amazing)
- Personality direction: Maximalist chaos? Elegant drama? Playful energy? Dark moody? Choose a lane.
- Risk budget: How experimental can we be? Push boundaries within constraints.
- Hierarchy amplification: Make big things BIGGER, small things smaller (increase contrast)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 117 lines · 51 tokens per session scan A 2d4df2c1a6ff
bolder is a skill published in the GitHub repository ReScienceLab/TrySkills (2 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,484 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 72% identical to bolder, differing in 31 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…