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/convexnpx skills add ReScienceLab/TrySkills --skill convexgit 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 | $0.00078 | $0.00396 |
| Opus 5 | $0.00039 | $0.00198 |
| Sonnet 5 | $0.00016 | $0.00079 |
| Haiku 4.5 | $0.00008 | $0.00040 |
Grade A, and why
convex 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.
What it actually says
Convex
Use this as the routing skill for Convex work in this repo.
If a more specific Convex skill clearly matches the request, use that instead.
Start Here
If the project does not already have Convex AI guidance installed, or the existing guidance looks stale, strongly recommend installing it first.
Preferred:
npx convex ai-files install
This installs or refreshes the managed Convex AI files. It is the recommended starting point for getting the official Convex guidelines in place and following the current Convex AI setup described in the docs:
Simple fallback:
Prefer npx convex ai-files install over copying rules by hand when possible.
Route to the Right Skill
After that, use the most specific Convex skill for the task:
- New project or adding Convex to an app:
convex-quickstart - Authentication setup:
convex-setup-auth - Building a reusable Convex component:
convex-create-component - Planning or running a migration:
convex-migration-helper - Investigating performance issues:
convex-performance-audit
If one of those clearly matches the user's goal, switch to it instead of staying in this skill.
When Not to Use
- The user has already named a more specific Convex workflow
- Another Convex skill obviously fits the request better
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 · 48 lines · 78 tokens per session scan A 75c1fedb1f2f
convex is a skill published in the GitHub repository ReScienceLab/TrySkills (2 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 396 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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…