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/0xku/kon/initnpx skills add 0xku/kon --skill initgit clone --depth 1 https://github.com/0xku/konWhat 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 | $0.00011 | $0.00725 |
| Opus 5 | $0.00005 | $0.00362 |
| Sonnet 5 | $0.00002 | $0.00145 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
init 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create or update AGENTS.md for this repository.
The goal is a compact instruction file that helps future Kon sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these): $ARGUMENTS
How to investigate
Read the highest-value sources first:
README*, root manifests, workspace config, lockfiles- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (
AGENTS.md,CLAUDE.md,.cursor/rules/,.cursorrules,.github/copilot-instructions.md) - repo-local Kon config and conventions
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as
lint -> typecheck -> test - monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good AGENTS.md content is usually hard-earned context that took reading multiple files to infer.
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 · 74 lines · 11 tokens per session scan A 508d206064d5
init is a skill published in the GitHub repository 0xku/kon (353 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 725 once invoked, about $0.0001 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 2026-08-30.
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…