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/dragoon0x/usemindit/mindit-contextnpx skills add Dragoon0x/usemindit --skill mindit-contextgit clone --depth 1 https://github.com/Dragoon0x/useminditWhat 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 | $0.00115 | $0.01213 |
| Opus 5 | $0.00057 | $0.00607 |
| Sonnet 5 | $0.00023 | $0.00243 |
| Haiku 4.5 | $0.00012 | $0.00121 |
Grade A, and why
mindit-context 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mindit-context
The fifth of the eight forces. Run this when the question is "who, when, where, in what state."
The force
Designs are usually shown in one state: the happy path, the desktop viewport, the freshly-onboarded user, the populated screen, the fast network. Real users never see that screen. They see the screen with no data, with a network error, on a phone in sunlight, on their fourth visit when they have forgotten how the product works, when they are tired, when they are trying to recover from a mistake.
Context asks: of all the states this screen will exist in, how many has the design accounted for?
When to run this
- The user shows only desktop mocks and the product is shipped on mobile too.
- The user mentions empty, loading, error, or offline states.
- The user mentions a persona, a user segment, or a specific use environment (one-handed mobile, dark mode, slow network, screen reader, kiosk).
- The user uses words like "what happens when," "edge case," "fallback," "first-time," "returning user."
- The user asks about onboarding, recovery flows, or recovery from mistakes.
How to analyze
-
Map the contexts. List every dimension that matters for this design:
- Audience: who, with what familiarity, with what motivation.
- Device and viewport: phones, tablets, desktops, larger; portrait/landscape.
- State: loading, empty, populated, error, partial, offline, slow, stale, locked, expired.
- Journey position: first visit, returning, mid-flow, post-completion, recovery.
- Environment: bright sun, noisy, mobile data, screen reader, voice assistant.
-
Identify which contexts the design has addressed. Compare the design against the list. Each context that has not been addressed is a finding.
-
Distinguish "not addressed" from "explicitly out of scope." Some contexts are intentionally not supported (e.g. tablet not a priority). That is a different finding than "we forgot mobile exists."
-
Score the criteria below.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 81 lines · 115 tokens per session scan A 171433b9f0a3
mindit-context is a skill published in the GitHub repository Dragoon0x/usemindit (2 stars, last pushed 3mo ago), licensed MIT. It adds 115 tokens to every session and 1,213 once invoked, about $0.0006 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…