Borrowing it
Nothing to install: this file belongs to dungnotnull/aim-training-sensitivity-converter-agent-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dungnotnull/aim-training-sensitivity-converter-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/aim-training-sensitivity-converter-agent-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/dungnotnull/aim-training-sensitivity-converter-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/aim-training-sensitivity-converter-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/aim-training-sensitivity-converter-agent-skill/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/dungnotnull/aim-training-sensitivity-converter-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/aim-training-sensitivity-converter-agent-skill/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.01421 | $0.01421 |
| Opus 5 | $0.00711 | $0.00711 |
| Sonnet 5 | $0.00284 | $0.00284 |
| Haiku 4.5 | $0.00142 | $0.00142 |
Grade A, and why
aim-training-sensitivity-converter-agent-skill CLAUDE.md 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 8d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md ? Skill 195: aim-training-sensitivity-converter
Skill Identity
- Skill Name:
aim-training-sensitivity-converter - Tagline: Aim Training & Sensitivity Converter (Mouse/Cross-Game) ? FPS Aim Training & Input Configuration analysis & decision-support harness.
- Current Phase: Phase 5 ? Integration & Polish (PRODUCTION READY v1.1.0)
- Folder:
D:\972026\195-aim-training-sensitivity-converter\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for FPS Aim Training & Input Configuration. It converts mouse sensitivity consistently across games using the authoritative cm/360 method (delegated to a deterministic engine, never by hand), applies recognized domain methods (deliberate practice, motor-learning feedback), cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources ? continuously self-improving through an automated knowledge crawl pipeline.
Harness Flow Summary
/aim-training-sensitivity-converter invoked
?
?? Pre-Flight: detect language (vi/en)
?? Step 1: sub-gather-requirements ? game(s), DPI, sens, FOV, grip, mouse, skill, weaknesses
?? Step 2: sub-evidence-collector ? engine constants, sensor specs, pro refs, docs, news
?? Step 3: sub-core-analysis ? cm/360 conversion (engine), aim assessment, routine
?? Step 4: sub-knowledge-updater ? academic evidence with tiers + gap flags
?? Step 5: sub-advisor ? risk-disclosed verdict + evidence chain + actions
?? Step 6: main (quality gate) ? verify U1?U6 + G1?G4, auto-fix, deliver
Sub-Skills
| Sub-skill | Purpose |
|---|---|
skills/sub-gather-requirements.md |
Clarify game(s), DPI, sens, FOV, grip, mouse, skill, weaknesses, audience, language. |
skills/sub-evidence-collector.md |
Fetch engine constants, sensor specs, pro references, docs, recent developments. |
skills/sub-core-analysis.md |
Convert sens via the engine (cm/360), assess aim by category, prescribe routine. |
skills/sub-knowledge-updater.md |
Surface academic evidence with tier labels; flag gaps for the crawl pipeline. |
skills/sub-advisor.md |
Synthesize a risk-disclosed verdict + evidence chain + recommended actions. |
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.
- 8d ago First seen · 133 lines · 1,421 tokens per session scan A 111125170683
aim-training-sensitivity-converter-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/aim-training-sensitivity-converter-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,421 tokens to every session, about $0.0071 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.
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