Borrowing it
Nothing to install: this file belongs to ahmed3elshaer/everything-claude-code-mobile. 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/ahmed3elshaer/everything-claude-code-mobile/main/.opencode/skills/continuous-learning-v2/SKILL.mdgit clone --depth 1 https://github.com/ahmed3elshaer/everything-claude-code-mobileWrote 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/skills/ahmed3elshaer/everything-claude-code-mobile/continuous-learning-v2)<a href="https://agentmods.dev/skills/ahmed3elshaer/everything-claude-code-mobile/continuous-learning-v2"><img src="https://agentmods.dev/badge/skills/ahmed3elshaer/everything-claude-code-mobile/continuous-learning-v2.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00317 |
| Opus 5 | $0.00012 | $0.00159 |
| Sonnet 5 | $0.00005 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
continuous-learning-v2 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.
What it actually says
Continuous Learning v2
Instinct-based learning with confidence scoring.
Instinct Structure
{
"id": "compose-state-hoisting",
"type": "pattern",
"description": "Always hoist state to caller in Composables",
"confidence": 0.85,
"examples": [...],
"context": "jetpack-compose",
"lastUsed": "2026-02-02"
}
Confidence Scoring
| Score | Meaning |
|---|---|
| 0.0-0.3 | Experimental |
| 0.3-0.6 | Validated |
| 0.6-0.8 | Established |
| 0.8-1.0 | Best practice |
Confidence increases with:
- Successful application
- User acceptance
- Consistency across sessions
Commands
/instinct-status # View with confidence
/instinct-import <file> # Import from others
/instinct-export # Export for sharing
/evolve # Cluster related instincts
Mobile-Specific Instincts
Pre-configured patterns for:
- Compose recomposition optimization
- MVI state management
- Koin module organization
- Ktor error handling
- Espresso test patterns
Remember: Instincts evolve. Low confidence patterns may become best practices.
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 · 59 lines · 23 tokens per session scan A 2bd7204822f5
continuous-learning-v2 is a skill published in the GitHub repository ahmed3elshaer/everything-claude-code-mobile (66 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 317 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
SKILL
This document provides a comprehensive technical reference for KSensor, a Kotlin Multiplatform (KMP) library designed for observing device sensors and system states on Android and iOS.
compose-animations
Use when writing or reviewing Jetpack Compose motion: visibility enter/exit, animating one property toward a target, color or size transitions, multiple properties from one state, switching composable content, or choosing between AnimatedVisibility, animateAsState, rememberTransition, AnimatedContent, and Crossfade.
compose-focus-navigation
Use when writing or reviewing Jetpack Compose UI for TV, keyboard, desktop, accessibility focus, D-pad navigation, FocusRequester, focusProperties, key events, or initial focus behavior.
compose-state-and-effects
Use when writing or reviewing Jetpack Compose state ownership, remember state, state hoisting, screen state holders, LaunchedEffect, DisposableEffect, SideEffect, Flow collection, navigation, snackbar, analytics, or focus requests.
compose-component-design
Use when designing or reviewing reusable Jetpack Compose component APIs with modifier parameters, root layout placement, caller-provided variable content, primitive content parameters, optional content, or boolean shape flags.
compose-performance
Use when investigating Jetpack Compose recomposition cost, compiler stability reports, skippability, unstable parameters, frame-rate State reads, cross-phase snapshot back-writing, or @ReadOnlyComposable contracts.