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 skills add saski/arnesto --skill ai-patternsgit clone --depth 1 https://github.com/saski/arnestoWrote 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/saski/arnesto/ai-patterns)<a href="https://agentmods.dev/skills/saski/arnesto/ai-patterns"><img src="https://agentmods.dev/badge/skills/saski/arnesto/ai-patterns/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/skills/saski/arnesto/ai-patterns"><img src="https://agentmods.dev/badge/skills/saski/arnesto/ai-patterns.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 | $0.00040 | $0.01263 |
| Opus 5.5 | $0.00016 | $0.00505 |
| Sonnet 5.5 | $0.00008 | $0.00253 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
ai-patterns 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 1mo 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.
This is a copy
100% identical to ai-patterns — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
AI Patterns Reference
Patterns for effective AI-augmented software development by Lada Kesseler (github nickname lexler), Llewellyn Falco, Ivett Ördög, and Nitsan Avni.
First Step: Ensure Repository Exists and Update
~/.claude/skills/ai-patterns/scripts/ensure-patterns-repo
Patterns Location
Base path: ~/.cache/claude-skills/augmented-coding-patterns/documents
Context Management
Managing AI context, knowledge, and focus.
Obstacles
- context-rot - Earlier instructions lose influence as conversation grows
- cannot-learn - LLMs can't learn from interactions; fixed weights prevent adaptation
- limited-context-window - Fixed context size forces choices about what to keep loaded
- limited-focus - Too much context causes diluted or misdirected attention
- excess-verbosity - AI defaults to verbose output with low signal-to-noise ratio
Anti-patterns
- distracted-agent - Using one agent for everything spreads attention; instructions inconsistently followed
Patterns
- context-management - Treat context as scarce resource requiring active append/reset operations
- knowledge-document - Save important information as markdown files for session loading
- ground-rules - Essential behavioral rules auto-loaded into every session
- extract-knowledge - Save emerging insights and corrections from ephemeral context to files immediately during sessions
- focused-agent - Single narrow responsibility gives AI cognitive space to follow rules better
- reference-docs - On-demand knowledge loaded only when needed for current task
- knowledge-composition - Split knowledge into focused, composable files with single responsibilities
- semantic-zoom - Control abstraction levels—zoom out for overview or zoom in for details
- noise-cancellation - Explicitly ask AI to be succinct and strip filler from responses
Reliability & Quality
Handling non-determinism, complexity, and verification.
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.
- 1mo ago First seen · 133 lines · 40 tokens per session scan A 20b409e4574a
ai-patterns is a skill published in the GitHub repository saski/arnesto (6 stars, last pushed 5d ago), licensed Unlicense. It adds 40 tokens to every session and 1,263 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ai-patterns, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
readonly-review
Run a no-network read-only review practice with two explorer steps and one synthesis step.
repo-harness-assessment
Use when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md, CLAUDE.md, GEMINI.md, Cursor rules, or GitHub instructions.
work-state-and-delivery
Use when designing or reconciling design docs, task boards, external trackers, execution plans, delivery records, handoffs, review evidence, task-to-change traceability, or repository commit-coupling policy.
repo-contracts-and-boundaries
Use when turning architecture, layering, ownership, dependency direction, schemas, structural metrics, quality thresholds, baselines, allowlists, or generated quality snapshots into repository checks.
runtime-evidence-and-tracing
Use when connecting observed behavior, logs, metrics, request IDs, run IDs, screenshots, traces, external dependency results, or artifacts into a runtime evidence loop.
validation-harness-design
Use when designing repository validation commands, doctor scripts, test matrices, JSON or JUnit outputs, CI gates, smoke checks, or harness command surfaces.