Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. 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/Smart-AI-Memory/attune-ai/main/.agents/skills/attune-hub/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/attune-hub)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/attune-hub"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/attune-hub/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/smart-ai-memory/attune-ai/attune-hub"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/attune-hub.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.01496 |
| Opus 5 | $0.00024 | $0.00748 |
| Sonnet 5 | $0.00010 | $0.00299 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
attune-hub 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 10d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attune Hub
IMPORTANT: Start your response by telling the user:
Attune Hub — Your starting point — discovers what you need and routes you to the right skill.
Scoping
If no argument is provided, use AskUserQuestion:
question: "What are you trying to accomplish?"
header: "attune-ai"
options:
- label: "Run a workflow"
description: "Security audit, code review, test gen, perf, release prep"
- label: "Manage memory"
description: "Store, retrieve, search, or forget patterns"
- label: "Configure settings"
description: "Check setup, update attune-ai, view telemetry"
- label: "Learn what attune-ai does"
description: "Overview of capabilities and skills"
Execution
Based on the user's answer or arguments, describe the intent so Claude matches the right skill:
| Input | Describe to Claude |
|---|---|
"Run a workflow" or security |
"Run a security audit on the code" |
"Run a workflow" or review |
"Review the code for quality issues" |
"Run a workflow" or tests |
"Generate tests for uncovered code" |
"Run a workflow" or perf |
"Analyze code for performance issues" |
"Run a workflow" or release |
"Prepare for a release" |
"Run a workflow" or bugs |
"Predict likely bug locations" |
"Manage memory" or memory |
"Store or retrieve from memory" |
"Configure settings" or setup |
Run attune doctor and attune auth |
"Configure settings" or update |
Run pip install --upgrade attune-ai |
Natural Language Routing
| Pattern | Route to |
|---|---|
| "security", "vulnerability", "audit" | security-audit skill |
| "review", "quality", "code review" | code-quality skill |
| "test", "generate tests", "coverage" | smart-test skill |
| "performance", "bottleneck", "optimize" | workflow-orchestration skill |
| "release", "publish", "ship" | release-prep skill |
| "bug", "predict", "risk" | bug-predict skill |
| "run all audits", "full sweep", "what should I fix" | discovery-sweep skill |
| "memory", "store", "remember" | memory-and-context skill |
| "docs", "documentation" | doc-gen skill |
| "plan", "feature", "architecture" | planning skill |
| "refactor", "tech debt", "simplify" | refactor-plan skill |
| "spec", "brainstorm", "plan and execute" | spec skill |
| "scope this", "ask me everything at once", "discovery form" | elicit skill |
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.
- 10d ago First seen · 130 lines · 48 tokens per session scan A 41de7935359a
attune-hub is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 1,496 once invoked, about $0.0002 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
eco-max
Maximum-savings variant of /eco - the same frugality rules PLUS a low reasoning-effort override for the invoked task. Use for routine chores (rename, small fix, quick question, boilerplate) when the user wants absolute minimum token spend; prefer plain /eco for hard or high-stakes work. Works in any language.
wiki-ingest
Ingest a source into the project wiki as OKF v0.2 markdown. Point at a file, PR, or doc and the wiki-curator extracts knowledge, writes YAML frontmatter, and updates relevant concept pages.
wiki-lint
Health-check the project wiki for OKF v0.2 conformance — missing frontmatter, missing type:, malformed index.md/log.md, stale pages past staleafter, broken cross-references, and coverage gaps.
run
Run a full pipeline for a task. Orchestrates roles through stages (standalone or HOTL-integrated).
assign
Assign a task to a specific role on your team.
ci-repair
Fix CI failures by fetching GitHub Actions logs, dispatching dev to fix, verifying locally, and pushing.