attune-ai 6.5.5

AI-powered developer workflows for Claude with cost optimization, multi-agent orchestration, and workflow automation.

Attune AI

Multi-agent developer workflows for Claude Code.

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18 multi-agent workflows, 14 auto-triggering Claude Code skills, and 36 MCP tools — specialist teams of 2–6 Claude subagents that review your code, surface vulnerabilities, generate tests, and plan refactors. The same system doubles as the authoring and assistance toolkit for building and maintaining knowledge bases at scale.

Managing and creating help content and docs? That's attune-gui — a dedicated Living Docs dashboard wrapping attune-rag, attune-help, and attune-author in a single UI. attune-ai is the developer workflow hub; attune-gui is the docs hub.

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Ecosystem

Package	Role	Install
attune-ai	Developer workflow hub (this package)	pip install attune-ai
attune-gui	Living Docs dashboard — create, manage, search help content	standalone app
attune-rag	RAG pipeline (core dep of attune-ai, v0.1.11+)	bundled
attune-author	Help content authoring, staleness detection	pip install 'attune-ai[author]'
attune-help	Progressive-depth template runtime	pip install attune-help

attune-rag ships as a core dependency of attune-ai (v0.1.11, >=0.1.5,<0.2). attune-help is standalone — not pulled in by a standard attune-ai install, but available as an optional corpus for attune-rag via pip install 'attune-rag[attune-help]'.

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How It Works

1. Skills trigger automatically

Say what you need in Claude Code and the right skill activates:

"review my code"        → code-quality skill
"scan for vulns"        → security-audit skill
"generate tests"        → smart-test skill
"plan this feature"     → planning skill

No command to remember. Claude reads your intent and picks the skill. Each skill runs a specialist multi-agent team, not a single prompt.

2. Multi-agent teams, not single prompts

Every workflow dispatches 2–6 subagents in parallel. Each reads your code with Read, Glob, and Grep. An orchestrator synthesizes their findings into a unified result:

security-audit → vuln-scanner + secret-detector + auth-reviewer + remediation-planner
code-review    → security + quality + perf + architect
test-gen       → identifier + designer + writer

Subagents are assigned models by task complexity — Opus for deep reasoning, Sonnet for analysis, Haiku for fast scanning — keeping cost proportional to value.

3. Socratic before execution

Workflows ask questions before executing, not after. The spec workflow brainstorms, then plans, then executes. planning clarifies scope before writing a line of code. This eliminates the most common failure mode: confidently solving the wrong problem.

4. RAG-grounded generation

attune-rag (core dep) grounds LLM generation in retrieved corpus passages and enforces citation-per-claim, cutting hallucination from 46.7% → 6.7% on the benchmark set. Retrieved passages are wrapped in sentinel tags to prevent prompt injection. The Claude provider automatically caches the stable RAG context prefix, eliminating repeated token costs across calls.

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Get Started in 60 Seconds

Plugin (works standalone)

claude plugin marketplace add Smart-AI-Memory/attune-ai
claude plugin install attune-ai@attune-ai

Then say "what can attune do?" in Claude Code.

Add Python Package (unlocks CLI + MCP)

pip install 'attune-ai[developer]'

What Each Layer Adds

Capability	Plugin only	Plugin + pip
14 auto-triggering skills	Yes	Yes
Security hooks	Yes	Yes
Prompt-based analysis	Yes	Yes
36 MCP tools	--	Yes
attune CLI	--	Yes
Multi-agent workflows	--	Yes
Help system maintenance	--	Yes
CI/CD automation	--	Yes

Note: Skills use your Claude subscription at no extra cost. CLI and MCP tools make direct Anthropic API calls — API key required. See API Mode.

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Cheat Sheet

Input	What Happens
"what can attune do?"	Auto-triggers attune-hub — guided discovery
"build this feature from scratch"	Auto-triggers spec — brainstorm, plan, execute
"review my code"	Auto-triggers code-quality skill
"scan for vulnerabilities"	Auto-triggers security-audit skill
"generate tests for src/"	Auto-triggers smart-test skill
"fix failing tests"	Auto-triggers fix-test skill
"predict bugs"	Auto-triggers bug-predict skill
"generate docs"	Auto-triggers doc-gen skill
"plan this feature"	Auto-triggers planning skill
"refactor this module"	Auto-triggers refactor-plan skill
"prepare a release"	Auto-triggers release-prep skill
"tell me more"	Auto-triggers coach — progressive depth help
"run all workflows"	Auto-triggers workflow-orchestration skill

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Workflows

Workflow	Agents	What It Does
code-review	security, quality, perf, architect	4-perspective code review
security-audit	vuln-scanner, secret-detector, auth-reviewer, remediation	Finds vulnerabilities and generates fix plans
deep-review	security, quality, test-gap	Multi-pass deep analysis
perf-audit	complexity, bottleneck, optimization	Identifies bottlenecks and O(n²) patterns
bug-predict	pattern-scanner, risk-correlator, prevention	Predicts likely failure points
health-check	dynamic team (2–6)	Project health across tests, deps, lint, CI, docs, security
test-gen	identifier, designer, writer	Writes pytest code for untested functions
test-audit	coverage, gap-analyzer, planner	Audits coverage and prioritizes gaps
doc-gen	outline, content, polish	Generates documentation from source
doc-audit	staleness, accuracy, gap-finder	Finds stale docs and drift
dependency-check	inventory, update-advisor	Audits outdated packages and advisories
refactor-plan	debt-scanner, impact, plan-generator	Plans large-scale refactors
simplify-code	complexity, simplification, safety	Proposes simplifications with safety review
release-prep	health, security, changelog, assessor	Go/no-go readiness check
doc-orchestrator	inventory, outline, content, polish	Full-project documentation
secure-release	security, health, dep-auditor, gater	Release pipeline with risk scoring
research-synthesis	summarizer, pattern-analyst, writer	Multi-source research synthesis

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MCP Tools

36 tools organized into 4 categories:

Workflow (20)

security_audit code_review bug_predict performance_audit refactor_plan simplify_code deep_review test_generation test_audit test_gen_parallel doc_gen doc_audit doc_orchestrator release_prep health_check dependency_check secure_release research_synthesis analyze_batch analyze_image

Help (5)

help_lookup help_init help_status help_update help_maintain

Memory (4)

memory_store memory_retrieve memory_search memory_forget

Utility (7)

auth_status auth_recommend telemetry_stats context_get context_set attune_get_level attune_set_level

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Accuracy & Faithfulness

RAG grounding — hallucination down 46.7% → 6.7%

Measured on a 15-query golden set with retrieval held constant:

Prompt variant	Hallucination rate	Mean faithfulness
baseline (no grounding rule)	46.67%	0.938
strict ("answer only from context")	26.67%	0.968
citation (shipped default)	6.67%	0.996

The gain comes from the prompting contract (citation-per-claim), not from retrieval. Full methodology:

• docs/rag/faithfulness-decision-2026-04-19.md
• docs/rag/ab-report-2026-04-19.json

Help resolver — 48/48 benchmark queries pass at P@1

Bucket	Count	P@1	Notes
easy	22	22/22 (100%)	feature-name synonyms
medium	26	26/26 (100%)	paraphrases + industry terminology
hard	4	0/4 (XFAIL)	shared-tag collisions — structural ambiguity

• tests/unit/help/fixtures/golden_queries.yaml

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Why Attune?

	Attune AI	Static Docs	Agent Frameworks	Coding CLIs
Ready-to-use workflows	18 built-in	None	Build from scratch	None
Multi-agent teams	2–6 agents per workflow	None	Yes	No
MCP integration	36 native tools	None	No	No
Auto-triggering skills	14 skills, natural language	None	None	None
Socratic discovery	Questions before execution	None	None	None
Portable security hooks	PreToolUse + PostToolUse	None	No	No

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Installation Options

# Recommended (agents, memory, RAG)
pip install 'attune-ai[developer]'

# Minimal (CLI + workflows + RAG)
pip install attune-ai

# With help authoring (generate / maintain .help/ templates)
pip install 'attune-ai[author]'

# All features
pip install 'attune-ai[all]'

# Development (contributing)
git clone https://github.com/Smart-AI-Memory/attune-ai.git
cd attune-ai && pip install -e '.[dev]'

The [rag] extra is a no-op alias kept for backward compatibility — attune-rag is now a core dependency included in every install.

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API Mode

export ANTHROPIC_API_KEY="sk-ant-..."     # Required
export REDIS_URL="redis://localhost:6379"  # Optional

Model Routing

Model	Agents	Rationale
Opus	security, vuln, architect	Deep reasoning
Sonnet	quality, plan, research	Balanced analysis
Haiku	complexity, lint, coverage	Fast scanning

export ATTUNE_AGENT_MODEL_SECURITY=sonnet  # Save cost
export ATTUNE_AGENT_MODEL_DEFAULT=opus     # Max quality

Budget Controls

Depth	Budget	Use Case
quick	$0.50	Fast checks
standard	$2.00	Normal analysis (default)
deep	$5.00	Thorough multi-pass review

export ATTUNE_MAX_BUDGET_USD=10.0  # Override

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Security

• Path traversal protection on all file operations (CWE-22)
• Memory ownership checks (created_by validation)
• MCP rate limiting (60 calls/min per tool)
• Hook import restriction (attune.* modules only)
• PreToolUse security guard (blocks eval/exec, path traversal)
• Prompt input sanitization (backticks, control chars, truncation)
• PII scrubbing in telemetry
• Automated security scanning (CodeQL, bandit, detect-secrets)

See SECURITY.md for vulnerability reporting and full security details.

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Migration

attune-help and attune-author have moved to their own marketplace at Smart-AI-Memory/attune-docs. If you previously installed either from the attune-ai marketplace:

1. /plugin marketplace add Smart-AI-Memory/attune-docs
   

2. /plugin uninstall attune-help@attune-ai
   /plugin uninstall attune-author@attune-ai
   

3. /plugin install attune-help@attune-docs
   /plugin install attune-author@attune-docs
   

New users: add Smart-AI-Memory/attune-docs directly.

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Links

• Full Documentation
• Plugin Setup
• attune-gui — Living Docs dashboard
• GitHub Repository

Apache License 2.0 — Free and open source.

If you find Attune useful, give it a star — it helps others discover the project.

Acknowledgments

• Anthropic — For Claude AI, the Model Context Protocol, and the Agent SDK patterns behind the multi-agent orchestration layer
• Boris Cherny — Creator of Claude Code, whose workflow posts validated Attune's plan-first, multi-agent approach
• Affaan Mustafa — For battle-tested Claude Code configurations that inspired the hook system

View Full Acknowledgements

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Built by Patrick Roebuck using Claude Code.