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.
git 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/agents/smart-ai-memory/attune-ai/release-prep)<a href="https://agentmods.dev/agents/smart-ai-memory/attune-ai/release-prep"><img src="https://agentmods.dev/badge/agents/smart-ai-memory/attune-ai/release-prep/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/agents/smart-ai-memory/attune-ai/release-prep"><img src="https://agentmods.dev/badge/agents/smart-ai-memory/attune-ai/release-prep.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 | $0.00009 | $0.00214 |
| Opus 5.5 | $0.00004 | $0.00086 |
| Sonnet 5.5 | $0.00002 | $0.00043 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
release-prep 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.
What it actually says
Release Prep Agent Team
A 4-agent team for automated release readiness assessment.
Agents
| Agent | Role | Tools |
|---|---|---|
| Security Auditor | Run bandit, classify vulnerabilities | bandit, SAST |
| Test Coverage | Run pytest --cov, parse coverage | pytest, coverage |
| Code Quality | Run ruff, check complexity | ruff, radon |
| Documentation | Check docstring coverage | interrogate |
Execution
- Coordination: Parallel execution with result aggregation
- Tier Strategy: Progressive (CHEAP -> CAPABLE -> PREMIUM)
- Output:
ReleaseReadinessReportwith go/no-go decision
Usage
/agent release-prep
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 · 39 lines · 9 tokens per session scan A 1c06b03db893
release-prep is an agent published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 9 tokens to every session and 214 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
tdd-guide
Test-Driven Development specialist enforcing write-tests-first methodology. Use PROACTIVELY when writing new features, fixing bugs, or refactoring code. Ensures 80%+ test coverage.
pr-test-analyzer
Review pull request test coverage quality and completeness, with emphasis on behavioral coverage and real bug prevention.
test-engineer
Specialized in Python testing with pytest. Expert in running tests, fixing failures, and ensuring test coverage. Use when running tests, fixing failing tests, or validating test coverage. Does NOT write new tests - focuses on running and fixing.
test-automator
Create comprehensive test suites with unit, integration, and e2e tests. Sets up CI pipelines, mocking strategies, and test data. Use PROACTIVELY for test coverage improvement or test automation setup.
test-architect
Generates and hardens the test suite — unit AND end-to-end — for a change, runs it, and validates the behavior actually works (not just that assertions pass). Use as the SAFETY GATE before any autonomous fix is approved or merged: no adequate tests, no green light. The thing that makes the autonomous loops safe.
cr-tests
Reviews the changed test files of a supplied diff for tests that cannot catch the defects they target. Use only when dispatched by the code-review skill.