Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/quay/ai-helpersnpx agentmods add skills/quay/ai-helpers/controllerWrote 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/quay/ai-helpers/controller)<a href="https://agentmods.dev/skills/quay/ai-helpers/controller"><img src="https://agentmods.dev/badge/skills/quay/ai-helpers/controller.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 | $0.00041 | $0.01586 |
| Opus 5 | $0.00020 | $0.00793 |
| Sonnet 5 | $0.00008 | $0.00317 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
controller scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl *) How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quay Bugfix Controller
You manage a 9-phase bug-fix workflow with confidence-based gating. After each phase, read the confidence assessment from the phase artifact and use it to decide whether to advance, comment, or escalate.
Session Bootstrap
On first run, ensure Lola plugins are installed:
bash .claude/scripts/session-setup.sh
Phases
-
Assess — the
assessskill Read the bug report, summarize understanding, identify gaps, propose a plan. -
Reproduce — the
reproduceskill Confirm the bug exists by reproducing it in a controlled environment. -
Diagnose — the
diagnoseskill Trace the root cause through code analysis, git history, and hypothesis testing. -
Fix — the
/dev:codeskill (from dev plugin) Read the root cause analysis, create a feature branch, then implement the minimal fix using/dev:code. Write implementation notes afterward. -
Test — the
testskill Write regression tests, run the full suite, and verify the fix holds. -
Review — the
reviewskill Critically evaluate the fix and tests — look for gaps, regressions, and missed edge cases. -
Document — the
documentskill Create release notes, changelog entries, JIRA updates, and PR description. -
PR — the
/dev:prskill (from dev plugin), then/dev:pollCreate a pull request using/dev:pr, then start CI polling with/dev:poll <PR#>. -
Summary — the
summaryskill Scan all artifacts and present a synthesized summary.
Confidence-Based Gating
Confidence Assessment Format
Each phase skill writes a ## Confidence Assessment section at the bottom
of its artifact:
## Confidence Assessment
- **Level**: high | medium | low
- **Score**: <0-100 integer>
- **Score rationale**: <1-2 sentences>
- **Open questions**: <bullet list, or "None">
Confidence Flow
After each phase completes, read the confidence level from the artifact:
| Confidence | Threshold | Action |
|---|---|---|
| High | >=90% | Advance to next phase silently |
| Medium | 70-89% | Post JIRA comment with findings and open questions, then advance |
| Low | <70% | Post JIRA comment, then stop and escalate via AskUserQuestion |
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.
- 2d ago First seen · 208 lines · 41 tokens per session scan A 0c01e562d0a6
controller is a skill published in the GitHub repository quay/ai-helpers (3 stars, last pushed 19d ago), licensed MIT. It adds 41 tokens to every session and 1,586 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
cli-e2e
Write, modify, or debug Docker-based Composio CLI end-to-end tests under ts/e2e-tests/cli, including binary invocation, fixture isolation, output assertions, and package manifests. Use for CLI E2E test suites only; use cli-command for CLI source implementation.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.