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 agentmods add skills/adambien/airails/continuous-testingnpx skills add AdamBien/airails --skill continuous-testinggit clone --depth 1 https://github.com/AdamBien/airailsWhat 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 | $0.00084 | $0.00430 |
| Opus 5 | $0.00042 | $0.00215 |
| Sonnet 5 | $0.00017 | $0.00086 |
| Haiku 4.5 | $0.00008 | $0.00043 |
Grade A, and why
continuous-testing 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 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.
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
Continuous test-driven development loop using $ARGUMENTS. Apply all rules from /microprofile-server, plus the verification loop below.
Server Lifecycle
- start Quarkus in dev mode (
mvn quarkus:dev) once at the beginning of the session - keep the server running across changes — Quarkus dev mode handles live reload automatically
- only restart the server on major errors (e.g., server crash, port conflict, unrecoverable startup failure)
- do not stop and restart the server between changes
Verification Loop
After every code change, execute the following steps in order:
- Build & Unit Tests — run
mvn testin the service module to compile and execute unit tests - Integration Tests — run
mvn verifyin the service module to execute integration tests (failsafe plugin) - System Tests — before running, verify that the
-stmodule'spom.xmlincludes themaven-failsafe-pluginconfiguration. Then runmvn verifyin the-stmodule against the running server (already running in dev mode)
Rules
- do not skip any step — every change triggers the full loop
- if unit tests fail, stop and fix before proceeding to integration tests
- if integration tests fail, stop and fix before proceeding to system tests
- if system tests fail, stop and fix before continuing with the next change
- report the outcome of each step before proceeding to the next
- treat compilation errors as a failed step — fix before continuing
- keep the loop running until all steps pass or the user intervenes
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.
- 2d ago First seen · 32 lines · 84 tokens per session scan A 773e78bd1c94
continuous-testing is a skill published in the GitHub repository AdamBien/airails (47 stars, last pushed 12d ago), licensed MIT. It adds 84 tokens to every session and 430 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
aws-cloudformation-cloudfront
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching…
aws-cloudformation-cloudwatch
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with…
drawio-logical-diagrams
Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider…
prompt-engineering
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt…
aws-cloudformation-auto-scaling
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack…
aws-cloudformation-bedrock
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation…