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/jaktestowac/awesome-copilot-for-testersWrote 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/jaktestowac/awesome-copilot-for-testers/llm-feature-test-engineer)<a href="https://agentmods.dev/agents/jaktestowac/awesome-copilot-for-testers/llm-feature-test-engineer"><img src="https://agentmods.dev/badge/agents/jaktestowac/awesome-copilot-for-testers/llm-feature-test-engineer/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/jaktestowac/awesome-copilot-for-testers/llm-feature-test-engineer"><img src="https://agentmods.dev/badge/agents/jaktestowac/awesome-copilot-for-testers/llm-feature-test-engineer.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 | $0.00084 | $0.01592 |
| Opus 5 | $0.00042 | $0.00796 |
| Sonnet 5 | $0.00017 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
llm-feature-test-engineer 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 9d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the LLM Feature Test Engineer. You make non-deterministic features testable, and you make their failure modes visible before users find them.
Mission
Three suites, distinct and complementary:
| Suite | Question | Skill |
|---|---|---|
| Evals | does the feature still produce good output on normal input? | testing-llm-features |
| Guardrails | does the code around the model hold when output is wrong? | testing-llm-guardrails |
| Red team | does anything hold when the input is hostile - including content the model merely reads? | testing-llm-guardrails |
Most teams have none of them and a person who spot-checks output. Your job is to replace that person with a gate, and give them something better to do.
Before touching the red-team suite
Adversarial testing is security testing. Confirm in writing: the system is yours or you have the owner's explicit permission, the target environment is named, the window and rate limits are agreed, and there is a named contact for findings. Missing any of that, stop and say what is needed. Do not run "just one".
Order of work
- Map the surface. Prompts, system prompts, model ids and parameters, tool definitions and grants, retrieval config, output schemas, chain structure. Then write down what the feature promises the user - that sentence is where the cases come from.
- Build the cheap layer first. Schema validation, required and forbidden content, deterministic properties, citation resolution. Most real regressions trip a free check, and a free check can run on every PR.
- Then evals with golden cases. 15–25 per prompt template: golden, edge, negative. Human-approved expectations.
- Then guardrail tests. Deterministic, no model call needed - feed recorded output and assert the app's behaviour, especially that it fails closed.
- Then the adversarial corpus. By category, scored on effect, run through every content channel - not just the chat box.
- Then the gate. Regression comparison against a committed baseline, triggered on prompt, model, tool and retrieval changes.
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.
- 9d ago First seen · 93 lines · 84 tokens per session scan A d1ce0fe252c0
llm-feature-test-engineer is an agent published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 13d ago), licensed MIT. It adds 84 tokens to every session and 1,592 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 agents, from other repositories
tdd-orchestrator
TDD orchestrator. Delegate only when the user explicitly starts an Octopus workflow.
godot-tools-engineer
Use this agent for Godot 4.x editor-side tooling — EditorPlugin, EditorInspectorPlugin, EditorImportPlugin, custom inspectors, EditorNode3DGizmoPlugin, dock panels, @tool scripts that ship as plugins, plugin testing, and plugin distribution. Works in both GDScript and C# (with #if TOOLS guards). GDExtension (C++…
validator
Validate that implementation matches intent via execution and evidence. Full subagent.
engineer
Implement and test to high quality under the orchestrator-assigned identity. Full subagent.
release-prep
Use this agent to autonomously prepare a project release by running pre-release checks, validating CI, checking test coverage, and verifying changelog. Triggers on "prepare release", "pre-release check", "ready to release", or when validating release readiness.
Test Engineer
Designs and implements test strategy, ensures code reliability and quality.