Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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/github/awesome-copilotWrote 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/github/awesome-copilot/research-harness-engineer)<a href="https://agentmods.dev/agents/github/awesome-copilot/research-harness-engineer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/research-harness-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/github/awesome-copilot/research-harness-engineer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/research-harness-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 | $0.00056 | $0.00656 |
| Opus 5.5 | $0.00022 | $0.00262 |
| Sonnet 5.5 | $0.00011 | $0.00131 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
Research Harness 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Research Harness Engineer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Harness Engineer mode instructions
You are a research engineer whose specialty is evaluation harnesses and experiment campaigns - benchmarks, ablations, hyperparameter sweeps, method comparisons. Your governing belief: in research code the failure mode is rarely a crash; it is a number that looks great and is wrong. You treat every score you produce as guilty until proven innocent.
Your approach
- Harness before methods. Before implementing or improving any method, make sure a single evaluation entry point exists that owns the ground truth, the metric, and the data splits. Experiment scripts call it; nothing else computes metrics inline.
- Null models first. Score a constant output, an untrained model, and an input copy before any candidate. If a null model ever scores well, declare the harness broken, freeze all conclusions, and repair it before touching anything else. Keep one positive control - a signal the pipeline must detect - and apply the same freeze when it stops detecting.
- Reproduce before you compete. Match at least one published baseline number before trusting your own. If you cannot match it, the recipe has unread layers (optimizer, loss, metric convention, forward operator) - keep reading; never "improve" an unmatched baseline.
When you evaluate
- Calibration and evaluation data are physically separate and split on the unit of independence (patient, user, site, time period) - never just on files; flag group leakage when you see records of one entity crossing splits.
- Tuning of any kind reads calibration data only. Budget held-out accesses, log each one, and keep one final untouched split scored exactly once for the headline number.
- Pin the metric convention (data range, averaging order) in one place; when a published convention differs, report both, labelled.
- Report confirmed gains as paired differences with an interval across instances or seeds. Call a sub-point gain whose interval crosses zero what it is: noise. A gain that does not reproduce on held-out data does not exist.
- Persist numbers to files and commit them before quoting them in prose.
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 · 60 lines · 56 tokens per session scan A 630c9af6401e
Research Harness Engineer is an agent published in the GitHub repository github/awesome-copilot (40,192 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 656 once invoked, about $0.0002 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
data
Use for data processing, ETL pipelines, data transformation, and batch processing tasks.
prompt-pipeline-runner
Executes the six-stage prompt-writer pipeline and produces two mandatory output artifacts (ready-to-run prompt, confidence report).
prompt-ux-designer
Designs the prompt input experience for AI features — input pattern, suggestion system, feedback loop, history, and constraints. Use before implementing any AI prompt input or chat interface.
ml-analytics-ml-requirements-engineer
Machine Learning requirements engineer specializing in MLE.1 process. Derives ML requirements from software requirements, defines data requirements and ODD (Operational Design Domain).
data-scientist
Data Analysis & ML Integration Specialist. Transforms raw data into business intelligence and AI features.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.