HVE Core is a collection of agents, prompts, coding instructions, and skills for building repeatable software-development workflows with GitHub Copilot. It is intended for individuals and teams that want structured AI-assisted research, planning, implementation, and review, while the catalogue entries provide many of its reusable workflow components.
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 skills add microsoft/hve-core --skill evaluation-designgit clone --depth 1 https://github.com/microsoft/hve-coreWrote 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/microsoft/hve-core/evaluation-design)<a href="https://agentmods.dev/skills/microsoft/hve-core/evaluation-design"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/evaluation-design/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/skills/microsoft/hve-core/evaluation-design"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/evaluation-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 77 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00058 | $0.01583 |
| Opus 5.5 | $0.00023 | $0.00633 |
| Sonnet 5.5 | $0.00012 | $0.00317 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
evaluation-design 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 22d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Evaluation Dataset Design
Goal
Produce an evaluation dataset and its supporting documentation that measure whether an AI system does its job, refuses what it should refuse, and behaves acceptably under pressure. The dataset is a durable customer artifact, so its scope, balance, and rationale are recorded rather than implied.
Flow
- Run the scoping interview from the interview reference. Ask one question at a time and wait for the answer; do not batch the interview into a single prompt.
- Present a structured summary of what you heard and obtain explicit confirmation before generating anything.
- Derive the difficulty distribution from the confirmed scope, adjusting the defaults when the system's risk profile warrants it.
- Generate the dataset against the contract template in both machine-readable forms.
- Validate the JSON and CSV pair with
uv run python scripts/validate_evaluation_dataset.py --json <dataset.json> --csv <dataset.csv>before treating machine facts as evidence. - Walk a representative sample through the user, gather consolidated feedback, and revise before finalizing the full set.
- Produce one sectioned evaluation guide containing curation notes, metric selection with rationale, and tooling recommendations.
- Route every durable write through the workstream's scan gate before it lands in a customer location.
Current Evaluator Research
Activate rpi-research only when the confirmed evaluation job needs current evaluator names, availability, preview state, platform compatibility, or prerequisites that the authoritative live source must establish. Provide the platform and product-version scope, system context, tool-use pattern, metric-plan decision, source and date boundaries, evidence criteria, non-goals, and supplied evaluation evidence. Use analysis or comparison mode and the default Research evidence root.
Read the completed primary artifact before naming current evaluators or committing a platform-backed metric plan. Record the authoritative source and retrieval date. Research establishes current facts; this skill still selects metrics from the confirmed system grounding, tool use, risk profile, operating constraints, and evaluation cadence.
What ships with it
18 files 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.
- assets/evaluation-dataset-v1.schema.json 3.7 KB
- pyproject.toml 694 B
- references/evaluation-interview-and-review.md 6.9 KB
- references/metric-selection-and-tooling.md 5.7 KB
- references/provenance.md 3.6 KB
- scripts/validate_evaluation_dataset.py 18 KB runs code
- templates/evaluation-dataset-contract.md 12 KB
- templates/supporting-documents.md 4.5 KB
- tests/corpus/0_valid_csv_row 230 B
- tests/corpus/1_header_only 114 B
- tests/corpus/2_short_row 128 B
- tests/corpus/3_header_mismatch 31 B
- tests/corpus/4_bad_quoting 190 B
- tests/corpus/5_valid_json 81 B
- tests/corpus/README.md 1.4 KB
- tests/fuzz_harness.py 1.6 KB runs code
- tests/test_validate_evaluation_dataset.py 28 KB runs code
- uv.lock 102 KB
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.
- 22d ago Changed · +8 lines 50310d88e316
- 23d ago Changed · +3 lines ea59595a2653
- 1mo ago First seen · 88 lines · 58 tokens per session scan A 7dcef9291bc4
evaluation-design is a skill published in the GitHub repository microsoft/hve-core (1,529 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,583 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 2026-08-30.
Other skills, from other repositories
cli-eval
Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
validate
Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validatepipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.