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 dataopsgit 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/dataops)<a href="https://agentmods.dev/skills/microsoft/hve-core/dataops"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/dataops/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/dataops"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/dataops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, 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 30 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
- medium Prompt Injection · line 30 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
- medium Prompt Injection · line 56 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 | $0.00062 | $0.01384 |
| Opus 5.5 | $0.00025 | $0.00554 |
| Sonnet 5.5 | $0.00012 | $0.00277 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
dataops 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.
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.
DataOps Reference Pack
Goal
Ground pipeline and test generation in the Microsoft CSE engineering playbook so that data tier semantics, validation placement, recovery invariants, and DS/MLOps test technique are applied consistently and attributed accurately.
Inputs
- The pipeline, transformation, validation, or test work under discussion
- The tier of each dataset involved, when the consuming workflow records one
- Existing test layout, package structure, and data-access boundaries
- A data classification produced elsewhere, when sensitivity matters
Reference index
Read only the reference that matches the active concern.
| Reference | Read this when |
|---|---|
| data-tiers-and-pipeline-invariants.md | Assigning tier meaning, placing validation, routing malformed records, or asserting replay, idempotency, testability, source-control, and configuration invariants |
| testing-data-science-and-mlops.md | Writing or reviewing tests for data loading, transformation, model load or predict, data validation, or model robustness |
| validation-drift-and-observability.md | Distinguishing data validation from drift detection, choosing remediation, or deciding which data and model signals matter |
| synthetic-data-operation-contract.md | Validating synthetic-data preflight authority, field lineage, conditional subgroup evidence, result linkage, or guarded local replacement |
| provenance.md | Confirming what is upstream guidance, what is HVE Core derivation, and where upstream is silent |
What ships with it
15 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/synthetic-data-operation-v1.schema.json 9.2 KB
- pyproject.toml 681 B
- references/data-tiers-and-pipeline-invariants.md 10 KB
- references/provenance.md 11 KB
- references/synthetic-data-operation-contract.md 6.4 KB
- references/testing-data-science-and-mlops.md 8.8 KB
- references/validation-drift-and-observability.md 7.2 KB
- scripts/synthetic_data_operation.py 21 KB runs code
- tests/corpus/0_empty 0 B
- tests/corpus/1_empty_object 2 B
- tests/corpus/2_non_object_json 2 B
- tests/corpus/3_minimal_preflight 27 B
- tests/fuzz_harness.py 1.2 KB runs code
- tests/test_synthetic_data_operation.py 24 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.
- 1mo ago Changed · +10 lines 5e777c5810e0
- 1mo ago First seen · 83 lines · 62 tokens per session scan A 6fbf7af2a326
dataops is a skill published in the GitHub repository microsoft/hve-core (1,529 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,384 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.