A structured study of whether available data and technical evidence can support a proposed data or machine-learning outcome. It records findings, assumptions, risks, evidence, and how the study changes over time.

It helps assess data and technical feasibility, track evidence and dependencies, record decisions, and prepare information for later functional planning.

It gives teams a durable way to separate confirmed facts from assumptions and gaps before committing to a project.

feasibility — Skill for Claude Code by microsoft/hve-core

About the project

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.

microsoft/hve-core · 1,606 stars · on GitHub

Install

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.

Any agent

npx skills add microsoft/hve-core --skill feasibility

Clone the repo

git clone --depth 1 https://github.com/microsoft/hve-core

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/microsoft/hve-core/feasibility/github.svg)](https://agentmods.dev/skills/microsoft/hve-core/feasibility)

Your own site

<a href="https://agentmods.dev/skills/microsoft/hve-core/feasibility"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/feasibility/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/microsoft/hve-core/feasibility"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/feasibility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,594 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 2 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 69
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 75
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

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00054	$0.01594
Opus 5.5	$0.00022	$0.00638
Sonnet 5.5	$0.00011	$0.00319
Haiku 4.5	$0.00005	$0.00159

Measured 1mo ago against content hash a9823bb81f86, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

feasibility 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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/validate_feasibility.py, tests/fuzz_harness.py, tests/test_validate_feasibility.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.github/skills/data-science-engineering/feasibility/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data and ML Feasibility Workflow

Goal

Produce one durable Markdown feasibility study that remains useful to people and can be consumed later by a Functional Planner. One constrained YAML block owns machine facts; narrative sections preserve evidence, interpretation, and context.

Flow

Confirm the proposed outcome, decision boundary, study scope, and durable output path.
Allocate UUIDv4 URNs for the study concept, study revision, each item concept, each item revision, and each relation. Never derive identity from a title, class, path, or content.
Capture candidate capabilities, constraints, assumptions, findings, risks, dependencies, decisions, evidence, gaps, and non-goals. Preserve uncertainty and source-authored criteria without promoting every item to a requirement.
Record lifecycle and provenance. Reclassification keeps conceptual identity and creates a new revision. Split, merge, derivation, withdrawal, and supersession retain explicit lineage.
Write or update the single named FEASIBILITY-STUDY-INTERCHANGE YAML block. Narrative can explain machine facts but cannot redefine them.
Validate constrained YAML, JSON Schema 2020-12 structure, semantic closure, revision lineage, tombstones, and narrative anchors with scripts/validate_feasibility.py.
Present the recommendation and unresolved review gaps. Preserve the study as read-only evidence for downstream consumers.
After the study is final, emit the sibling feasibility-to-PRD handoff described in feasibility-to-prd-handoff.md. Regenerate it after any material study revision, then validate both artifacts with scripts/validate_feasibility.py <study.md> --handoff <handoff.yml>.

Inputs

Problem definition, desired outcome, and decision the study must support
Data access, discovery, architecture, exploration, preprocessing, and experiment evidence
Source-authored acceptance criteria, when known
Risk, privacy, Responsible AI, performance, and operational evidence
Prior study revision and item identity registry, when revising an existing study

Read the full file on GitHub · 87 lines

Files

What ships with it

20 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.

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.

1mo ago Changed · +1 lines a9823bb81f86
2mo ago First seen · 86 lines · 54 tokens per session scan A d6e65fce6e80

Subscribe to this mod's changes

feasibility is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 1,594 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.

Analytics and metrics Data and AI

feasibility next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
feasibilitymicrosoft/hve-core	Skill	1.6k	yesterday	MIT	A	54	Claude Code
agent-platform-model-registrygoogle/skills	Skill	24k	2d ago	Apache-2.0	A	60	Claude Code, Codex
foundry-config-setupmicrosoft/agent-framework	Skill	15k	yesterday	MIT	A	65	Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills	Skill	24k	2d ago	Apache-2.0	A	132	Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleep	Skill	19k	yesterday	MIT	A	35	Claude Code
nemo-automodel-launcher-configNVIDIA/skills	Skill	3.9k	2d ago	Apache-2.0	A	30	Claude Code
finetuningmicrosoft/skills	Skill	3.2k	3d ago	MIT	A	132	Claude Code