outcome-hypothesis

outcome-hypothesis is a skill for Claude Code from microsoft/hve-core. It costs 103 tokens per session (4,239 once invoked), scanned A, original, MIT.

A method for writing or reviewing a testable prediction about a business result, including who benefits, what will change, and by when.

In plain words
What is it for?
Use it to frame outcomes for an MVP, proof of concept, feature, or technical project, and to check whether an existing outcome is specific, quantified, and based on evidence.
Why use it?
It replaces vague goals or technical deliverables with measurable claims that evidence can confirm or disprove.

Skill for Claude Code ✓ vendor

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to frame outcomes for an MVP, proof of concept, feature, or technical project, and to check whether an existing outcome is specific, quantified, and based on evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/hve-core/outcome-hypothesis
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 outcome-hypothesis
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.

agentmods badge for outcome-hypothesis

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/hve-core/outcome-hypothesis/github.svg)](https://agentmods.dev/skills/microsoft/hve-core/outcome-hypothesis)
Your own site
<a href="https://agentmods.dev/skills/microsoft/hve-core/outcome-hypothesis"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/outcome-hypothesis/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.

agentmods 80×15 button for outcome-hypothesis

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/hve-core/outcome-hypothesis"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/outcome-hypothesis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,239 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 pass 7 Sept 2026
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00103 $0.04239
Opus 5.5 $0.00041 $0.01696
Sonnet 5.5 $0.00021 $0.00848
Haiku 4.5 $0.00010 $0.00424

Measured yesterday against content hash 32e1da540b54, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

outcome-hypothesis 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 yesterday.

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/project-planning/outcome-hypothesis/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Outcome Hypothesis

Goal

Produce an evidence-grounded prediction of what business result will change, for whom, within a specific timeframe, and how leading and lagging indicators will prove or disprove it.

Treat "outcome hypothesis", "business outcome hypothesis", "value hypothesis", and "outcome statement" as equivalent requests.

When to Use

Use this skill to:

  • Frame or sharpen an outcome for a project, initiative, or engagement.
  • Convert a technical idea or deliverable-led proposal into a measurable beneficiary result.
  • Assess whether an existing hypothesis is falsifiable, quantified, baselined, and traceable.
  • Prepare an evidence-based starting point for an outcome conversation.

Do not use it to create a full project plan, write a decision record, produce a status update, or run evidence-free ideation.

Use requirements-author when the outcome is understood and the task is to create or govern a BRD or PRD. Use performance-slo-planner for production SLOs, capacity, latency budgets, and load-test planning.

When an AI or ML intervention materially affects people's access, eligibility, treatment, allocation, or opportunities, continue outcome framing here and initiate the RAI Planner as a separate assessment. AI or ML involvement alone does not trigger this route.

Modes

Resolve the mode before scoring:

  • create: Author a new or revised hypothesis from the available evidence.
  • assess: Evaluate a supplied hypothesis without changing its facts, structure, or prose.

If the request is ambiguous, ask whether the user wants to create a hypothesis or assess an existing one. Assess mode requires the existing hypothesis or outcome document. A revised draft is a separate create or revision request after assessment.

Flow

Use the Outcome-Hypothesis CAUTION in the Outcome-Hypothesis section of the disclaimer-language skill (../../shared/disclaimer-language/SKILL.md) as the only disclaimer source. That skill and section are required before create or assess delivery. Load the CAUTION verbatim when rendering a create-mode document or delivering either mode's result; never store a second literal in this skill or its template. If the required skill or section is missing, unreadable, or unavailable, state that the canonical CAUTION is unavailable. Do not fabricate, invent, or paraphrase a substitute. Stop before delivering a draft or assessment, persistence, or handoff, and name rerunning after the required skill and section are accessible as the condition to resume.

Read the full file on GitHub · 208 lines

Files

What ships with it

2 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.

  1. yesterday Changed 32e1da540b54
  2. 2mo ago First seen · 208 lines · 103 tokens per session scan A 14238389c886

Subscribe to this mod's changes

outcome-hypothesis is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 4,239 once invoked, about $0.0004 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.

Related

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outcome-hypothesis next to 6 skills from other repositories
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