A method for linking the outcomes different user types receive to product-usage and north-star metrics, which are the main measures of product success.

Use it to map value for one user type or all user types, define evidence of success, and check whether the relevant metrics show healthy value delivery.

It separates value users actually receive from features merely being shipped. It also reveals when an outcome is assumed or cannot yet be measured.

How VGrss/Acumen configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to VGrss/Acumen. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/VGrss/Acumen/main/.agents/skills/value/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/VGrss/Acumen

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/vgrss/acumen/value/github.svg)](https://agentmods.dev/skills/vgrss/acumen/value)

Your own site

<a href="https://agentmods.dev/skills/vgrss/acumen/value"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/value/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/vgrss/acumen/value"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/value.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,418 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.

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	$0.00038	$0.01418
Opus 5.5	$0.00015	$0.00567
Sonnet 5.5	$0.00008	$0.00284
Haiku 4.5	$0.00004	$0.00142

Measured 2mo ago against content hash c12ca9af07b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

value 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 2mo 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.

.agents/skills/value/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MANDATORY PREPARATION

Invoke /product-thinking — it contains product principles and the Context Gathering Protocol. Follow the protocol before proceeding — if no product context exists yet, you MUST run /teach-acumen first.

Mindset: Value is not what you built — it's the outcome the user got. A feature ships; value is delivered only when the user's situation improves and you can see it in the numbers. If you can't connect a persona to a metric, you don't know if you're delivering value — you're hoping.

Behavior

When called with a specific persona: Build or update the value map for that persona. Focus on what outcome they get, what metric proves it, and whether that metric is actually healthy.

When called without argument: Build the full value map across all personas. Identify where value delivery is proven, assumed, or unmeasured.

Research Process

Read .acumen.md for product context — north star metric, strategy, business model, success definitions
Read .acumen/personas.md for behavioral personas — job to be done, success criteria, feedback signals
Read .acumen/features.md for feature inventory — what exists, who it serves, what's measured
Read .acumen/value-chain.md for end-to-end workflow — where the product sits, what it owns
Check .acumen/sources.md for data sources — pull real metrics if available
If data sources are configured, pull actual numbers. If not, ask the user to share what they have.

For each persona, answer:

What value do they get? Not features — outcomes. "Saves 4 hours/week on report generation" not "has a report builder."
What's the north star metric for this persona? The single number that best captures whether this persona is getting value. This may be the product-wide north star or a persona-specific proxy.
What are the usage metrics that lead to value? The behavioral signals that predict value delivery — activation events, frequency patterns, depth of engagement, workflow completion rates.
What's the proof? Current metric values, trends, cohort data. If no data exists, say so — an unmeasured value claim is an assumption.
What's the value gap? Where is the persona not getting the value they expect? Where does the product promise more than it delivers?

Read the full file on GitHub · 118 lines

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.

2mo ago First seen · 118 lines · 38 tokens per session scan A c12ca9af07b7

Subscribe to this mod's changes

value is a skill published in the GitHub repository VGrss/Acumen (12 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,418 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 Marketing and SEO

value next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
valueVGrss/Acumen	Skill	12	3mo ago	Apache-2.0	A	38	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	today	MIT	A	103	Claude Code

Acumen: Skill for Claude Code