value-realization

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value-realization

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Analyze whether end users will discover clear value in product ideas.

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# Value Realization

Judge whether something actually produces value — not by feel, but by landing it in a concrete scenario, telling which conclusions rest on evidence and which are still assumption, and seeing how deep each direction has been probed.

**Version**: 2.0.0 ｜ **Type**: Analytical framework ｜ **Status**: ready ✅

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## What it solves

When judging whether something has value, the real difficulty is rarely "did nothing." Most people took action; the problem lies in the **weighting** of that action — they did something, but it went off-target or stayed shallow, and they didn't know it.

The most common shapes:

- **Verified, but the verification target was off.** Starting out on a product, you go talk to friends nearby; friends are happy to cheer you on and give positive feedback, so it sounds "verified." But friends aren't in the scenario you're anchoring to, so what you get back is a position-skewed signal. The problem isn't whether you talked, but whether who you talked to matches the target.
- **Named a scenario, but the scenario is too fuzzy.** You'll say "for people who do marketing," but the precision stops there, short of "who, in what state, stuck on what problem." Scenario is 30%, features 70% — not zero scenario, just imbalanced.
- **Copied a case, but copied the surface and ignored time.** Seeing someone succeed, you lift the actions without restoring the preconditions that made it hold. More insidious is time — a product that worked two years ago looks like the same conditions today, but competitors, platforms, and user habits have long since shifted; copying it now might not work.
- **Has a judgment, but can't tell evidence from assumption.** "Looks like it holds," and drilling down, most of it hangs on assumption — a pretty but empty judgment, and the person usually doesn't know it's empty.

None of these are black-and-white failures; they're all a matter of how much weight each carries. The real world is almost all half-finished plus imbalanced weighting, so judging value can't be a "holds / doesn't hold" binary verdict — it has to express degree, and make visible where things are fuzzy and how fuzzy.

## The premise it stands on

One core judgment runs throughout: **value is not a fixed thing the product owns unilaterally and waits to explain and hand to the user, but a relationship co-produced by both sides' states in a specific scenario.**

So the question "does it have value" is itself malformed. The right question is "in which configuration — who, what state, what conditions — does it produce a beneficial property." Find that configuration and you've found value; fail to, and everything you say is empty.

From this follow a few more points: one person can't compute complete value (the other half is with real users); value has degree, it's not a switch; conditions carry time and drift out of validity as the environment shifts. Together these decide what this skill looks like.

## How it helps you

Around the center of gravity — "in which scenario is value produced" — it takes a vague "is this good" apart along four mutually independent dimensions:

- **Value Scenario** — in which relationship configuration is value produced? (the center of gravity)
- **Value Conditions** — on what basis does it hold, and are the conditions supporting it still present?
- **Value Timeline** — when is it produced, immediate or requiring accumulation?
- **Value Delivery** — can it be perceived low-loss, or is it buried in the backend where it can't get out?

Each dimension gives two judgments: **direction** (does it hold, 🔴🟡🟢) and **solidity** (how much rests on evidence, 🟩🟨🟧🟥). The two are given separately, because a green light might be "confirmed to hold" or merely "assumed to hold" — a big difference, and exactly the thing most worth watching.

It's not an evaluation form you run through the four dimensions once and hand in a report — it's a tool for **sustained conversation**, with people and with AI. Each round talks the target more precise and raises solidity from empty/thin to half/solid, closing in round by round on a value or business opportunity that genuinely stands up. Discovering value comes from sharp falsification and probing, not from agreement and paraphrase.

## It cures the very disease it most wants to prevent

This skill set its own target using its own method: not knowing which target you're firing at, and producing something fuzzy — that's exactly the disease it treats. So it requires every analysis to state clearly "which target I'm firing at, how far I've probed, how much rests on evidence, who to ask next."

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## Applies and doesn't apply

**Applies**: judging whether a value proposition genuinely holds, value assessment of a product / feature / copy / scenario, adoption and retention problems, positioning problems, mapping features to concrete scenarios.

**Doesn't apply (use caution)**: enterprise software where the decision-maker ≠ the end user, monopoly products (the user has no choice), products whose value is inherently delayed and whose conditions are extremely stable.

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## Reference files

- `SKILL.md` — the full methodology: three foundational definitions, four orthogonal dimensions, two meta-principles, conversation plan, analytical flow, common traps, core principles.
- `references/scoring-rubric.md` — the criteria for the four dimensions' direction (🔴🟡🟢) and solidity (🟩🟨🟧🟥).

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## Languages

- 中文: `SKILL-zh.md`, `README-zh.md`
- English: `SKILL.md`, `README.md`

---

## License

MIT — see [LICENSE](LICENSE)

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