# Opportunity Evaluation And Judgment > Restates an identified opportunity as a falsifiable claim and structurally evaluates it against fixed criteria — market viability, feasibility, resource fit, strategic fit, risk — before resources are committed. Use once an opportunity has already been noticed and needs a deliberate go/no-go judgment, not another round of scanning for more ideas. - Skill: `pilot2service/opportunity-evaluation-and-judgment` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/opportunity-evaluation-and-judgment` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/opportunity-evaluation-and-judgment/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/opportunity-evaluation-and-judgment --- # Opportunity Evaluation & Judgment ## Purpose Structurally assesses the viability of an identified opportunity before resources are committed to it — the deliberate, evaluative counterpart to the noticing done in `market-and-signal-scanning` and the connecting done in `pattern-and-analogy-connector`. ## Anchored in research - Tang, J., Kacmar, K. M., & Busenitz, L. (2012), "Entrepreneurial Alertness in the Pursuit of New Opportunities," *Journal of Business Venturing*, 27(1), 77–94 — **evaluation and judgment**, the third of the paper's three dimensions of entrepreneurial alertness (alongside scanning-and-search and association-and-connection, which anchor `../market-and-signal-scanning/SKILL.md` and `../pattern-and-analogy-connector/SKILL.md` respectively). Evaluation and judgment is the deliberate weighing of a noticed opportunity against criteria such as feasibility, risk, and fit — distinct from the earlier, more intuitive noticing and connecting stages. - SFIA (Skills Framework for a Digital World) — **Business Situation Analysis**, a professional skill definition describing the systematic investigation of a business situation or problem to identify options, risks, and success criteria before a course of action is chosen. ## Method 1. **State the opportunity as a testable claim, not a description.** Before evaluating, restate the opportunity as a specific claim that could turn out to be false (e.g. "Segment X will pay for Y because Z"), not just a general description of an idea — evaluation only works against a claim that's falsifiable. 2. **List the criteria the opportunity will be judged against**, decided in advance rather than picked to fit the answer you already want: typically market viability, technical/operational feasibility, resource fit, strategic fit, and risk exposure. Keep the criteria list stable across opportunities you're comparing, so judgments stay comparable. 3. **Gather the evidence available for each criterion**, and be explicit about the strength of that evidence — a validated customer interview is not the same class of evidence as an internal assumption. Where evidence is missing, mark it, don't fill the gap with a guess (`[assumption — verify]`). 4. **Apply deliberate judgment, separated from the initial noticing.** This is the step Tang, Kacmar & Busenitz's model is built around: consciously switch from "does this feel promising" (the alertness that got you here) to "does this hold up against the stated criteria" — the two are different cognitive modes, and collapsing them tends to let excitement about the pattern override an honest look at the evidence. 5. **Identify the single biggest point of uncertainty** — the one assumption that, if wrong, would most change the conclusion. Naming it explicitly turns the evaluation into an actionable next step (test that assumption) rather than a static verdict. 6. **Produce a structured verdict**: viable / viable with named conditions / not viable as currently framed — each with the reasoning that led there, so the next reader can see *why*, not just *what*. 7. **Route the outcome onward.** A "viable" or "viable with conditions" result feeds into `../opportunity-value-assessment/SKILL.md` (or this pack's own scoring, once the owner's version is filled in) for a fuller, two-dimensional assessment. ## What this skill does NOT do - Doesn't make the final decision for you — it produces a structured draft to support a human decision. - Doesn't confirm figures, market data, or competitor data from memory — it uses the inputs you provide, or clearly flags an assumption (`[assumption — verify]`). - Doesn't make a binding go/no-go call — it structures the criteria for the decision-maker. - Doesn't skip straight to judgment without first stating the opportunity as a testable claim and naming the evaluation criteria — doing so invites post-hoc rationalization of a conclusion already reached informally. ## Refinement notes Areas to keep deepening with real practice: - your own rules of thumb and heuristics for this technique - concrete templates (into `../../references/`) - reference cases / your own examples - what this skill deliberately does *not* do (guardrails, common mistakes) — add to the list above Once this section is filled in and validated in practice, update the `maturity` field in `skills_index.json` to `draft`, `validated`, or `canonical` (see `../../../meta/maturity_levels.md`). **Don't add new fields to the frontmatter** — `name` and `description` are the only ones allowed (see `../../../meta/frontmatter_schema.md`). ## Continue from here - Next in this pack: `../market-sizing-tam-sam-som/SKILL.md` — Quantitatively sizes the market and the genuinely reachable share of it. - The owner's validated, productized version of this same task: `../opportunity-value-assessment/SKILL.md` — a concrete 2D matrix + 7-lens assessment, `maturity: validated`. - A ready-made skill chain for this situation: see `../../../playbooks/` - This pack's shared guardrails: `../../CLAUDE.md` ## References - `../../references/` — the pack's shared background material - `../../CLAUDE.md` — this pack's shared guardrails