Openclaw Skills turns side-project ideas into evidence-first investment memos that judge demand, monetization, and focus before you build.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install evaluate-indie-opportunity
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install evaluate-indie-opportunity using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Evaluate Indie Opportunity?
Openclaw Skills for evaluate-indie-opportunity is an evidence-first decision framework for side businesses, indie products, AI projects, open-source monetization, and small games before you spend serious time or money.
It forces a crisp thesis, separates facts from assumptions, checks red lines and boundary risks, and anchors every recommendation to a real first-transaction path instead of vanity signals. Openclaw Skills then turns the idea into a structured memo with opportunity class, evidence ledger, capped scoring, Phase 0 design, and a final action such as COMMIT, TEST NOW, RESEARCH, WATCH, PARK, or AVOID.
Evaluate Indie Opportunity Use Cases
- Validating a side hustle before building a full MVP
- Comparing multiple indie SaaS or digital product ideas
- Testing AI project monetization before writing more code
- Checking whether open-source, content, or plugin work can become repeatable income
- Designing a low-cost Phase 0 experiment with clear stop rules
- Deciding whether an idea is a cash experiment, compounding asset, strategic option, or capability lab
- Screening ideas for legal, privacy, platform policy, IP, or time-boundary issues
- Picking the single best opportunity when focus is limited
How Evaluate Indie Opportunity Works
- Restate the opportunity in one sentence with the user, payer, pain point, exchange, and revenue path.
- Separate facts, inferences, assumptions, and unknowns, then ask up to three questions if key evidence is missing.
- Run the red-line and boundary review for legality, privacy, IP, platform policy, conflicts, and sustainability.
- Classify the idea as
CASH EXPERIMENT,COMPOUNDING ASSET,STRATEGIC OPTION,CAPABILITY LAB, orAVOID. - Build an evidence ledger using
E0toE4, preferring official sources, first-party behavior, and observable transactions. - Score the opportunity across the rubric, but cap the final score by evidence quality rather than enthusiasm.
- Design the first transaction and a Phase 0 test with one high-risk assumption, one minimal action, audience, cost, time box, and stop thresholds.
- Compare portfolio fit, surface the best next action, and return a final decision label with confidence.
Evaluate Indie Opportunity Setup
- Add the skill to your Openclaw Skills library.
- Make sure the reference files are included next to
SKILL.md. - If you are packaging from a repo, create the expected folder structure:
mkdir -p ~/.openclaw/skills/evaluate-indie-opportunity/references
cp /path/to/evaluate-indie-opportunity/SKILL.md ~/.openclaw/skills/evaluate-indie-opportunity/
cp /path/to/evaluate-indie-opportunity/references/*.md ~/.openclaw/skills/evaluate-indie-opportunity/references/
- No external runtime dependencies are required; the skill is a reasoning workflow, not a compiled tool.
- When prompting the skill, provide the opportunity idea, target user, likely payer, current evidence, and any boundary or compliance concerns.
- For best results, ask for a short memo first, then request deeper research only if the conclusion is still uncertain.
Evaluate Indie Opportunity Data Schema & Taxonomy
Openclaw Skills uses a lightweight, memo-style schema rather than persistent application state.
| Artifact | Path / Type | Purpose | Key contents |
|---|---|---|---|
| Skill spec | SKILL.md |
Core behavior and output contract | rules, workflow, output template, stop rules |
| Scoring rubric | references/rubric.md |
Evidence grading and scoring | E0 to E4, 100-point rubric, action thresholds |
| Phase 0 patterns | references/phase-zero-patterns.md |
Experiment templates | single high-risk assumption, minimal test, time and cost caps |
| Source policy | references/source-policy.md |
Information quality guardrails | official sources, first-party behavior, raw data, reuse limits |
| Evidence ledger | Runtime working notes | Traceable reasoning trail | facts, inferences, assumptions, unknowns, dates, sources |
| Final memo | Runtime output | Decision deliverable | one-line conclusion, opportunity recap, evidence, first transaction, Phase 0, risk notes, next step |
Taxonomy
- Evidence levels:
E0,E1,E2,E3,E4 - Opportunity classes:
CASH EXPERIMENT,COMPOUNDING ASSET,STRATEGIC OPTION,CAPABILITY LAB,AVOID - Decision labels:
COMMIT,TEST NOW,RESEARCH,WATCH,PARK,AVOID - Confidence labels:
low,medium,high - Risk tags: boundary review, legal, tax, privacy, platform policy, IP, time box, cost cap
Evaluate Indie Opportunity Advanced Features
- Evidence-first analysis that refuses to confuse hype, examples, or social proof with paid demand
- First-transaction planning before feature planning, so the monetization path is explicit
- Phase 0 experiments with a single high-risk assumption, strict time boxes, and stop thresholds
- Boundary review for legality, privacy, IP, conflicts, and platform policy issues
- Portfolio-fit analysis across existing products, audience, code, content, data, brand, and channels
- Evidence-capped scoring that prevents enthusiasm from inflating the final recommendation
- Clear action labels that make the outcome operational:
COMMIT,TEST NOW,RESEARCH,WATCH,PARK, orAVOID - Great for Openclaw Skills workflows that compare multiple indie opportunities side by side
SKILL.md
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