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Get Started Free →Strategic advisory for climate-tech founders on carbon markets, GHG accounting, climate regulation, and funding. Use when scoping a climate-tech idea or picking a category, or mentioning climate, carbon, GHG, ESG, IRA, DOE, or net-zero.
.claude/skills/borghei-climate-tech-advisor/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 1% | 0% |
Strategic frameworks for climate-tech founders, operators, and product leaders.
> Disclaimer: Frameworks only. Climate compliance, carbon accounting, and verification require qualified specialists. Engage GHG / climate counsel and verifiers for binding decisions.
climate, climate-tech, climate tech, carbon, GHG, greenhouse gas, scope 1, scope 2, scope 3, ESG, IRA, DOE, CSRD, SEC climate rule, net zero, decarbonization, carbon removal, CDR, EU Green Deal, taxonomy, MRV
bashpython scripts/carbon_impact_estimator.py business_description.txt
Estimates rough order-of-magnitude carbon impact category and surfaces verification considerations. Not a verified carbon accounting result.
references/climate_categories.mdTime Estimate: 4-8 weeks for first sizing.
references/climate_funding_sources.mdTime Estimate: Continuous.
references/ghg_accounting_basics.mdTime Estimate: 6-12 weeks for first robust MRV plan.
Classifies a business description into climate categories and provides order-of-magnitude impact ranges and verification considerations.
bashpython scripts/carbon_impact_estimator.py description.txt python scripts/carbon_impact_estimator.py description.txt --json
This is a categorization tool, not a verified carbon-accounting calculator. Real GHG accounting requires methodology selection, data collection, and (for credit issuance) third-party verification.
references/climate_categories.md — Categories overview, market sizing intuition, common business model patternsreferences/climate_funding_sources.md — Grants (DOE, USDA, NSF), tax credits (IRA), prizes, climate VCreferences/ghg_accounting_basics.md — Scope 1/2/3, GHG Protocol, methodologies, MRV, verificationassets/climate_impact_assessment.md — Document template for capturing category, impact estimate, and MRV planc-level-advisor/cfo-advisor — climate-tech often combines grant + equity + project financelegal/ — IRA tax-credit qualification, carbon-credit contractsra-qm-team/ — some categories overlap with regulated industries (chemicals, energy, food)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 7,498 | 1,729 | -77% | 1 | 1 | 0% | 1,184 | 1,273 | +8% | 0 | 0 | — |
case-02 | pass→pass | 15,807 | 4,000 | -75% | 1 | 1 | 0% | 2,456 | 1,701 | -31% | 0 | 0 | — |
case-03 | pass→pass | 20,582 | 20,425 | -1% | 1 | 1 | 0% | 3,051 | 4,163 | +36% | 0 | 0 | — |
case-04 | pass→pass | 15,328 | 11,915 | -22% | 1 | 1 | 0% | 2,245 | 2,776 | +24% | 0 | 0 | — |
case-05 | pass→pass | 11,914 | 11,101 | -7% | 1 | 1 | 0% | 1,689 | 2,763 | +64% | 0 | 0 | — |
case-06 | pass→pass | 15,881 | 17,119 | +8% | 1 | 1 | 0% | 2,021 | 3,346 | +66% | 0 | 0 | — |
case-07 | pass→pass | 15,376 | 19,094 | +24% | 1 | 1 | 0% | 2,468 | 3,927 | +59% | 0 | 0 | — |
case-08 | fail→pass | 15,474 | 16,454 | +6% | 1 | 1 | 0% | 2,336 | 3,436 | +47% | 0 | 0 | — |
case-09 | pass→pass | 15,740 | 17,913 | +14% | 1 | 1 | 0% | 2,267 | 3,646 | +61% | 0 | 0 | — |
case-10 | pass→pass | 5,193 | 8,918 | +72% | 1 | 1 | 0% | 748 | 2,269 | +203% | 0 | 0 | — |
case-11 | pass→pass | 7,389 | 8,618 | +17% | 1 | 1 | 0% | 1,075 | 2,258 | +110% | 0 | 0 | — |
case-12 | pass→pass | 20,473 | 17,989 | -12% | 1 | 1 | 0% | 2,783 | 3,537 | +27% | 0 | 0 | — |
case-13 | fail→pass | 13,149 | 15,414 | +17% | 1 | 1 | 0% | 1,789 | 3,085 | +72% | 0 | 0 | — |
case-14 | fail→fail | 17,903 | 21,426 | +20% | 1 | 1 | 0% | 2,615 | 4,035 | +54% | 0 | 0 | — |
case-15 | pass→pass | 3,790 | 5,059 | +33% | 1 | 1 | 0% | 592 | 1,795 | +203% | 0 | 0 | — |
case-16 | pass→pass | 5,837 | 6,471 | +11% | 1 | 1 | 0% | 973 | 2,073 | +113% | 0 | 0 | — |
case-17 | pass→pass | 18,458 | 19,016 | +3% | 1 | 1 | 0% | 2,828 | 4,043 | +43% | 0 | 0 | — |
case-18 | fail→pass | 12,038 | 1,827 | -85% | 1 | 1 | 0% | 1,734 | 1,285 | -26% | 0 | 0 | — |
case-19 | fail→pass | 17,467 | 7,297 | -58% | 1 | 1 | 0% | 2,812 | 2,190 | -22% | 0 | 0 | — |
case-20 | pass→pass | 6,807 | 11,274 | +66% | 1 | 1 | 0% | 1,094 | 2,731 | +150% | 0 | 0 | — |
case-21 | fail→pass | 22,380 | 17,897 | -20% | 1 | 1 | 0% | 3,734 | 3,761 | +1% | 0 | 0 | — |
case-22 | fail→pass | 22,206 | 26,820 | +21% | 1 | 1 | 0% | 3,597 | 5,428 | +51% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +27 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.