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Get Started Free →Active Inference multi-track exemplar — analytical formulas, pymdp simulation, sheaf manuscript composition, GNN, ontology, Lean gates, roadmap tracks.
.claude/skills/docxology-template-active-inference/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -49% | 0% |
Project-scoped skill for the in-repo exemplar at projects/templates/template_active_inference/. Load this when working inside the project.
template_active_inference exemplar — running scripts, editing source,or regenerating outputs.
no-mocks testing) still hold after changes.
bash# From this project root uv run pytest tests --cov=src --cov-fail-under=90 # From the repository root uv run python scripts/pipeline/stage_02_analysis.py --project templates/template_active_inference uv run python scripts/pipeline/stage_03_render.py --project templates/template_active_inference uv run python scripts/pipeline/stage_04_validate.py --project templates/template_active_inference uv run python scripts/pipeline/stage_05_copy.py --project templates/template_active_inference
src/ or sharedinfrastructure/, not in scripts/.
computation.
output/ — regenerate fromsource and config.
as working directory unless the child AGENTS.md states otherwise.
AGENTS.mdREADME.mdTODO.mdprojects/AGENTS.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 6,035 | 4,507 | -25% | 1 | 1 | 0% | 966 | 669 | -31% | 0 | 0 | — |
case-02 | fail→fail | 10,028 | 5,960 | -41% | 1 | 1 | 0% | 1,936 | 891 | -54% | 0 | 0 | — |
case-03 | fail→fail | 12,046 | 8,150 | -32% | 1 | 1 | 0% | 2,197 | 2,135 | -3% | 0 | 0 | — |
case-04 | pass→pass | 13,141 | 6,296 | -52% | 1 | 1 | 0% | 2,387 | 1,509 | -37% | 0 | 0 | — |
case-05 | pass→pass | 7,605 | 3,551 | -53% | 1 | 1 | 0% | 1,072 | 1,028 | -4% | 0 | 0 | — |
case-06 | pass→pass | 9,103 | 3,362 | -63% | 1 | 1 | 0% | 1,503 | 1,073 | -29% | 0 | 0 | — |
case-07 | fail→pass | 15,536 | 7,715 | -50% | 1 | 1 | 0% | 2,356 | 1,697 | -28% | 0 | 0 | — |
case-08 | fail→pass | 3,296 | 1,652 | -50% | 1 | 1 | 0% | 489 | 651 | +33% | 0 | 0 | — |
case-09 | fail→pass | 7,035 | 2,295 | -67% | 1 | 1 | 0% | 1,329 | 868 | -35% | 0 | 0 | — |
case-10 | fail→pass | 7,521 | 2,059 | -73% | 1 | 1 | 0% | 1,478 | 753 | -49% | 0 | 0 | — |
case-11 | fail→pass | 8,024 | 3,051 | -62% | 1 | 1 | 0% | 1,483 | 738 | -50% | 0 | 0 | — |
case-12 | fail→pass | 10,898 | 2,491 | -77% | 1 | 1 | 0% | 1,769 | 823 | -53% | 0 | 0 | — |
case-13 | fail→pass | 10,018 | 5,242 | -48% | 1 | 1 | 0% | 1,743 | 1,394 | -20% | 0 | 0 | — |
case-14 | pass→pass | 10,655 | 7,185 | -33% | 1 | 1 | 0% | 1,989 | 1,658 | -17% | 0 | 0 | — |
case-15 | pass→pass | 10,722 | 4,350 | -59% | 1 | 1 | 0% | 1,768 | 1,289 | -27% | 0 | 0 | — |
case-16 | fail→pass | 8,794 | 2,924 | -67% | 1 | 1 | 0% | 1,323 | 905 | -32% | 0 | 0 | — |
case-17 | pass→pass | 5,480 | 2,156 | -61% | 1 | 1 | 0% | 1,029 | 744 | -28% | 0 | 0 | — |
case-18 | pass→pass | 7,533 | 1,596 | -79% | 1 | 1 | 0% | 1,356 | 702 | -48% | 0 | 0 | — |
case-19 | fail→pass | 17,274 | 4,012 | -77% | 1 | 1 | 0% | 881 | 1,247 | +42% | 0 | 0 | — |
case-20 | pass→pass | 12,484 | 7,605 | -39% | 1 | 1 | 0% | 2,304 | 1,200 | -48% | 0 | 0 | — |
case-21 | pass→pass | 16,560 | 16,112 | -3% | 1 | 1 | 0% | 3,535 | 3,835 | +8% | 0 | 0 | — |
case-22 | pass→pass | 8,068 | 7,607 | -6% | 1 | 1 | 0% | 1,910 | 1,988 | +4% | 0 | 0 | — |
case-23 | pass→pass | 9,847 | 5,012 | -49% | 1 | 1 | 0% | 1,980 | 1,425 | -28% | 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. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +43 percentage points is the difference between those two pass rates over the 21 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.