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.claude/skills/graniet-obsidian/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -4% | 0% |
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal => bashweb_extract => web_fetch, plus web_search when discovery is neededsearch_files => grep_search and glob_searchbrowser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitlyWhen the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Location: Set via OBSIDIAN_VAULT_PATH environment variable (e.g. in ~/.kheish/.env).
If unset, defaults to ~/Documents/Obsidian Vault.
Note: Vault paths may contain spaces - always quote them.
bashVAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" cat "$VAULT/Note Name.md"
bashVAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" # All notes find "$VAULT" -name "*.md" -type f # In a specific folder ls "$VAULT/Subfolder/"
bashVAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" # By filename find "$VAULT" -name "*.md" -iname "*keyword*" # By content grep -rli "keyword" "$VAULT" --include="*.md"
bashVAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" cat > "$VAULT/New Note.md" << 'ENDNOTE' # Title Content here. ENDNOTE
bashVAULT="${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}" echo " New content here." >> "$VAULT/Existing Note.md"
Obsidian links notes with [[Note Name]] syntax. When creating notes, use these to link related content.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,458 | 22,057 | +395% | 1 | 1 | 0% | 747 | 796 | +7% | 0 | 0 | — |
case-02 | fail→fail | 6,139 | 9,471 | +54% | 1 | 1 | 0% | 990 | 1,074 | +8% | 0 | 0 | — |
case-03 | fail→fail | 9,994 | 5,625 | -44% | 1 | 1 | 0% | 806 | 759 | -6% | 0 | 0 | — |
case-04 | fail→pass | 3,484 | 1,830 | -47% | 1 | 1 | 0% | 615 | 865 | +41% | 0 | 0 | — |
case-22 | pass→pass | 8,741 | 7,079 | -19% | 1 | 1 | 0% | 1,682 | 1,932 | +15% | 0 | 0 | — |
case-05 | fail→pass | 7,762 | 2,139 | -72% | 1 | 1 | 0% | 1,386 | 921 | -34% | 0 | 0 | — |
case-06 | fail→pass | 7,717 | 2,744 | -64% | 1 | 1 | 0% | 1,502 | 962 | -36% | 0 | 0 | — |
case-07 | pass→pass | 5,936 | 2,494 | -58% | 1 | 1 | 0% | 963 | 993 | +3% | 0 | 0 | — |
case-08 | pass→fail | 7,540 | 7,343 | -3% | 1 | 1 | 0% | 1,406 | 1,758 | +25% | 0 | 0 | — |
case-09 | fail→pass | 5,356 | 1,852 | -65% | 1 | 1 | 0% | 918 | 856 | -7% | 0 | 0 | — |
case-10 | fail→pass | 5,507 | 2,114 | -62% | 1 | 1 | 0% | 947 | 912 | -4% | 0 | 0 | — |
case-11 | pass→pass | 5,361 | 1,155 | -78% | 1 | 1 | 0% | 859 | 647 | -25% | 0 | 0 | — |
case-12 | fail→pass | 7,141 | 1,478 | -79% | 1 | 1 | 0% | 1,212 | 783 | -35% | 0 | 0 | — |
case-13 | fail→pass | 9,810 | 1,838 | -81% | 1 | 1 | 0% | 1,681 | 807 | -52% | 0 | 0 | — |
case-14 | fail→pass | 9,827 | 1,335 | -86% | 1 | 1 | 0% | 1,590 | 690 | -57% | 0 | 0 | — |
case-15 | fail→fail | 5,865 | 1,651 | -72% | 1 | 1 | 0% | 912 | 748 | -18% | 0 | 0 | — |
case-16 | pass→pass | 2,942 | 1,711 | -42% | 1 | 1 | 0% | 500 | 800 | +60% | 0 | 0 | — |
case-17 | pass→pass | 12,080 | 5,181 | -57% | 1 | 1 | 0% | 1,997 | 1,431 | -28% | 0 | 0 | — |
case-18 | pass→pass | 9,517 | 1,563 | -84% | 1 | 1 | 0% | 1,689 | 786 | -53% | 0 | 0 | — |
case-19 | fail→pass | 2,413 | 1,791 | -26% | 1 | 1 | 0% | 395 | 857 | +117% | 0 | 0 | — |
case-20 | pass→pass | 17,626 | 18,169 | +3% | 1 | 1 | 0% | 3,914 | 4,554 | +16% | 0 | 0 | — |
case-21 | pass→pass | 12,606 | 9,237 | -27% | 1 | 1 | 0% | 2,490 | 2,350 | -6% | 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, and 19 counted toward the lift figure. The other 3 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 +36 percentage points is the difference between those two pass rates over the 19 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
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.