Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Campaign for mapping argument structures — extract claims, link evidence, assess strength, synthesize positions. Produces argument graphs in the wiki vault.
.claude/skills/yogsoth-ai-argument-mapping/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -13% | 0% |
Map argument structures for research domains. Extracts claims from sources, links supporting and opposing evidence, assesses argument strength, and synthesizes coherent positions from complex debates.
| Level | Count | Skills | |-------|-------|--------| | Strategy | 3 | claim-extraction, evidence-linking-arg, argument-synthesis | | Tactic | 2 | claim-decomposition, strength-assessment | | SOP | 6 | claim-page-creation, rebuttal-documentation, evidence-attachment, strength-scoring, argument-visualization, synthesis-report |
| Metric | Small | Medium | Large | |--------|-------|--------|-------| | Claims extracted | 10 | 25 | 50 | | Evidence links created | 15 | 40 | 80 | | Rebuttals documented | 3 | 10 | 20 | | Strength scores assigned | 10 | 25 | 50 | | Synthesis reports | 1 | 3 | 5 |
vault_search — find existing claims and evidencevault_add_edge — create argument edges (supported_by, contradicts, derived_from)vault_query_graph — trace argument chainsvault_graph_stats — assess argument coveragevault_lint — validate structural integrity<HARD-GATE>
context-init at campaign startcontext-checkpoint after each strategy completesknowledge-compilation after each strategy</HARD-GATE>
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | argument-synthesis | Strategy for synthesizing argument positions — aggregate evidence, resolve contradictions, produce synthesis reports identifying which claims survive scrutiny. | | claim-extraction | Strategy for extracting claims from source material — identify propositions, decompose compound claims, classify claim types, create claim pages in the vault. | | evidence-linking-arg | Strategy for linking evidence to claims — find supporting/contradicting evidence, create typed edges, assess evidence quality, identify gaps in evidential coverage. |
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | knowledge-compilation | Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 9,803 | 5,401 | -45% | 1 | 1 | 0% | 1,519 | 1,799 | +18% | 0 | 0 | — |
case-01 | fail→pass | 29,214 | 38,952 | +33% | 1 | 1 | 0% | 4,690 | 7,129 | +52% | 0 | 0 | — |
case-02 | fail→fail | 28,620 | 6,645 | -77% | 1 | 1 | 0% | 4,160 | 1,234 | -70% | 0 | 0 | — |
case-03 | pass→pass | 14,770 | 26,863 | +82% | 1 | 1 | 0% | 2,633 | 6,094 | +131% | 0 | 0 | — |
case-04 | pass→fail | 4,980 | 7,754 | +56% | 1 | 1 | 0% | 820 | 2,020 | +146% | 0 | 0 | — |
case-05 | pass→pass | 16,920 | 16,684 | -1% | 1 | 1 | 0% | 2,693 | 3,437 | +28% | 0 | 0 | — |
case-06 | pass→pass | 13,169 | 9,254 | -30% | 1 | 1 | 0% | 2,053 | 2,389 | +16% | 0 | 0 | — |
case-07 | pass→pass | 14,026 | 6,629 | -53% | 1 | 1 | 0% | 2,065 | 1,878 | -9% | 0 | 0 | — |
case-08 | pass→pass | 11,249 | 5,498 | -51% | 1 | 1 | 0% | 1,865 | 1,902 | +2% | 0 | 0 | — |
case-09 | pass→pass | 10,937 | 4,740 | -57% | 1 | 1 | 0% | 1,535 | 1,608 | +5% | 0 | 0 | — |
case-10 | pass→pass | 14,447 | 8,334 | -42% | 1 | 1 | 0% | 2,021 | 2,130 | +5% | 0 | 0 | — |
case-11 | pass→pass | 11,178 | 7,454 | -33% | 1 | 1 | 0% | 1,600 | 2,051 | +28% | 0 | 0 | — |
case-13 | fail→pass | 9,319 | 2,372 | -75% | 1 | 1 | 0% | 1,489 | 1,252 | -16% | 0 | 0 | — |
case-14 | fail→fail | 12,706 | 1,852 | -85% | 1 | 1 | 0% | 1,945 | 1,238 | -36% | 0 | 0 | — |
case-15 | fail→pass | 22,565 | 1,930 | -91% | 1 | 1 | 0% | 1,590 | 1,213 | -24% | 0 | 0 | — |
case-16 | pass→pass | 8,763 | 2,138 | -76% | 1 | 1 | 0% | 1,235 | 1,279 | +4% | 0 | 0 | — |
case-17 | fail→pass | 12,051 | 4,067 | -66% | 1 | 1 | 0% | 1,771 | 1,538 | -13% | 0 | 0 | — |
case-18 | fail→pass | 7,468 | 2,003 | -73% | 1 | 1 | 0% | 1,155 | 1,215 | +5% | 0 | 0 | — |
case-19 | fail→pass | 14,631 | 2,624 | -82% | 1 | 1 | 0% | 2,147 | 1,374 | -36% | 0 | 0 | — |
case-20 | fail→pass | 6,062 | 1,914 | -68% | 1 | 1 | 0% | 931 | 1,223 | +31% | 0 | 0 | — |
case-21 | pass→pass | 9,535 | 2,027 | -79% | 1 | 1 | 0% | 1,475 | 1,216 | -18% | 0 | 0 | — |
case-22 | fail→pass | 13,601 | 6,551 | -52% | 1 | 1 | 0% | 2,029 | 1,872 | -8% | 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 21 counted toward the lift figure. The other 1 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 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.