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Get Started Free →Apply systems thinking — causal loop diagrams, stock-and-flow models, system archetypes, and leverage-point analysis — to organizational, economic, or social problems where feedback loops, delays, or emergent behavior drive recurring failure across multiple interacting actors. Use this skill when the user describes a multi-actor situation that resists linear fixes: policy interventions that backfire, org-level fixes that break other teams, market symptoms that return after being solved, or time-
.claude/skills/asgard-ai-platform-meta-systems-thinking/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -6% | 0% |
IRON LAW: First-Order Fixes in Complex Systems Produce Second-Order
Backlash Within 2 Cycles — Map the Feedback Loop BEFORE Intervening
Agents default to "fix the symptom directly" (e.g., high turnover → raise
salaries). In systems with feedback loops, the direct fix triggers a
compensating response that makes the original problem worse OR creates
a new one (raise salaries → budget squeeze → cut training → worse
onboarding → higher turnover). Before recommending any intervention,
draw the causal loop diagram and identify at least one reinforcing and
one balancing loop. If you can't find any, the problem may not be a
systems problem — don't force the framework.Key concepts assumed known: feedback loops (reinforcing/balancing), emergence, delays, leverage points, stocks and flows. For system archetypes (Fixes That Fail, Shifting the Burden, Limits to Growth, etc.) see references/system-archetypes.md.
markdown# Systems Analysis: {Problem} ## System Boundary - In scope: ... - Out of scope: ... ## Key Variables - {Variable A}: {description} ## Feedback Loops - Reinforcing: {A → B → A (amplifying)} - Balancing: {A → B → C → opposes A (stabilizing)} ## Delays - {Input} → {Effect} (delay: {timeframe}) ## Leverage Points 1. {where small change = big impact} ## Unintended Consequences Risk - If we {intervention}, it might also {side effect} because {loop/connection}
Scenario: Why does hiring more engineers not speed up the project?
Reinforcing loop (intended): More engineers → more code → faster progress Balancing loop (unintended): More engineers → more communication overhead → more meetings → less coding time → slower progress (Brooks' Law) Delay: New engineers need 3-6 months to become productive
Leverage point: Instead of adding people, reduce communication overhead (smaller teams, clearer ownership, better documentation) ✓
references/system-archetypes.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 | 33,407 | 44,686 | +34% | 1 | 1 | 0% | 5,365 | 3,292 | -39% | 0 | 0 | — |
case-02 | fail→pass | 33,822 | 19,628 | -42% | 1 | 1 | 0% | 5,119 | 3,934 | -23% | 0 | 0 | — |
case-03 | pass→pass | 16,565 | 15,451 | -7% | 1 | 1 | 0% | 2,869 | 3,364 | +17% | 0 | 0 | — |
case-04 | pass→fail | 7,386 | 26,425 | +258% | 1 | 1 | 0% | 995 | 5,018 | +404% | 0 | 0 | — |
case-05 | pass→pass | 12,596 | 22,134 | +76% | 1 | 1 | 0% | 1,958 | 4,282 | +119% | 0 | 0 | — |
case-06 | fail→pass | 21,514 | 17,253 | -20% | 1 | 1 | 0% | 3,364 | 3,346 | -1% | 0 | 0 | — |
case-07 | fail→fail | 24,600 | 14,415 | -41% | 1 | 1 | 0% | 3,536 | 3,085 | -13% | 0 | 0 | — |
case-08 | pass→fail | 22,456 | 22,321 | -1% | 1 | 1 | 0% | 3,123 | 3,232 | +3% | 0 | 0 | — |
case-09 | pass→pass | 22,302 | 16,663 | -25% | 1 | 1 | 0% | 3,609 | 3,419 | -5% | 0 | 0 | — |
case-10 | pass→pass | 21,852 | 14,366 | -34% | 1 | 1 | 0% | 3,367 | 3,183 | -5% | 0 | 0 | — |
case-11 | pass→pass | 17,885 | 16,397 | -8% | 1 | 1 | 0% | 2,897 | 3,516 | +21% | 0 | 0 | — |
case-12 | pass→pass | 27,267 | 15,791 | -42% | 1 | 1 | 0% | 4,014 | 2,986 | -26% | 0 | 0 | — |
case-13 | pass→pass | 28,744 | 19,849 | -31% | 1 | 1 | 0% | 4,605 | 3,559 | -23% | 0 | 0 | — |
case-14 | pass→fail | 20,531 | 13,904 | -32% | 1 | 1 | 0% | 3,355 | 3,118 | -7% | 0 | 0 | — |
case-15 | fail→pass | 23,428 | 17,843 | -24% | 1 | 1 | 0% | 3,351 | 3,643 | +9% | 0 | 0 | — |
case-16 | pass→pass | 19,970 | 13,373 | -33% | 1 | 1 | 0% | 2,978 | 2,938 | -1% | 0 | 0 | — |
case-17 | pass→fail | 17,881 | 12,655 | -29% | 1 | 1 | 0% | 2,842 | 3,092 | +9% | 0 | 0 | — |
case-18 | pass→pass | 25,681 | 15,770 | -39% | 1 | 1 | 0% | 3,912 | 3,451 | -12% | 0 | 0 | — |
case-19 | pass→pass | 22,625 | 13,389 | -41% | 1 | 1 | 0% | 3,675 | 2,778 | -24% | 0 | 0 | — |
case-20 | fail→pass | 19,564 | 15,414 | -21% | 1 | 1 | 0% | 3,368 | 3,152 | -6% | 0 | 0 | — |
case-21 | pass→pass | 21,502 | 14,313 | -33% | 1 | 1 | 0% | 3,025 | 3,229 | +7% | 0 | 0 | — |
case-22 | fail→fail | 55,766 | 19,902 | -64% | 1 | 1 | 0% | 8,122 | 3,959 | -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 0 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 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.