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Get Started Free →Apply Sociotechnical Systems Theory to analyze and design work systems through joint optimization of social and technical subsystems. Use this skill when the user needs to diagnose why a technology implementation disrupted work practices, design IT-enabled work systems that balance human and technical needs, or when they ask 'why did this system hurt productivity despite being technically sound', 'how do we design work around new technology', or 'why are people resisting this technically superio
.claude/skills/asgard-ai-platform-grad-sociotechnical/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 27% | 0% |
| case-20 | ✓→✓ | = Same ✓ | 29% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 55% | 0% |
Sociotechnical Systems Theory, originating from the Tavistock Institute (Trist & Bamforth, 1951), holds that organizations are composed of interdependent social and technical subsystems. The social subsystem encompasses people, roles, relationships, and culture; the technical subsystem encompasses tools, processes, and technologies. Optimizing one subsystem in isolation degrades the other — effective design requires joint optimization of both.
IRON LAW: Optimizing the technical subsystem alone DEGRADES the social
subsystem (and vice versa) — joint optimization is required for system
effectiveness.Key assumptions:
Identify the human elements of the work system:
Identify the technological and process elements:
Map how changes in one subsystem affect the other. Look for:
Apply STS design principles:
markdown## Sociotechnical Analysis: [Work System / Organization] ### Social Subsystem | Element | Current State | Issues | |---------|-------------|--------| | Roles & Skills | | | | Team Structure | | | | Culture & Norms | | | | Worker Autonomy | | | ### Technical Subsystem | Element | Current State | Issues | |---------|-------------|--------| | Technology | | | | Processes | | | | Environment | | | | Key Variances | | | ### Interdependency Map | Technical Change | Social Impact | Severity | |-----------------|--------------|----------| | | | | ### Joint Optimization Recommendations | Principle | Current Gap | Recommended Action | |-----------|-----------|-------------------| | Minimal Critical Specification | | | | Variance Control | | | | Boundary Management | | | | Support Congruence | | | ### Implementation Priorities 1. ... 2. ...
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-18 | fail→pass | 29,185 | 15,418 | -47% | 1 | 1 | 0% | 2,290 | 3,590 | +57% | 0 | 0 | — |
case-20 | pass→pass | 18,696 | 15,629 | -16% | 1 | 1 | 0% | 2,746 | 3,529 | +29% | 0 | 0 | — |
case-01 | fail→fail | 25,997 | 40,207 | +55% | 1 | 1 | 0% | 4,077 | 4,564 | +12% | 0 | 0 | — |
case-02 | fail→fail | 37,604 | 19,500 | -48% | 1 | 1 | 0% | 5,786 | 4,044 | -30% | 0 | 0 | — |
case-03 | fail→pass | 32,625 | 20,368 | -38% | 1 | 1 | 0% | 4,552 | 4,172 | -8% | 0 | 0 | — |
case-04 | pass→fail | 29,500 | 32,343 | +10% | 1 | 1 | 0% | 4,638 | 5,896 | +27% | 0 | 0 | — |
case-05 | pass→pass | 22,764 | 23,254 | +2% | 1 | 1 | 0% | 3,393 | 5,248 | +55% | 0 | 0 | — |
case-06 | pass→pass | 19,517 | 23,125 | +18% | 1 | 1 | 0% | 3,015 | 4,798 | +59% | 0 | 0 | — |
case-07 | pass→pass | 17,067 | 17,263 | +1% | 1 | 1 | 0% | 2,549 | 3,850 | +51% | 0 | 0 | — |
case-08 | pass→pass | 18,877 | 18,953 | +0% | 1 | 1 | 0% | 2,998 | 4,275 | +43% | 0 | 0 | — |
case-09 | fail→fail | 18,789 | 15,932 | -15% | 1 | 1 | 0% | 2,758 | 3,523 | +28% | 0 | 0 | — |
case-10 | pass→pass | 20,123 | 17,166 | -15% | 1 | 1 | 0% | 3,297 | 3,713 | +13% | 0 | 0 | — |
case-11 | pass→pass | 21,359 | 15,068 | -29% | 1 | 1 | 0% | 3,284 | 3,439 | +5% | 0 | 0 | — |
case-19 | pass→pass | 15,299 | 15,077 | -1% | 1 | 1 | 0% | 2,388 | 3,546 | +48% | 0 | 0 | — |
case-12 | pass→pass | 15,580 | 16,228 | +4% | 1 | 1 | 0% | 2,254 | 3,503 | +55% | 0 | 0 | — |
case-13 | pass→pass | 23,608 | 17,823 | -25% | 1 | 1 | 0% | 3,177 | 3,607 | +14% | 0 | 0 | — |
case-14 | pass→pass | 20,499 | 18,085 | -12% | 1 | 1 | 0% | 2,940 | 3,945 | +34% | 0 | 0 | — |
case-15 | pass→pass | 15,767 | 18,167 | +15% | 1 | 1 | 0% | 2,306 | 3,606 | +56% | 0 | 0 | — |
case-16 | pass→pass | 19,954 | 18,432 | -8% | 1 | 1 | 0% | 3,119 | 3,980 | +28% | 0 | 0 | — |
case-17 | pass→pass | 15,296 | 17,843 | +17% | 1 | 1 | 0% | 2,322 | 3,838 | +65% | 0 | 0 | — |
case-21 | pass→pass | 16,842 | 15,547 | -8% | 1 | 1 | 0% | 2,260 | 3,265 | +44% | 0 | 0 | — |
case-22 | pass→pass | 17,148 | 18,668 | +9% | 1 | 1 | 0% | 2,723 | 4,011 | +47% | 0 | 0 | — |
case-23 | pass→pass | 16,482 | 16,451 | -0% | 1 | 1 | 0% | 2,359 | 3,586 | +52% | 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. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 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.