Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Apply Bourdieu's field theory to analyze power relations through the interplay of field, capital, and habitus. Use this skill when the user needs to map positions and position-takings within a social field, analyze how different forms of capital (economic, cultural, social, symbolic) structure competition, explain why actors behave as they do within institutional settings, or when they ask 'why do people in this industry act this way', 'who has power and why', or 'how does this field reproduce i
.claude/skills/asgard-ai-platform-grad-field-theory/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 60% | 0% |
Bourdieu's field theory explains social life as a series of semi-autonomous fields (champs) where agents compete for various forms of capital. Behavior is generated by the interaction of habitus (internalized dispositions) with field structure, producing strategies that are neither fully conscious nor fully determined.
IRON LAW: Behavior is produced by the interaction of habitus AND field —
neither alone explains action. Attributing behavior solely to individual
dispositions or solely to structural position violates the theory.Key assumptions:
Identify the field boundaries, its nomos (fundamental law), and the specific stakes agents compete for.
Identify the forms of capital that matter in this field and how they are distributed among agents.
| Capital Type | Definition | Examples | |-------------|------------|----------| | Economic | Material wealth and financial resources | Revenue, funding, assets | | Cultural | Knowledge, credentials, taste | Degrees, expertise, aesthetic sensibility | | Social | Network of relationships | Connections, memberships, alliances | | Symbolic | Recognized legitimacy | Reputation, prestige, authority |
Map agents according to their capital volume (total) and capital composition (ratio of capital types). Identify strategies of conservation (dominant) vs. subversion (challengers).
Explain how agents' dispositions (shaped by trajectory and social origin) interact with field pressures to produce observable practices.
markdown## Field Analysis: [Context] ### Field Definition - Field: [name and boundaries] - Nomos: [fundamental law of the field] - Stakes: [what agents compete for] ### Capital Map | Agent/Group | Economic | Cultural | Social | Symbolic | Total Volume | Position | |-------------|----------|----------|--------|----------|-------------|----------| | [name] | H/M/L | H/M/L | H/M/L | H/M/L | [sum] | [dominant/challenger/newcomer] | ### Field Dynamics - Conservation strategies (dominant): ... - Subversion strategies (challengers): ... - Doxa (taken-for-granted beliefs): ... ### Habitus Analysis - How dispositions shape strategy: ... - Hysteresis effects (habitus-field mismatch): ... ### Implications 1. [Who benefits from the current field structure] 2. [Where transformation pressure is building]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-15 | fail→pass | 27,453 | 22,500 | -18% | 1 | 1 | 0% | 3,935 | 3,622 | -8% | 0 | 0 | — |
case-01 | fail→fail | 53,085 | 37,711 | -29% | 1 | 1 | 0% | 6,951 | 5,972 | -14% | 0 | 0 | — |
case-02 | fail→fail | 51,282 | 34,019 | -34% | 1 | 1 | 0% | 6,882 | 5,135 | -25% | 0 | 0 | — |
case-08 | fail→pass | 48,074 | 20,583 | -57% | 1 | 1 | 0% | 3,141 | 4,025 | +28% | 0 | 0 | — |
case-03 | fail→pass | 37,335 | 25,269 | -32% | 1 | 1 | 0% | 5,262 | 4,547 | -14% | 0 | 0 | — |
case-04 | pass→pass | 22,189 | 21,478 | -3% | 1 | 1 | 0% | 3,136 | 3,877 | +24% | 0 | 0 | — |
case-05 | pass→pass | 49,128 | 21,095 | -57% | 1 | 1 | 0% | 4,665 | 3,903 | -16% | 0 | 0 | — |
case-06 | fail→fail | 33,070 | 47,407 | +43% | 1 | 1 | 0% | 3,491 | 5,785 | +66% | 0 | 0 | — |
case-07 | fail→pass | 52,504 | 24,335 | -54% | 1 | 1 | 0% | 4,214 | 4,545 | +8% | 0 | 0 | — |
case-09 | pass→pass | 22,476 | 20,565 | -9% | 1 | 1 | 0% | 3,257 | 3,604 | +11% | 0 | 0 | — |
case-10 | fail→pass | 24,225 | 52,568 | +117% | 1 | 1 | 0% | 3,127 | 5,006 | +60% | 0 | 0 | — |
case-11 | fail→pass | 23,887 | 29,338 | +23% | 1 | 1 | 0% | 2,834 | 4,438 | +57% | 0 | 0 | — |
case-12 | fail→fail | 38,616 | 35,979 | -7% | 1 | 1 | 0% | 4,813 | 5,294 | +10% | 0 | 0 | — |
case-13 | fail→pass | 21,743 | 19,183 | -12% | 1 | 1 | 0% | 2,678 | 3,451 | +29% | 0 | 0 | — |
case-14 | fail→fail | 30,871 | 33,705 | +9% | 1 | 1 | 0% | 4,071 | 5,487 | +35% | 0 | 0 | — |
case-16 | fail→pass | 22,741 | 19,374 | -15% | 1 | 1 | 0% | 2,427 | 3,377 | +39% | 0 | 0 | — |
case-17 | fail→pass | 23,861 | 24,237 | +2% | 1 | 1 | 0% | 3,086 | 4,435 | +44% | 0 | 0 | — |
case-18 | fail→pass | 23,782 | 25,983 | +9% | 1 | 1 | 0% | 2,848 | 4,152 | +46% | 0 | 0 | — |
case-19 | fail→pass | 19,971 | 28,873 | +45% | 1 | 1 | 0% | 2,495 | 3,811 | +53% | 0 | 0 | — |
case-20 | pass→pass | 28,999 | 29,019 | +0% | 1 | 1 | 0% | 3,788 | 4,867 | +28% | 0 | 0 | — |
case-21 | pass→pass | 18,066 | 18,469 | +2% | 1 | 1 | 0% | 3,349 | 4,605 | +38% | 0 | 0 | — |
case-22 | pass→pass | 21,360 | 23,853 | +12% | 1 | 1 | 0% | 2,825 | 4,162 | +47% | 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 +50 percentage points is the difference between those two pass rates over the 22 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.