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Get Started Free →Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
.claude/skills/ibrahim-3d-context-driven-development/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 101% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 272% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 98% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 106% | 0% |
Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation.
Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions.
Key principles:
Follow the Context -> Spec -> Evaluate-Loop workflow:
conductor-orchestrator:loop-planner -- creates execution plan from specloop-plan-evaluator -- verifies plan before codingloop-executor -- implements tasks, updates plan.mdloop-execution-evaluator -- verifies implementationloop-fixer -- fixes evaluation failures, loops backSee conductor/workflow.md for the full Evaluate-Loop process.
Purpose: Captures product vision, goals, target users, and business context.
Contents:
Update when:
Purpose: Establishes brand voice, messaging standards, and communication patterns.
Contents:
Update when:
Purpose: Documents technology choices, dependencies, and architectural decisions.
Contents:
Update when:
Purpose: Establishes development practices, quality gates, and team workflows.
Contents:
Update when:
Purpose: Registry of all work units with status and metadata.
Contents:
Update when:
Ensure changes in one artifact reflect in related documents:
Before adding any new dependency:
After completing a feature track:
Before starting any track:
For new projects:
/orchestrator-supaconductor:setup to create all artifacts interactivelyCharacteristics:
For existing codebases:
/orchestrator-supaconductor:setup with existing codebase detectionCharacteristics:
conductor/
├── index.md # Navigation hub linking all artifacts
├── product.md # Product vision and goals
├── product-guidelines.md # Communication standards
├── tech-stack.md # Technology preferences
├── workflow.md # Development practices
├── tracks.md # Work unit registry
├── setup_state.json # Resumable setup state
├── code_styleguides/ # Language-specific conventions
│ ├── python.md
│ ├── typescript.md
│ └── ...
└── tracks/
└── <track-id>/
├── spec.md
├── plan.md
├── metadata.json
└── index.md/orchestrator-supaconductor:setupBefore starting implementation on any track, validate context:
Avoid these context management mistakes:
Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process.
Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types.
Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact.
Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative.
Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment.
Configure your IDE to display context files prominently:
Consider pre-commit hooks that:
Include context validation in pipelines:
Conductor supports multi-session development through context persistence:
If interrupted mid-task:
[~] with note about stopping point| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | fail→pass | 10,241 | 2,929 | -71% | 1 | 1 | 0% | 1,382 | 2,771 | +101% | 0 | 0 | — |
case-05 | fail→pass | 4,964 | 2,767 | -44% | 1 | 1 | 0% | 743 | 2,765 | +272% | 0 | 0 | — |
case-01 | fail→pass | 21,311 | 19,439 | -9% | 1 | 1 | 0% | 3,581 | 5,162 | +44% | 0 | 0 | — |
case-02 | fail→pass | 13,314 | 11,003 | -17% | 1 | 1 | 0% | 2,112 | 4,175 | +98% | 0 | 0 | — |
case-03 | fail→pass | 12,158 | 7,716 | -37% | 1 | 1 | 0% | 1,769 | 3,645 | +106% | 0 | 0 | — |
case-04 | fail→pass | 13,087 | 3,881 | -70% | 1 | 1 | 0% | 1,994 | 2,943 | +48% | 0 | 0 | — |
case-07 | fail→pass | 9,010 | 2,695 | -70% | 1 | 1 | 0% | 1,341 | 2,801 | +109% | 0 | 0 | — |
case-08 | fail→pass | 13,288 | 4,634 | -65% | 1 | 1 | 0% | 2,136 | 3,138 | +47% | 0 | 0 | — |
case-09 | pass→pass | 7,880 | 2,593 | -67% | 1 | 1 | 0% | 1,231 | 2,764 | +125% | 0 | 0 | — |
case-10 | fail→pass | 15,914 | 8,622 | -46% | 1 | 1 | 0% | 2,224 | 3,818 | +72% | 0 | 0 | — |
case-11 | pass→pass | 6,897 | 3,586 | -48% | 1 | 1 | 0% | 897 | 2,924 | +226% | 0 | 0 | — |
case-12 | fail→pass | 6,536 | 3,136 | -52% | 1 | 1 | 0% | 1,049 | 2,904 | +177% | 0 | 0 | — |
case-13 | fail→pass | 10,395 | 2,463 | -76% | 1 | 1 | 0% | 1,349 | 2,707 | +101% | 0 | 0 | — |
case-14 | pass→pass | 12,648 | 7,003 | -45% | 1 | 1 | 0% | 1,934 | 3,564 | +84% | 0 | 0 | — |
case-15 | fail→pass | 15,042 | 12,489 | -17% | 1 | 1 | 0% | 2,224 | 4,279 | +92% | 0 | 0 | — |
case-16 | fail→pass | 18,951 | 18,817 | -1% | 1 | 1 | 0% | 2,717 | 5,389 | +98% | 0 | 0 | — |
case-17 | fail→pass | 15,712 | 6,339 | -60% | 1 | 1 | 0% | 2,552 | 3,399 | +33% | 0 | 0 | — |
case-18 | fail→pass | 13,498 | 1,806 | -87% | 1 | 1 | 0% | 1,917 | 2,645 | +38% | 0 | 0 | — |
case-19 | pass→pass | 10,940 | 3,773 | -66% | 1 | 1 | 0% | 1,625 | 2,884 | +77% | 0 | 0 | — |
case-20 | pass→fail | 6,606 | 5,383 | -19% | 1 | 1 | 0% | 949 | 3,215 | +239% | 0 | 0 | — |
case-21 | pass→pass | 2,777 | 2,464 | -11% | 1 | 1 | 0% | 469 | 2,764 | +489% | 0 | 0 | — |
case-22 | pass→pass | 3,798 | 3,499 | -8% | 1 | 1 | 0% | 575 | 2,932 | +410% | 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 +64 percentage points is the difference between those two pass rates over the 22 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.