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| 1 | +--- |
| 2 | +name: "DARC Feature Proposal" |
| 3 | +about: "Propose a new feature, improvement, or component using the DARC methodology" |
| 4 | +title: "[Feature]: " |
| 5 | +labels: enhancement |
| 6 | +assignees: "" |
| 7 | +--- |
| 8 | + |
| 9 | +# 🧠 D: DEFINE — Problem & Opportunity |
| 10 | + |
| 11 | +**What is the problem, limitation, or opportunity? Why does this matter for SKaiNET?** |
| 12 | + |
| 13 | +Explain the context in clear terms: |
| 14 | +- What functionality is missing or insufficient? |
| 15 | +- Why is this important now? |
| 16 | +- Who benefits (users, developers, researchers)? |
| 17 | +- What is the high-level goal of this proposal? |
| 18 | + |
| 19 | +**Summary (1–2 sentences):** |
| 20 | +> _Provide a concise overview of what this issue aims to address._ |
| 21 | +
|
| 22 | +--- |
| 23 | + |
| 24 | +# 🔍 A: ASSESS — Feasibility & Impact |
| 25 | + |
| 26 | +Provide an evaluation of the proposal. Address the following: |
| 27 | + |
| 28 | +### ✔️ Feasibility |
| 29 | +- Is the feature straightforward to implement? |
| 30 | +- Are there architectural constraints? |
| 31 | + |
| 32 | +### ✔️ Expected Impact |
| 33 | +- How does this improve SKaiNET? |
| 34 | +- Does it unlock new capabilities? |
| 35 | + |
| 36 | +### ✔️ Risks / Constraints |
| 37 | +- Technical challenges? |
| 38 | +- Numerical stability concerns? |
| 39 | +- API consistency? |
| 40 | + |
| 41 | +### ✔️ Dependencies |
| 42 | +- Does this rely on an existing SKaiNET module? |
| 43 | +- Does it require third-party libraries or standards? |
| 44 | + |
| 45 | +--- |
| 46 | + |
| 47 | +# 📚 R: RESEARCH — What Must Be Understood First? |
| 48 | + |
| 49 | +Document research tasks or open questions that must be answered before implementation: |
| 50 | + |
| 51 | +### Research Tasks |
| 52 | +- [ ] Review existing frameworks (PyTorch, JAX, TensorFlow, etc.) |
| 53 | +- [ ] Identify relevant algorithms or formulas |
| 54 | +- [ ] Compare design patterns for modularity |
| 55 | +- [ ] Investigate numerically stable or optimized variants |
| 56 | +- [ ] Check for relevant academic papers or benchmarks |
| 57 | + |
| 58 | +### Open Questions |
| 59 | +> _List unknowns that contributors should discuss or resolve._ |
| 60 | +
|
| 61 | +--- |
| 62 | + |
| 63 | +# 🛠️ C: CONTRIBUTE — Implementation Plan |
| 64 | + |
| 65 | +Break down actionable steps required to deliver this feature: |
| 66 | + |
| 67 | +### Development Tasks |
| 68 | +- [ ] Design module interface |
| 69 | +- [ ] Implement core functionality |
| 70 | +- [ ] Write unit tests (correctness + edge cases) |
| 71 | +- [ ] Add configuration integration |
| 72 | +- [ ] Update training loop or pipeline components (if applicable) |
| 73 | +- [ ] Add documentation & examples |
| 74 | +- [ ] Create or update benchmarks (optional) |
| 75 | +- [ ] Submit PR referencing this issue |
| 76 | + |
| 77 | +### Acceptance Criteria |
| 78 | +- [ ] Feature works as intended and passes all tests |
| 79 | +- [ ] Fully documented with clear examples |
| 80 | +- [ ] Follows SKaiNET coding style & modular design |
| 81 | +- [ ] Reviewed and approved by maintainers |
| 82 | +- [ ] No regressions introduced elsewhere |
| 83 | + |
| 84 | +--- |
| 85 | + |
| 86 | +# 💬 Additional Notes |
| 87 | + |
| 88 | +> _Add diagrams, pseudo-code, references, or implementation notes that may help future contributors._ |
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