Extract paper structure
SkillDev toolsExtract a forecasting paper's implementable architecture, equations, tensor contracts, official-code clarifications, training objective, and ambiguities. Use before local implementation or structure audit; not for broad literature discovery.
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Extract paper structure skill
What this skill tells your AI
The instructions your AI receives, as published by diaugeia/moderntsf in .agents/skills/extract-paper-structure/SKILL.md and read by ahel’s review.
Use the primary paper and supplement. Locate official code when available, record its license, pin a revision, and inspect it to resolve paper omissions. Keep paper facts, official implementation details, and local design in separate fields; do not copy source text or code into the implementation map.
Produce a compact implementation map containing:
- task, inputs, covariates, tensor axes, output, and probabilistic semantics;
- preprocessing, normalization, decomposition, embeddings, main blocks, head, and inverse transforms in execution order;
- defining equations with paper section or equation references;
- loss, auxiliary objectives, initialization, defaults, and train/eval differences;
- shape invariants, sequence constraints, marks or adjacency contracts, and edge cases;
- official-code clarifications with revision and source path when available;
- unspecified details and decisions that would materially change fidelity.
For every implementable operation, include a component decision:
reuse-existing with the component name and matched contract, extract-new
with expected consumers, or model-local with the semantic mismatch. Prefer
reuse-existing whenever equivalence is established.
Separate paper facts from implementation inference. Match each operation to an existing component only after checking mathematics, axes, normalization, masking, residual order, initialization, state, and outputs with:
uv run tsf component match <operation-and-contract-terms> --json
uv run tsf component show <candidate>
Deliver the map independently of code. It is complete when another worker can implement or audit the model without guessing a defining operation. Stop and surface ambiguity when choosing silently would change the named method.
Signals
- GitHub stars
- 65
- Forks
- 8
- Last commit
- Sep 2026
Advanced
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extract-paper-structure- Source
- github.com/diaugeia/moderntsf