Implement a model
SkillAI & modelsImplement or replace one ModernTSF forecasting model as local code after checking its paper and pinned official implementation when available. Use after structure extraction and component matching; not for paper discovery, scaffolding alone, or experiment reproduction.
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 Implement a model skill
What this skill tells your AI
The instructions your AI receives, as published by diaugeia/moderntsf in .agents/skills/implement-model/SKILL.md and read by ahel’s review.
Require a completed paper-structure map, resolved input/output contract, and an explicit component decision for every defining operation. Inspect the primary paper and supplement. When official code exists, pin its revision and inspect it to resolve paper omissions such as tensor order, padding, initialization, defaults, and train/eval behavior. Record its license. Never copy, rename, mechanically rewrite, import, or depend on external model source.
If the requested outcome is the released pretrained foundation model rather than
a locally trained paper architecture, stop this workflow and use
integrate-foundation-model; the official runtime is deliberately not rewritten.
- Design local modules from the extracted equations and verified implementation
details. Mark each operation
reuse-existing,extract-new, ormodel-local. - Reuse
src/models/_components/only after proving mathematical and runtime equivalence. Keep paper-specific or semantically different blocks local. - Implement inside the flat
src/models/<slug>/package. Preserve useful paper formulas and explanatory comments, but not source-derived code or comments. Keep the canonical four-inputforwardsignature exact; model-specific operations belong in explicitly named methods rather than extra public inputs. - Keep
spec.pylimited to construction, parameter schema, config, capabilities, declared components, and runtime contract. Reject unknown parameters instead of accepting**kwargsor hidden aliases. - Complete the model card with paper facts, official code facts, local mapping, reused components, model-local blocks, differences, limits, and verification.
- Add focused equation/structure checks. If official code exists, add a bounded
reference_comparison; otherwise record that check asnot-applicable. - For pretrained weights or tokenizers, declare checksum-pinned
ModelArtifactentries inspec.py. Never download implicitly; inspect or fetch them withtsf model artifacts, and document whether the local path is optional or required. Add an explicitartifact_factory(cfg, params, paths)that constructs the local architecture from verified local paths; do not read an unpinned cache path or make the ordinary factory download assets.
Run:
uv run tsf model show <Name>
uv run tsf verify model <Name> # existing model; new entries verify during model add
uv run tsf model audit <Name>
uv run tsf repo doctor --strict --models <Name>
uv run tsf component audit
uv run tsf repo audit
Success requires unified evidence covering every verification check, a readable card, no peer-model implementation import, and no unresolved defining operation. Stop rather than add a placeholder when the paper, inputs, or defining behavior is too ambiguous to implement truthfully.
Signals
- GitHub stars
- 65
- Forks
- 8
- Last commit
- Sep 2026
Advanced
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implement-model- Source
- github.com/diaugeia/moderntsf