| name | haipipe-task-for-algo |
| description | algo-dev task-folder specialist: scaffolds {NN}_<name>/ task-folders in the algo-dev task-group (default X_algo) that smoke-test a newly developed algorithm class end-to-end on a TINY config. NOT full training -- see /haipipe-task-for-fit. Called by /haipipe-task when task-type=algo. Cross-references /haipipe-nn-algo. |
| argument-hint | [project_id] [group] [task-name] |
| allowed-tools | Bash, Read, Write, Edit, Grep, Glob, Skill |
| metadata | {"version":"0.1.2","last_updated":"2026-07-04","summary":"algo-dev task-folder build specialist."} |
Skill: haipipe-task-for-algo
Scaffolds an algo-dev smoke-test task-folder.
Purpose: verify a new algorithm class (forward / loss / metric) runs end-to-end.
This is NOT a training run — minimal config, minutes-not-hours, just "did it crash + does the loss go down on one batch".
Invocation modes: interactive (human steers; missing fields get ASKed) OR headless (haipipe-task-creator-agent calls this skill during Phase 2: Build, then authors the <TASK>.py body).
Always end with the structured return block (status / task_folder / run_name / files).
task-algo vs task-training (don't confuse them)
task-algo (this) task-training
Purpose smoke-test algorithm train + sweep a model
Group letter X (paired demo) A (model-run)
Config minimal / 1-batch full hyperparam grid
Runtime minutes hours-to-days
Outputs "didn't crash" + loss checkpoint → _WorkSpace/5
Audience algo developer self cross-run comparison
Pipeline skill /haipipe-nn-algo /haipipe-nn-tuner+instance
What this scaffolds
tasks/X_algo/ ← X-series group (paired Track A)
└── {NN}_test_<algo_name>/
├── {NN}_test_<algo_name>.py
├── configs/
│ └── algo_<name>_tiny.yaml seeded from ref/config-seed.yaml
├── runs/
│ └── algo_<name>_tiny.sh
├── results/ loss.json, "ran" marker
└── notebooks/
Group letter default: X (algo-dev demo).
Heavy outputs: none (tiny / disposable).
Cross-reference to pipeline skill
/haipipe-nn-algo owns the algorithm class itself (Layer 1: model, forward, loss, metric).
This skill scaffolds the smoke-test demo that exercises it.
Typical flow:
/haipipe-nn-algo — author the algorithm class./haipipe-task-for-algo — scaffold the paired demo.- Run the demo. Iterate.
- Once stable →
/haipipe-task-for-fit for the real run.
Scaffold flow
See fn/scaffold.md for the detailed step-by-step.
Summary:
- Identify project + task-group.
- Collect metadata (NN, name, type-specific extras, _meta block).
- Create skeleton (.py, configs/, runs/, results/, notebooks/).
- Seed config from
ref/config-seed.yaml.
- Copy run-script from
../../haipipe-task/ref/run-sh-template.sh.
- Suggest next via cross-skill link.
- Emit return contract.
Return contract
status: ok | blocked | failed
summary: 2-3 sentences on what was scaffolded
artifacts: [paths created]
next: suggested next command (run the demo / /haipipe-nn-algo refine)
Workflow plan
When /haipipe-task plan targets an existing task-folder of this type, the generated plan-script YAML should follow the type-specific sample:
ref/workflow-plan-sample.yaml ← script-level phases for this type
../../haipipe-task/ref/workflow-template.yaml ← task-level template (Run/Gate1/Gate2)
Schema source of truth:
task/haipipe-workflow/ref/plan-schema.md
