haipipe-task-for-agent

name	haipipe-task-for-agent
description	agent task-folder specialist: scaffolds {NN}_<name>/ task-folders in the agent task-group (default F-series) that call an LLM agent with prompts + tools -> results/<run>/{transcript.json, summary.md}. Called by /haipipe-task when task-type=agent. Engine: /haipipe-task-llm-engine.
argument-hint	[project_id] [group] [task-name]
allowed-tools	Bash, Read, Write, Edit, Grep, Glob, Skill
metadata	{"version":"0.1.3","last_updated":"2026-07-04","summary":"agent task-folder build specialist."}

Skill: haipipe-task-for-agent

Scaffolds an LLM-agent task-folder. The scaffolded script makes its LLM calls through the domain's engine, /haipipe-task-llm-engine (owns code/haiutils/llm_engine/). Inputs: prompts + tool spec + (optional) data context. Outputs: transcript + structured result under results/<run>/.

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).

What this scaffolds

tasks/F{NN}_<group_name>/                    ← F-series group (agent)
└── {NN}_<task_name>/
    ├── {NN}_<task_name>.py
    ├── prompts/                             system + user prompt files
    │   ├── system.md
    │   └── user.md
    ├── configs/
    │   └── agent_<name>.yaml                seeded from ref/config-seed.yaml
    ├── runs/
    │   └── agent_<name>.sh
    ├── results/
    │   └── <run>/                           transcript.json, summary.md
    └── notebooks/

Group letter default: F (agent). Heavy outputs: none.

Cross-reference to pipeline skill

Engine: /haipipe-task-llm-engine (LLM call runtime). Agent infra (Claude API client, tool dispatch, transcript logging) is project-owned for now. Adjacent skills: /claude-api.

Scaffold flow

See fn/scaffold.md for the detailed step-by-step. Summary:

1. Identify project + task-group.
2. Collect metadata (NN, name, type-specific extras, _meta block).
3. Create skeleton (.py, configs/, runs/, results/, notebooks/).
4. Seed config from ref/config-seed.yaml.
5. Copy run-script from ../../haipipe-task/ref/run-sh-template.sh.
6. Suggest next via cross-skill link.
7. 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.sh / edit prompts/)

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: ../../haipipe-workflow/ref/plan-schema.md