ai4s-agent

AI4S Agent (meta-skill)

Overview

Top-level entry point for the AI4S research stack. This skill contains no work of its own — its only job is to call four downstream skills in the right order, with the right slug, and reuse intermediate artifacts by path convention.

direction → research-explorer → topic
topic     → literature-survey  (60+ real bib, 100+ recommended)
topic     → experiment-suite   (design + code + results + figures)
topic     → paper-writer       (assembles into 200+ cite PDF)

Each downstream skill is already single-stage and self-sufficient: its agent loads that skill's SKILL.md and produces the full final-quality artifact directly. There is no skeleton/enrichment split. This meta-skill only handles ordering, the path convention, and disclosure consistency.

When to use

• User asks for "a paper on X" or "research package on X" and wants the whole stack run end to end.
• User wants to compare what each skill produces — useful for developing or debugging the pipeline itself.

When NOT to use

• User wants to run only one stage (e.g. only the literature survey) → invoke that skill directly.
• User wants only topic exploration → invoke research-explorer directly.

The slug contract

Every skill computes the same slug from the same topic string:

import re, hashlib
def slug(t):
    n = re.sub(r'[\s_]+', '-', re.sub(r'[^\w\s-]', '', t.lower().strip())).strip('-')[:40].rstrip('-')
    h = hashlib.sha1(t.encode()).hexdigest()[:8]
    return f"{n}-{h}"

Use the same string across all four skills. If the user provides a direction (not a topic), research-explorer runs against the direction; once a topic is chosen, the topic becomes the slug input for the remaining three.

Workflow

Step 1 — Understand the user's starting point

• Direction ("transformer time series forecasting") — start at research-explorer, pick a topic from its research_exploration.md, then proceed.
• Topic ("Transformer-based long-horizon forecasting with patch tokenisation") — skip research-explorer; go straight to the parallel branch (literature-survey, experiment-suite, paper-writer).
• Real measured experiment data? If yes, the user supplies a results.json path; experiment-suite loads it instead of writing a simulated one, and the paper's \thanks drops the simulated clause.