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.

Step 2 — Explore (only if input was a direction)

Load the research-explorer skill. Follow its 5 steps to produce:

output/research-explorer/<dir_slug>/latest/{research_exploration.md, topic_matrix.md, literature_pre_survey.md}

Discuss the candidate topics with the user. They pick one specific topic; that string becomes $TOPIC for the rest.

Step 3 — Literature survey

Load the literature-survey skill with $TOPIC. It produces:

output/literature-survey/<topic_slug>/latest/survey_paper/
├── main.pdf                    # the 6–20 page survey
├── main.tex
├── bibliography.bib            # 60+ real entries, 100+ recommended (URL-anchored)
├── sections/, figures/
output/literature-survey/<topic_slug>/latest/literature_table.md

The survey bibliography must pass the temporal profile selected by literature-survey; AI4S defaults to at least 60% from the current calendar year and previous two years.

Step 4 — Experiment package

Load the experiment-suite skill with $TOPIC. It produces:

output/experiment-suite/<topic_slug>/latest/
├── experiment_design.md
├── experiment/                  # runnable model.py / data.py / train.py / evaluate.py
├── results.json                 # with "simulated" + "provenance"
├── figures/                     # publication-grade + manifest.json (basenames only)
└── experiment_report.md

If a real results path was provided in Step 1, the agent loads it here and results.json is flagged "simulated": false.

Step 5 — Paper

Load the paper-writer skill with $TOPIC. Its cross-skill conventions automatically pick up Steps 3 and 4:

• Seeds bibliography.bib from output/literature-survey/<topic_slug>/latest/survey_paper/bibliography.bib, then expands it to 200+ inside paper-writer if needed.
• Re-runs the paper-writer freshness gate after expansion; adding older foundational references must not silently make a fast-moving bibliography stale.
• Reads numbers and provenance from output/experiment-suite/<topic_slug>/latest/results.json.
• Copies/symlinks the publication-grade figures from output/experiment-suite/<topic_slug>/latest/figures/.

It produces:

output/paper-writer/<topic_slug>/latest/paper/
├── main.pdf                    # 8–14 pages, 200+ cites
├── main.tex
├── bibliography.bib
├── sections/, figures/

Step 6 — Deliver

Report the four output roots to the user:

1. output/research-explorer/<dir_slug>/latest/ (if exploration ran)
2. output/literature-survey/<topic_slug>/latest/
3. output/experiment-suite/<topic_slug>/latest/
4. output/paper-writer/<topic_slug>/latest/

Plus the paper-writer stats per its references/05-quality-gate.md report format.

Disclosure consistency

The same simulated flag must drive disclosure across all four artifacts:

• experiment-suite/.../results.json → "simulated": true|false is the source of truth.
• experiment-suite/.../experiment_report.md top-of-page disclosure must match.
• paper-writer/.../main.tex \author{AI4S Agent\thanks{…}} must include the simulated clause iff results.json has "simulated": true.
• The always-on human-review clause is mandatory in every case.

Rules

• No LLM SDK in any skill, including this one. Pure markdown — SKILL.md only.
• One slug per topic, computed identically across skills. The contract above is non-negotiable.
• Never collapse the four skills into one agent run. Each skill's SKILL.md is the single source of truth for what counts as "done" for its artifact.
• A non-interactive runner (e.g. claude --print headless) lives outside the skills. The skills stay pure.