Answer Question

Read a generated analysis report (from /analyze-results) and answer a specific question about it. Can perform additional analysis on the underlying data if the existing report doesn't fully answer the question. Saves the answer as markdown with supporting files in a dedicated directory.

Usage

code

/answer-question What is the sample size?
/answer-question How does weight vary by position? ./analysis_1

If no analysis directory is specified, the skill will locate the most recent one automatically.

Workflow

1. Parse the Input

Extract two things from the user's input:

•Question: The question to answer (required)
•Analysis directory: An optional path to a specific analysis directory (e.g., ./analysis_3)

If no analysis directory is provided, check how many analysis directories contain a report.md:

python

import glob
import os

existing = sorted(glob.glob("./analysis_*"), key=os.path.getmtime, reverse=True)
with_reports = [d for d in existing if os.path.isfile(os.path.join(d, "report.md"))]

•If zero directories have reports, tell the user to run /analyze-results first.
•If exactly one directory has a report, use it automatically.
•If multiple directories have reports, use AskUserQuestion to let the user pick. Read the first line of each report.md to build descriptive labels:

python

# Build option labels like "analysis_5 — Anchoring Bias Experiment (2026-02-06)"
options = []
for d in with_reports:
    first_line = open(os.path.join(d, "report.md")).readline().strip().lstrip("# ")
    options.append({"label": os.path.basename(d), "description": first_line})

Then ask:

code

Which analysis directory should I use?

with those options. Use the user's choice as analysis_dir.

2. Read the Report and Data

Load the key artifacts from the analysis directory:

•report.md — The main analysis report. Read it fully.
•results.csv — The raw data. Load it into pandas for potential follow-up analysis.
•survey.md — The survey structure. Read it for context about question wording and types.

python

import pandas as pd

report_md = open(f"{analysis_dir}/report.md").read()
survey_md = open(f"{analysis_dir}/survey.md").read()
df = pd.read_csv(f"{analysis_dir}/results.csv")

Also read any other files in the directory (mermaid diagrams, existing charts) to have full context.

3. Create Output Directory

Create a directory named with a slugified version of the question:

python

import re
import os

def slugify(text, max_length=60):
    """Convert text to a URL-friendly slug."""
    text = text.lower().strip()
    # Remove punctuation except hyphens
    text = re.sub(r'[^\w\s-]', '', text)
    # Replace whitespace with hyphens
    text = re.sub(r'[\s_]+', '-', text)
    # Remove leading/trailing hyphens
    text = text.strip('-')
    # Truncate to max_length at a word boundary
    if len(text) > max_length:
        text = text[:max_length].rsplit('-', 1)[0]
    return text

slug = slugify(question)
output_dir = f"{analysis_dir}/{slug}"
os.makedirs(output_dir, exist_ok=True)

For example:

•"What is the sample size?" → analysis_1/what-is-the-sample-size/
•"How does weight vary by position?" → analysis_1/how-does-weight-vary-by-position/

If the directory already exists, append a numeric suffix: what-is-the-sample-size-2.

4. Answer the Question

Attempt to answer the question in this order:

a) Check the existing report first. If the report already contains the answer, extract and summarize it. No additional analysis needed.

b) If the report doesn't fully answer the question, do additional analysis. Write and run Python scripts against results.csv to compute statistics, generate charts, or perform deeper analysis. Save any generated charts or data files to the output directory.

c) If the question requires information not available in the data, say so clearly and explain what data would be needed.

When performing additional analysis, save the Python script as analysis.py in the output directory for reproducibility.

5. Write the Answer

Write the answer as answer.md in the output directory:

markdown

# [Question text here]

[Clear, concise answer in 1-3 paragraphs]

## Supporting Evidence

[Tables, statistics, or references to charts that back up the answer]

## Additional Context

[Any caveats, limitations, or related observations — only if relevant]

Keep answers direct and focused. Lead with the answer, then provide evidence.

If charts or visualizations were generated, reference them with relative paths:

markdown

![Description](chart_name.png)

6. Write Metadata

Save a meta.json file in the output directory:

python

import json
from datetime import datetime, timezone

meta = {
    "question": question,
    "slug": slug,
    "analysis_dir": analysis_dir,
    "timestamp": datetime.now(timezone.utc).isoformat(),
    "files": os.listdir(output_dir),
    "required_additional_analysis": True  # or False
}

with open(f"{output_dir}/meta.json", "w") as f:
    json.dump(meta, f, indent=2)

7. Generate HTML (optional)

If the answer includes charts or tables, convert to HTML for easy viewing:

bash

# Locate the bundled CSS using Glob("**/assets/report.css")
CSS_FILE="<discovered_css_path>"
pandoc "${output_dir}/answer.md" \
    -o "${output_dir}/answer.html" \
    --css="${CSS_FILE}" \
    --standalone

8. Report Back

Tell the user:

•The answer (summarized directly in chat)
•The path to the output directory with all files

Output Files

File	Description
`answer.md`	The answer in markdown format
`answer.html`	Styled HTML version of the answer (if generated)
`meta.json`	Metadata: question, timestamp, files list, flags
`analysis.py`	Python script used for additional analysis (if any)
`*.png`	Charts or visualizations generated (if any)

Example

User runs:

code

/answer-question What is the average weight by position?

Skill finds ./analysis_1/ (most recent), reads report.md and results.csv, sees the report already has a position breakdown, extracts and reformats it, and produces:

code

analysis_1/what-is-the-average-weight-by-position/
├── answer.md
├── answer.html
└── meta.json

If the report didn't have that breakdown, the skill would write and run a script:

code

analysis_1/what-is-the-average-weight-by-position/
├── answer.md
├── answer.html
├── meta.json
├── analysis.py
└── weight_by_position.png

Tips

•Always read the full report before deciding whether additional analysis is needed
•When doing additional analysis, save the script so results are reproducible
•Keep answers concise — a paragraph or two plus a table is usually sufficient
•Reference existing charts from the parent analysis directory when they're relevant rather than regenerating them
•Use relative paths for all file references so the directory is portable