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Analyze Results

Community
sunnnybala
analyze-results

Turn raw experiment outputs into publication-ready figures, tables, and statistical analysis. Use when asked to "analyze results", "make figures", "create tables", or "visualize experiments". Proactively invoke after /experiment produces results.

Overview

Publishersunnnybala
RepositoryRstack
Skill nameanalyze-results
Stars
71
Forks
10
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sunnnybala on GitHub. Read the source before you install it.

Installation

Install the Analyze Results AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sunnnybala/Rstack/tree/main/analyze-results
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sunnnybala/Rstack.git /tmp/Rstack
mkdir -p .claude/skills
cp -r /tmp/Rstack/analyze-results .claude/skills/analyze-results
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyze Results in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyze Results on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyze Results is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preamble (run first)

bash
# --- GENERATED PREAMBLE START ---
_PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
if ! git rev-parse --show-toplevel >/dev/null 2>&1; then
  echo "WARNING: Not inside a git repository. Files will be written to $(pwd)."
fi
mkdir -p ~/.rstack/sessions ~/.rstack/analytics "$_PROJECT_ROOT/.rstack"
touch ~/.rstack/sessions/"$PPID"
find ~/.rstack/sessions -mmin +120 -type f -delete 2>/dev/null || true
_SESSIONS=$(find ~/.rstack/sessions -mmin -120 -type f 2>/dev/null | wc -l | tr -d ' ')
_RSTACK_DIR="$(cd "$(dirname "$0")/.." 2>/dev/null && pwd || echo "$HOME/.claude/skills/rstack")"
_RSTACK_CONFIG="$_RSTACK_DIR/bin/rstack-config"
_UPD=$("$_RSTACK_DIR/bin/rstack-update-check" 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true
_VENUE=$("$_RSTACK_CONFIG" get venue 2>/dev/null || echo "arxiv")
_COMPUTE=$("$_RSTACK_CONFIG" get compute_preferred 2>/dev/null || echo "modal")
_PROACTIVE=$("$_RSTACK_CONFIG" get proactive 2>/dev/null || echo "true")
_TEL=$("$_RSTACK_CONFIG" get telemetry 2>/dev/null || echo "off")
_TEL_PROMPTED=$([ -f ~/.rstack/.telemetry-prompted ] && echo "yes" || echo "no")
_BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
echo "PROJECT_ROOT: $_PROJECT_ROOT"
echo "BRANCH: $_BRANCH"
echo "VENUE: $_VENUE"
echo "COMPUTE: $_COMPUTE"
echo "PROACTIVE: $_PROACTIVE"
if [ ! -f ~/.rstack/.setup-complete ]; then
  echo "NEEDS_SETUP"
fi
_TEL_START=$(date +%s)
_SESSION_ID="$$-$(date +%s)"
echo "TELEMETRY: ${_TEL:-off}"
echo "TEL_PROMPTED: $_TEL_PROMPTED"
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"analyze-results","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","repo":"'$(basename "$(git rev-parse --show-toplevel 2>/dev/null)" 2>/dev/null || echo "unknown")'"}'  >> ~/.rstack/analytics/skill-usage.jsonl 2>/dev/null || true
fi
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"analyze-results","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","session_id":"'"$_SESSION_ID"'","rstack_version":"'"$(cat "$_RSTACK_DIR/VERSION" 2>/dev/null | tr -d "[:space:]" || echo "unknown")"'"}'  > ~/.rstack/analytics/.pending-"$_SESSION_ID" 2>/dev/null || true
fi
for _PF in $(find ~/.rstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null); do
  if [ -f "$_PF" ]; then
    _PF_BASE="$(basename "$_PF")"
    _PF_SID="${_PF_BASE#.pending-}"
    [ "$_PF_SID" = "$_SESSION_ID" ] && continue
    if [ "$_TEL" != "off" ] && [ -x "$_RSTACK_DIR/bin/rstack-telemetry-log" ]; then
      "$_RSTACK_DIR/bin/rstack-telemetry-log" --event-type skill_run --skill _pending_finalize --outcome unknown --session-id "$_SESSION_ID" 2>/dev/null || true
    fi
    rm -f "$_PF" 2>/dev/null || true
  fi
  break
done
eval "$("$_RSTACK_DIR/bin/rstack-slug" 2>/dev/null)" 2>/dev/null || true
echo "SLUG: ${SLUG:-unknown}"
# --- GENERATED PREAMBLE END ---

If output shows UPGRADE_AVAILABLE <old> <new>: read rstack-upgrade/SKILL.md and follow the "Inline Upgrade Flow". Then continue with this skill. If output shows JUST_UPGRADED <from> <to>: tell user "Running RStack v{to} (just updated!)" and continue.

If output shows NEEDS_SETUP: tell user to run /setup first.

If TEL_PROMPTED is no: Ask the user about telemetry. Use AskUserQuestion:

Help RStack get better! Community mode shares usage data (which skills you use, how long they take, crash info) with a stable device ID so we can track trends and fix bugs faster. No code, file paths, or repo names are ever sent. Change anytime with rstack-config set telemetry off.

Options:

  • A) Help RStack get better! (recommended)
  • B) No thanks

If A: run rstack-config set telemetry community

If B: ask a follow-up:

How about anonymous mode? Just a counter that helps us know if anyone's out there.

Options:

  • A) Sure, anonymous is fine
  • B) No thanks, fully off

If B→A: run rstack-config set telemetry anonymous If B→B: run rstack-config set telemetry off

Always run:

bash
touch ~/.rstack/.telemetry-prompted

This only happens once. If TEL_PROMPTED is yes, skip this entirely.

Important: Note the PROJECT_ROOT value from the preamble output. All file paths below are relative to this project root directory. Work products (analysis/, figures) go at the project root. Plumbing (.rstack/experiments.jsonl) goes in the .rstack/ subdirectory.

Step 0: Load Experiment Data

First, create output directories:

bash
_PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
mkdir -p "$_PROJECT_ROOT/analysis/figures" "$_PROJECT_ROOT/analysis/tables" "$_PROJECT_ROOT/analysis/scripts"
  1. Read .rstack/experiments.jsonl. If it does not exist or is empty, tell user: "No experiment results found. Run /experiment first or provide results manually."
  2. Read results/ directory to find raw outputs (metrics.json, figures/, stdout.log for each run).
  3. Count completed runs. If fewer than 2, warn: "Only {N} completed runs. Results may not be meaningful. Consider running more experiments."

Step 1: Generate Comparison Table

Create a LaTeX-formatted comparison table comparing all experiment runs:

RunHypothesisMetricValueImproved?Duration

Read from experiments.jsonl. Include baseline (first run or user-specified baseline) and all subsequent runs. Highlight the best result.

Write table source to analysis/tables/comparison.tex.

Step 2: Generate Figures

For each visualization needed, generate a self-contained Python matplotlib script and run it locally:

Training curves (if metrics.json contains per-epoch data):

python
import matplotlib.pyplot as plt
import json
# Read metrics, plot loss/accuracy curves, save to analysis/figures/

Ablation chart (if multiple experiment variants exist):

  • Bar chart comparing metric across runs
  • Error bars if multiple seeds were used

Other figures as appropriate for the experiment type (confusion matrix, attention maps, etc.).

For each figure:

  1. Write a Python script to analysis/scripts/fig_{name}.py
  2. Run it: python analysis/scripts/fig_{name}.py
  3. Output PNG + PDF to analysis/figures/

If matplotlib is not installed, run pip install matplotlib first.

Step 3: Statistical Summary

Write analysis/stats.json with:

json
{
  "total_runs": 5,
  "completed_runs": 4,
  "failed_runs": 1,
  "best_run": "run-003",
  "best_metric": {"name": "val_loss", "value": 0.342},
  "baseline_metric": {"name": "val_loss", "value": 0.456},
  "improvement": "25.0%",
  "figures_generated": ["loss_curve.png", "ablation.png"],
  "tables_generated": ["comparison.tex"]
}

Step 4: Human Checkpoint

Show the user:

  • Summary table (text format)
  • List of generated figures (read and display the PNGs)
  • Key finding: "Best result: {metric} = {value} (run-{N}), {X}% improvement over baseline"

Use AskUserQuestion:

Figures and tables generated. {N} figures, {M} tables. Best result: {metric_name} = {value} ({improvement}% vs baseline).

A) Looks good — proceed to paper writing B) Re-run analysis with different parameters C) Need more experiments first

Important Rules

  • Never invent data points. Every number comes from experiments.jsonl or results/ files.
  • Always generate both PNG and PDF versions of figures (PNG for preview, PDF for LaTeX).
  • Use clean, publication-quality style: no gridlines, readable fonts, proper axis labels.
  • If only one experiment run exists, skip ablation charts and note "single run, no comparison possible."

Telemetry (run last)

After the skill workflow completes (success, error, or abort), log the telemetry event.

bash
# --- GENERATED EPILOGUE START ---
_TEL_END=$(date +%s)
_TEL_DUR=$(( _TEL_END - _TEL_START ))
rm -f ~/.rstack/analytics/.pending-"$_SESSION_ID" 2>/dev/null || true
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"analyze-results","duration_s":"'"$_TEL_DUR"'","outcome":"OUTCOME","session":"'"$_SESSION_ID"'","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"}' >> ~/.rstack/analytics/skill-usage.jsonl 2>/dev/null || true
fi
if [ "$_TEL" != "off" ] && [ -x "$_RSTACK_DIR/bin/rstack-telemetry-log" ]; then
  "$_RSTACK_DIR/bin/rstack-telemetry-log" \
    --skill "analyze-results" --duration "$_TEL_DUR" --outcome "OUTCOME" \
    --session-id "$_SESSION_ID" --pipeline-stage "analyze-results" 2>/dev/null &
fi
# --- GENERATED EPILOGUE END ---

Replace OUTCOME with success/error/abort based on the workflow result.

Frequently asked questions

What does the Analyze Results AI skill do?

Turn raw experiment outputs into publication-ready figures, tables, and statistical analysis. Use when asked to "analyze results", "make figures", "create tables", or "visualize experiments". Proactively invoke after /experiment produces results.

Why use Analyze Results on TypingMind?

Because you install it once and use it with any model. Analyze Results is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyze Results in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sunnnybala/Rstack/tree/main/analyze-results. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyze Results?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyze Results?

As many as you like. As long as a model supports skills, you can use Analyze Results with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyze Results AI skill free?

Yes. It is published on GitHub by sunnnybala under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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