agentleFS
sunnnybalasunnnybala/Rstack/analyze-results/SKILL.md

analyze-results

Skill69 starsChanged 7 months agoMITView on GitHub

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.

  • Installs packages

10 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Show its rules and text

We split this skill into the rules it gives an agent

20 rules. 0 are common, 8 are distinctive, and 12 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 8

    Distinctive

    This author's own choices. Worth a look.

  • 12

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If output shows UPGRADE_AVAILABLE <old> <new>: read rstack-upgrade/SKILL.md and follow the "Inline Upgrade Flow".

    line 76
  2. Very specific:

    If output shows JUST_UPGRADED <from> <to>: tell user "Running RStack v{to} (just updated!)" and continue.

    line 77
  3. Very specific:

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

    line 79
  4. Very specific:

    If TEL_PROMPTED is no: Ask the user about telemetry.

    line 81
  5. Very specific:

    If A: run rstack-config set telemetry community

    line 92
  6. Distinctive:

    If B: ask a follow-up:

    line 94
  7. Very specific:

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

    line 101
  8. Very specific:

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

    line 102
  9. Very specific:

    Read .rstack/experiments.jsonl.

    line 121
  10. Very specific:

    Read results/ directory to find raw outputs (metrics.json, figures/, stdout.log for each run).

    line 122
  11. Distinctive:

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

    line 127
  12. Distinctive:

    Read from experiments.jsonl.

    line 132
  13. Very specific:

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

    line 134
  14. Very specific:

    Write a Python script to analysis/scripts/fig_{name}.py

    line 154
  15. Very specific:

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

    line 158
  16. Distinctive:

    Never invent data points. Every number comes from experiments.jsonl or results/ files.

    line 194
  17. Distinctive:

    Always generate both PNG and PDF versions of figures (PNG for preview, PDF for LaTeX).

    line 195
  18. Distinctive:

    Use clean, publication-quality style: no gridlines, readable fonts, proper axis labels.

    line 196
  19. Distinctive:

    If only one experiment run exists, skip ablation charts and note "single run, no comparison possible."

    line 197
  20. Distinctive:

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

    line 203

“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Preamble (run first)

# --- 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:

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:

_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):

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:

{
  "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.

# --- 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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.