dabiggm0e/autoresearch-opencode/autoresearch

Claude Code and agent skill by dabiggm0e/autoresearch-opencode

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

Set up and run an autonomous experiment loop for any optimization target. Use when asked to start autoresearch or run experiments.

Install

npx vskill@latest install dabiggm0e/autoresearch-opencode/autoresearch

SKILL.md

Autoresearch

Autonomous experiment loop: try ideas, keep what works, discard what doesn't, never stop.

Setup

  1. Ask (or infer): Goal, Command, Metric (+ direction), Files in scope, Constraints.
  2. git checkout -b autoresearch/<goal>-<date>
  3. Read the source files. Understand the workload deeply before writing anything.
  4. mkdir -p experiments then write autoresearch.md, autoresearch.sh, and experiments/worklog.md (see below). Commit all three.
  5. Initialize experiment (write config header to autoresearch.jsonl) → run baseline → log result → start looping immediately.
autoresearch.md

This is the heart of the session. A fresh agent with no context should be able to read this file and run the loop effectively. Invest time making it excellent.

markdown
# Autoresearch: <goal>
## Objective
<Specific description of what we're optimizing and the workload.>
## Metrics
-**Primary**: <name> (<unit>, lower/higher is better)
-**Secondary**: <name>, <name>, ...
## How to Run
`./autoresearch.sh` — outputs `METRIC name=number` lines.
## Files in Scope
<Every file the agent may modify, with a brief note on what it does.>
## Off Limits
<What must NOT be touched.>
## Constraints
<Hard rules: tests must pass, no new deps, etc.>
## What's Been Tried
<Update this section as experiments accumulate. Note key wins, dead ends,
and architectural insights so the agent doesn't repeat failed approaches.>

Update autoresearch.md periodically — especially the "What's Been Tried" section — so resuming agents have full context.

autoresearch.sh

Bash script (set -euo pipefail) that: pre-checks fast (syntax errors in <1s), runs the benchmark, outputs METRIC name=number lines. Keep it fast — every second is multiplied by hundreds of runs. Update it during the loop as needed.


JSONL State Protocol

All experiment state lives in autoresearch.jsonl. This is the source of truth for resuming across sessions.

Config Header

The first line (and any re-initialization line) is a config header:

json
{"type":"config","name":"<session name>","metricName":"<primary metric name>","metricUnit":"<unit>","bestDirection":"lower|higher"}

Rules:

  • First line of the file is always a config header.
  • Each subsequent config header (re-init) starts a new segment. Segment index increments with each config header.
  • The baseline for a segment is the first result line after the config header.
Result Lines

Each experiment result is appended as a JSON line:

json
{"run":1,"commit":"abc1234","metric":42.3,"metrics":{"secondary_metric":123},"status":"keep","description":"baseline","timestamp":1234567890,"segment":0}

Fields:

  • run: sequential run number (1-indexed, across all segments)
  • commit: 7-char git short hash (the commit hash AFTER the auto-commit for keeps, or current HEAD for discard/crash)
  • metric: primary metric value (0 for crashes)
  • metrics: object of secondary metric values — once you start tracking a secondary metric, include it in every subsequent result
  • status: keep | discard | crash
  • description: short description of what this experiment tried
  • timestamp: Unix epoch seconds
  • segment: current segment index
Initialization (equivalent of init_experiment)

To initialize, write the config header to autoresearch.jsonl:

bash
echo '{"type":"config","name":"<name>","metricName":"<metric>","metricUnit":"<unit>","bestDirection":"<lower|higher>"}' > autoresearch.jsonl

To re-initialize (change optimization target), append a new config header:

bash
echo '{"type":"config","name":"<name>","metricName":"<metric>","metricUnit":"<unit>","bestDirection":"<lower|higher>"}' >> autoresearch.jsonl

Data Integrity Protocol

CRITICAL: JSONL data must never be corrupted or lost.

Pre-Write Validation (before appending to JSONL)

Before writing any new experiment result, validate the JSONL file:

bash
# Validate JSONL file before writing
validate_jsonl() {
local jsonl_file="autoresearch.jsonl"
if [[ -f "$jsonl_file" ]]; then
# Count existing runs
local run_count=$(grep -c '"run":' "$jsonl_file" 2>/dev/null || echo 0)
echo "Current runs in JSONL: $run_count" >&2
# Verify last 5 lines are valid JSON
tail -n 5 "$jsonl_file" 2>/dev/null | while IFS= read -r line; do
if ! echo "$line" | python3 -m json.tool >/dev/null 2>&1; then
echo "WARNING: Invalid JSON found in state file" >&2
return 1
fi
done
echo "JSONL validation: OK" >&2
return 0
fi
return 0 # File doesn't exist yet, that's OK
}
# Call validation before any write
validate_jsonl || {
echo " WARNING: JSONL validation failed. Proceeding with caution." >&2
}
Atomic Write Pattern

Never append directly to JSONL. Use atomic write pattern:

bash
write_jsonl_entry() {
local entry="$1"
local jsonl_file="autoresearch.jsonl"
local temp_file="${jsonl_file}.tmp.$$"
# Create temp file
cat "$jsonl_file" > "$temp_file" 2>/dev/null || touch "$temp_file"
# Append entry
echo "$entry" >> "$temp_file"
# Validate the new entry
if ! echo "$entry" | python3 -m json.tool >/dev/null 2>&1; then
rm -f "$temp_file"
echo " WARNING: Invalid JSON entry, not writing" >&2
return 1
fi
# Atomic move (guaranteed all-or-nothing)
mv "$temp_file" "$jsonl_file"
# Verify write succeeded
local new_count=$(grep -c '"run":' "$jsonl_file" 2>/dev/null || echo 0)
echo "Write verification: $new_count runs in JSONL" >&2
return 0
}
Post-Write Verification

After every write operation, verify the data was written correctly:

bash
verify_write() {
local expected_run=$1
local jsonl_file="autoresearch.jsonl"
if [[ -f "$jsonl_file" ]]; then
local actual_count=$(grep -c '"run":' "$jsonl_file" 2>/dev/null || echo 0)
if [[ "$actual_count" -lt "$expected_run" ]]; then
echo " WARNING: Run count mismatch! Expected $expected_run, got $actual_count" >&2
echo "This may indicate data loss in previous writes." >&2
return 1
fi
echo "Write verification: OK (run $expected_run present)" >&2
return 0
fi
return 1
}

User-Confirmable Actions

Before any user-confirmable action (e.g., manual intervention, major changes, discarding multiple experiments), create a backup:

bash
# Backup state before user-confirmable action
backup_before_confirm() {
echo " User confirmation required. Creating backup..." >&2
# Use backup utility if available
if [[ -f "./scripts/backup-state.sh" ]]; then
./scripts/backup-state.sh backup autoresearch.jsonl 2>/dev/null || true
else
# Fallback: simple backup
cp autoresearch.jsonl "autoresearch.jsonl.backup.$(date +%s)" 2>/dev/null || true
fi
echo "Backup created. Awaiting user confirmation..." >&2
}

Always call `backup_before_confirm` before any operation that requires user approval.


Dashboard Data Consistency Check

When generating the dashboard, check for data consistency:

Data Consistency Check

If the number of runs in autoresearch.jsonl doesn't match the number of entries in experiments/worklog.md:

  1. Check for backups: scripts/backup-state.sh list autoresearch.jsonl
  2. If backups exist: Restore with scripts/backup-state.sh restore-auto
  3. If no backups: Manually recreate missing runs from worklog notes
  4. Note the discrepancy in the dashboard header

Add this warning banner to the dashboard when inconsistency is detected:

markdown
**DATA INCONSISTENCY DETECTED**
-**Worklog documents**: <WORKLOG_RUN_COUNT> experiments
-**JSONL contains**: <JSONL_RUN_COUNT> runs
-**Missing**: <DIFF> runs **LOST!**
**Recovery steps:**
1.Check backups: `scripts/backup-state.sh list autoresearch.jsonl`
2.Restore if available: `scripts/backup-state.sh restore-auto`
3.Otherwise, manually recreate missing runs from worklog

Running Experiments (equivalent of run_experiment)

Run the benchmark command, capturing timing and output:

bash
START_TIME=$(date +%s%N)
bash -c "./autoresearch.sh" 2>&1 | tee /tmp/autoresearch-output.txt
EXIT_CODE=$?
END_TIME=$(date +%s%N)
DURATION=$(echo "scale=3; ($END_TIME - $START_TIME) / 1000000000" | bc)
echo "Duration: ${DURATION}s, Exit code: ${EXIT_CODE}"

After running:

  • Parse METRIC name=number lines from the output to extract metric values
  • If exit code != 0 → this is a crash
  • Read the output to understand what happened

Logging Results (equivalent of log_experiment)

After each experiment run, follow this exact protocol:

1. Determine status
  • keep: primary metric improved (lower if bestDirection=lower, higher if bestDirection=higher)
  • discard: primary metric worse or equal to best kept result
  • crash: command failed (non-zero exit code)

Secondary metrics are for monitoring only — they almost never affect keep/discard decisions. Only discard a primary improvement if a secondary metric degraded catastrophically, and explain why in the description.

2. Git operations

If keep:

bash
git add -A
git diff --cached --quiet && echo "nothing to commit" || git commit -m "<description>
Result: {\"status\":\"keep\",\"<metricName>\":<value>,<secondary metrics>}"

Then get the new commit hash:

bash
git rev-parse --short=7 HEAD

If discard or crash:

bash
git checkout -- .
git clean -fd

Use the current HEAD hash (before revert) as the commit field.

3. Append result to JSONL
bash
echo '{"run":<N>,"commit":"<hash>","metric":<value>,"metrics":{<secondaries>},"status":"<status>","description":"<desc>","timestamp":'$(date +%s)',"segment":<seg>}' >> autoresearch.jsonl
4. Update dashboard

After every log, regenerate autoresearch-dashboard.md (see Dashboard section below).

5. Append to worklog

After every experiment, append a concise entry to experiments/worklog.md. This file survives context compactions and crashes, giving any resuming agent (or the user) a complete narrative of the session. Format:

markdown
### Run N: <short description> — <primary_metric>=<value> (<STATUS>)
-Timestamp: YYYY-MM-DD HH:MM
-What changed: <1-2 sentences describing the code/config change>
-Result: <metric values>, <delta vs best>
-Insight: <what was learned, why it worked/failed>
-Next: <what to try next based on this result>

Also update the "Key Insights" and "Next Ideas" sections at the bottom of the worklog when you learn something new.

On setup, create experiments/worklog.md with the session header, data summary, and baseline result. On resume, read experiments/worklog.md to recover context.

6. Secondary metric consistency

Once you start tracking a secondary metric, you MUST include it in every subsequent result. Parse the JSONL to discover which secondary metrics have been tracked and ensure all are present.

If you want to add a new secondary metric mid-session, that's fine — but from that point forward, always include it.


Dashboard

After each experiment, regenerate autoresearch-dashboard.md:

markdown
# Autoresearch Dashboard: <name>
**Runs:** 12 | **Kept:** 8 | **Discarded:** 3 | **Crashed:** 1
**Baseline:** <metric_name>: <value><unit> (#1)
**Best:** <metric_name>: <value><unit> (#8, -26.2%)
| # | commit | <metric_name> | status | description |
|---|--------|---------------|--------|-------------|
| 1 | abc1234 | 42.3s | keep | baseline |
| 2 | def5678 | 40.1s (-5.2%) | keep | optimize hot loop |
| 3 | abc1234 | 43.0s (+1.7%) | discard | try vectorization |
...

This is the start of the file. Read the full SKILL.md on GitHub.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
127
Repository forks
12
7-day trend score
24

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 16 Mar 2026, 7 months ago.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/dabiggm0e/autoresearch-opencode/autoresearch/badge)](https://verified-skill.com/skills/dabiggm0e/autoresearch-opencode/autoresearch)

Something wrong with this skill or its page? Tell us.