OpenFlare/.agents/skills/autoresearch/references/plan-workflow.md

Plan workflow — autoresearch plan mode

Auto-detect the project stack, propose a complete autoresearch configuration, do a dry run, and hand the ready-to-run command back to the user.

No manual goal/scope/verify required. Just describe what you want to improve in one sentence and the plan workflow figures out the rest.

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Invocation

autoresearch plan <goal in plain english>

Examples:

autoresearch plan improve test coverage
autoresearch plan make the app faster
autoresearch plan reduce the bundle size
autoresearch plan fix all TypeScript errors
autoresearch plan improve the SEO of my blog posts
autoresearch plan shrink the Docker image

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What the plan workflow does

Step 1 — Detect project stack

Scan the project root for signal files:

File found	Stack detected
package.json + jest.config.*	Node.js + Jest
package.json + vitest.config.*	Node.js + Vitest
next.config.*	Next.js
Dockerfile	Docker
*.tf	Terraform
.github/workflows/*.yml	GitHub Actions CI
content/blog/*.md OR posts/*.md	Markdown content/blog
src/**/*.ts OR src/**/*.tsx	TypeScript project
pyproject.toml OR setup.py	Python project
requirements.txt + pytest	Python + pytest
go.mod	Go project
Cargo.toml	Rust project

Print detected stack. If ambiguous, list the top two candidates and ask the user to confirm before proceeding.

Step 2 — Map goal to metric + verify command

Use the goal description and detected stack to propose:

Goal keyword	Metric	Verify command template
"test coverage"	coverage % (higher is better)	npm test -- --coverage | grep "All files"
"bundle size" / "build size"	size in KB (lower is better)	npm run build 2>&1 | grep "First Load JS"
"TypeScript errors" / "type errors"	error count (lower is better)	npx tsc --noEmit 2>&1 | grep -c "error TS" || echo "0"
"lighthouse" / "performance score"	score 0-100 (higher is better)	npx lighthouse http://localhost:3000 --output json --quiet 2>/dev/null | jq '.categories.performance.score * 100'
"docker image" / "image size"	size in MB (lower is better)	docker build -t bench . -q && docker images bench --format "{{.Size}}"
"flaky tests"	failure count (lower is better)	for i in {1..5}; do npm test 2>&1; done | grep -c "FAIL" || echo "0"
"SEO" / "blog" / "content"	SEO score (higher is better)	node scripts/seo-score.js <detected content path>
"lines of code" / "complexity"	LOC count (lower is better)	find src/ -name "*.ts" | xargs wc -l | tail -1 | awk '{print $1}'
"CI pipeline" / "pipeline speed"	seconds (lower is better)	node scripts/estimate-ci-time.js
"Python tests" / "pytest"	coverage % (higher is better)	pytest --cov=src --cov-report=term-missing | grep "TOTAL"
"faster" / "performance" / "latency"	p95 ms (lower is better)	npm run bench 2>&1 | grep "p95"

Step 3 — Detect scope

Based on goal + stack, propose the tightest scope that covers the goal:

• Test coverage → src/**/*.ts, src/**/*.test.ts
• Bundle size → src/**/*.tsx, src/**/*.ts
• Docker → Dockerfile, .dockerignore
• SEO → content/blog/*.md or detected content directory
• TypeScript errors → src/**/*.ts
• CI pipeline → .github/workflows/*.yml

Step 4 — Dry run

Run the proposed Verify command once against the current state.

• If it exits 0 and outputs a number → baseline confirmed, proceed
• If it exits non-zero → diagnose and fix the verify command before proposing
• If it hangs → propose a faster alternative

Step 5 — Output the ready-to-run command

Print this exact block for the user to copy-paste or confirm:

=== Autoresearch plan ===
Stack:    <detected stack>
Goal:     <interpreted goal>
Scope:    <proposed scope>
Metric:   <metric name> (<higher/lower> is better)
Verify:   <verify command>
Baseline: <dry run result>

Ready to run. Confirm or adjust any field, then:

/autoresearch
Goal:   <goal>
Scope:  <scope>
Metric: <metric>
Verify: <verify command>

Or, for an unattended run, put these same fields into a Qoder Automation prompt
(see "Unattended / overnight mode" in SKILL.md).
===

If the user says "looks good" or "run it" — start the autoresearch loop immediately without requiring them to retype the command.

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Web research calibration

After the dry run, use WebSearch to calibrate:

• For SEO goals: search for [target keyword] to see what top results look like. Note any structural patterns (FAQ sections, word count, heading structure) that the current content lacks. Add these as initial hypotheses.

• For performance goals: search for [framework] performance benchmarks [year] to calibrate whether the baseline is already good or has significant headroom.

• For security goals: search for [stack] common vulnerabilities [year] to seed the initial hypothesis pool with known attack vectors.

This research step happens during plan, not during the loop — so it adds context once without slowing down iterations.

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Edge cases

Goal is too vague ("make it better"): Ask one clarifying question: "Better in what way — speed, quality, size, coverage, or something else?" Then proceed.

Multiple valid verify commands exist: Propose the fastest one. Note the slower alternative in a comment.

Verify command requires a running server: Note this in the plan output. Add a # requires: local server on :3000 comment. Suggest the user start it before running the loop.

No matching stack detected: Ask the user to describe their stack in one sentence, then proceed with a custom verify command.