BB autoresearch-loop
Apply Karpathy's autoresearch methodology to iteratively improve anything measurable — Claude skills, n8n workflows, system prompts, business processes, or any artifact with a clear quality metric. Inspired by github.com/karpathy/autoresearch (56k stars). The loop: propose a change → test it → measure against the target metric → keep if better, discard if not → repeat until a stopping condition is met. Trigger this skill when the user explicitly requests an iterative improvement loop, e.g.: "improve this skill automatically", "iterate on this workflow", "run autoresearch on", "run experiments on this", "optimize this automatically", "set up an improvement loop", or "run the autoresearch method".
Apply Karpathy's autoresearch methodology to iteratively improve anything measurable — Claude skills, n8n workflows, system prompts, business processes, or…
As a process B 73/100 · Nearly there — weak spots: consistency, execution cost
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
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low Risky intent
intent-offensive-securitySKILL.md:97Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)**Eval quality and designer bias** — if every new artifact hits 100% within 2–3 sessions, your evals are probably too easy. The risk is amplified when the same person designs the eval and runs the loo
quoted
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 13127 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 410): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 73/100
- 40Consistency. Frontmatter name (autoresearch-loop) differs from the folder (as-autoresearch-loop)
- 40Execution cost. Instruction body is 13127 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (git, node) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 85 steps, 3 vague phrases
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 8 branches, has a failure section
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +1No license
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 704: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 85 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.