CtrlK
BlogDocsLog inGet started
Tessl Logo

autoresearch

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

71

Quality

87%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, actionable body with a clear multi-step workflow, concrete output schema, and built-in validation via scoring rounds and quality gates. The main gaps are a missing prompt template for the panel-scoring call, placeholder output-writing commands, and some repeated guidance.

Suggestions

Provide a concrete prompt template for the 5-expert panel batch-scoring call (the persona lenses are defined, but the actual scoring prompt structure is only described, not shown).

Replace the placeholder '# Write optimized content / experiments JSON / optimization report' comments with concrete write commands or a small script, so Step 5 is copy-paste ready.

State the batch-once API rule once in the Step 3 execution protocol and remove the duplicate restatements in the panel, round-structure, and anti-patterns sections to tighten token use.

DimensionReasoningScore

Conciseness

Mostly efficient and assumes Claude's intelligence (no preamble on what A/B testing or landing pages are), but the batch-rule ('Never call the API once per variant') is restated 3-4 times across the panel, round-structure, step-3, and anti-patterns sections — minor over-explanation that could be trimmed.

4 / 5

Actionability

Concrete guidance throughout: full experiments JSON schema, named models, $ANTHROPIC_API_KEY env var, and a clear step sequence. Minor gaps: the core panel-scoring API call is described ('Batch-score all 10 with the 5-expert panel') rather than given as a prompt template, and the output step uses placeholder comments ('# Write optimized content').

4 / 5

Workflow Clarity

Steps 1-6 are clearly sequenced with a defined round structure, explicit stop condition, and a round-3 feedback loop targeting the weakest dimension; quality gates act as a checklist. Validation (scoring/ranking) is built into the batch operation, so the batch-cap does not apply, though the validate->fix->retry loop is implicit rather than explicit.

4 / 5

Progressive Disclosure

Well-organized with clear section headers and no nested references; content (per-type score dimensions, JSON schema) is reasonably kept inline. No bundle files exist, and the only referenced file (references/founder-voice.md) is described as user-created rather than a provided reference, so structure is scored on the in-file organization, which is good with minor gaps.

4 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, concrete description that explicitly states capabilities, trigger phrases, and use cases in third-person voice. It clearly answers both what the skill does and when to invoke it, with low conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete, quantified actions — 'Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log' — matching the comprehensive-coverage anchor.

5 / 5

Completeness

Clearly answers both what (generate/score/evolve/output) and when ('Use when optimizing landing pages, email sequences, ad copy...') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Explicit quoted natural triggers ('optimize this page', 'run autoresearch', 'score these variants', 'A/B test this copy') plus an enumerated list of content types give comprehensive coverage of phrases a user would actually say.

5 / 5

Distinctiveness Conflict Risk

The 'Karpathy-style autoresearch' / '5-expert simulated panel' framing plus conversion-content triggers carve a clear niche with minimal overlap risk against other skills.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
ericosiu/ai-marketing-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.