CtrlK
BlogDocsLog inGet started
Tessl Logo

yc-batch-evaluator

Evaluate YC batch companies for investment — scrapes the YC directory, researches each company and its founders (work history, LinkedIn, website), assesses founder-company fit, and exports to Google Sheets with priority rankings. Use when asked to evaluate YC companies, research a YC batch, screen startups, or do due diligence on YC companies.

62

Quality

75%

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

Fix and improve this skill with Tessl

tessl review fix ./skills/lead-generation/capabilities/yc-batch-evaluator/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

A detailed, well-sequenced operational skill whose main weakness is execution correctness: every multi-field curl payload has the same JSON syntax error. Beyond that, a large batch workflow lacks explicit validation checkpoints and the single monolithic file foregoes progressive disclosure.

Suggestions

Fix the malformed `-d` JSON in every curl example: the proxy wrapper object must not close before the request body fields — merge `api`/`path` with `website_url`/`user_prompt`/`body` etc. inside one JSON object (e.g. `-d '{"api":"scrapegraph","path":"/v1/smartscraper","website_url":"...","user_prompt":"..."}'`).

Add an explicit validation checkpoint in the batch loop — e.g. after each company's research, verify the row wrote successfully and re-trigger any failed scrape/Apollo call before moving to the next batch — to satisfy the batch-operations feedback-loop requirement.

Split the large body into one-level-deep reference files (e.g. references/api-reference.md for endpoint payloads, references/formatting-rules.md for column layout and link formatting, references/scoring-rubric.md for fit/overall/rank guidance) and keep SKILL.md as a concise overview that links to them.

DimensionReasoningScore

Conciseness

Almost all content is API-specific operational detail Claude does not already know (endpoints, field-name variants, column layouts), so it avoids explaining known concepts; however the 'live-fill demo effect' point is restated three times and several emphatic 'do NOT'/'do not skip' directives are redundant, so it could be tightened.

2 / 3

Actionability

Commands are highly concrete with exact endpoints, expected response shapes, and column mappings, but every multi-field curl `-d` payload is malformed JSON (the wrapper object closes with `}` before `website_url`/`user_prompt`, and those fields sit outside the quoted string), so the examples are not copy-paste executable as written.

2 / 3

Workflow Clarity

Steps 1–5 are clearly sequenced with a parallelization strategy and per-failure fallbacks, but this batch operation over ~22 companies has no explicit validate-then-proceed checkpoint, which caps workflow clarity at 2 per the batch-operations rule.

2 / 3

Progressive Disclosure

The ~510-line body is well-sectioned but monolithic with no bundle files; the API reference, formatting rules, fit/scoring rubrics, and cost table are inline content that would navigate better split into one-level-deep reference files.

2 / 3

Total

8

/

12

Passed

Description

100%

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, third-person description that states concrete capabilities and an explicit 'Use when...' trigger with multiple natural phrasings. It cleanly answers what the skill does and when to invoke it, with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions — 'scrapes the YC directory, researches each company and its founders (work history, LinkedIn, website), assesses founder-company fit, and exports to Google Sheets with priority rankings' — matching the score-3 anchor.

3 / 3

Completeness

Explicitly answers both 'what' (evaluate YC batch companies for investment with the listed actions) and 'when' via the 'Use when asked to evaluate YC companies, research a YC batch...' trigger clause.

3 / 3

Trigger Term Quality

Covers natural phrasings a user would actually say — 'evaluate YC companies', 'research a YC batch', 'screen startups', 'do due diligence on YC companies' — giving good coverage of common variations.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche — YC batch investment due diligence — with distinct YC/startup-screening triggers unlikely to collide with other skills.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (515 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
gooseworks-ai/goose-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.