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gtm-enrichment-smart

Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.

59

Quality

69%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/lead-generation/capabilities/gtm-enrichment-smart/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

The content is well-structured and highly actionable with clear phased gating and error visibility, but malformed curl JSON bodies and the absence of validation checkpoints before paid/expensive calls hold back actionability and workflow clarity.

Suggestions

Fix the malformed curl JSON bodies (1a, 3a, 3b, 4a) so request fields are inside the -d JSON object and the commands are copy-paste runnable.

Add an explicit validation checkpoint before Phase 3 (Sixtyfour fallback) — e.g. confirm the projected spend with the user and verify prior phases truly returned no person data before incurring $0.10-$0.20 calls.

De-duplicate the cost/gating information between inline phase headers, the Cost Tracking table, and the Tips section to tighten token efficiency.

DimensionReasoningScore

Conciseness

The body is mostly efficient with concrete commands and field paths, but cost figures and gating logic are restated across inline phase headers, the Cost Tracking table, and the Tips section — minor padding that could be trimmed.

4 / 5

Actionability

Concrete curl commands and explicit field-extraction lists make the guidance largely executable, but several JSON request bodies are malformed (stray keys outside the -d JSON object in 1a, 3a, 3b, and 4a), which breaks copy-paste readiness.

4 / 5

Workflow Clarity

The phased sequence and conditional gates are clear and error-visibility is strong, but this paid, per-call batch operation has no validation checkpoint before firing expensive fallback calls (Phase 3) and no confirm-cost-with-user step, so the destructive/batch cap applies.

3 / 5

Progressive Disclosure

With no bundle files, the skill is a single well-organized SKILL.md with clear section headers and an example; per the simple/no-external-references guideline, well-organized single-file structure earns the top score.

5 / 5

Total

16

/

20

Passed

Description

66%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.

The description is specific and actionable with a clear, distinct niche, but it omits an explicit 'Use when...' trigger clause, which limits its completeness. Adding a natural trigger phrase would move it toward the top band.

Suggestions

Append a 'Use when...' clause naming natural trigger phrases, e.g. 'Use when enriching a lead from an email, looking up person/company data, or building a prospect list.'

Add a couple of common user phrasings (e.g. 'enrich this lead', 'look up this email') to improve trigger-term coverage.

Consider trimming the cost figure detail from the description to keep it lean and trigger-focused.

DimensionReasoningScore

Specificity

Lists several concrete actions — 'Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback' plus 'confidence scoring and full error visibility' — with only minor coverage gaps; not quite the comprehensive multi-action list of a 5.

4 / 5

Completeness

It clearly answers 'what does this do' but has no 'Use when...' clause or equivalent explicit trigger guidance, which the rubric caps at 3.

3 / 5

Trigger Term Quality

Natural terms like 'lead enrichment', 'email', and 'person + company data' are present and would match user requests, but a few common GTM phrasings (e.g. 'enrich this lead', 'look up this email') are not surfaced.

4 / 5

Distinctiveness Conflict Risk

The 'Multi-provider waterfall lead enrichment' niche with cost/confidence framing is mostly distinct from other skills, with only minor overlap risk against generic enrichment skills.

4 / 5

Total

15

/

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

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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