deepline-pre-research

Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.

Quality

92%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

85%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 body is highly actionable with explicit gates, validation feedback loops, and well-structured one-level-deep references. Its main weakness is conciseness: reinforcement of the same rules across multiple sections adds tokens that could be consolidated.

Suggestions

Consolidate the Artifact Resolution Gate rules so they appear once (in §4.55) and are only briefly referenced from Non-Negotiables and Finish Criteria, rather than restated in full in each place.

Trim the bolded emphatic restatements (e.g. 'is not done. ... is done', 'no exceptions') where the operative instruction already conveys the rule, to reduce token cost without losing clarity.

Consider moving the large markdown report template (§7) into a references file and summarizing its section list inline, since it is a format spec rather than core decision logic.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and avoids explaining known concepts, but it is lengthy with notable repetition — the Artifact Resolution Gate is restated in Non-Negotiables, §4.55, and Finish Criteria, and bolded rule emphasis recurs throughout, so it could be tightened.	3 / 5
Actionability	It provides fully executable, copy-paste-ready guidance: install/auth CLI commands, parallel `deepline tools search` invocations, curl calls to the planning API, a python query-design script invocation, specific query shapes, and a complete markdown report template.	5 / 5
Workflow Clarity	A clear Standard Flow (steps 1–7 with sub-steps 4.25/4.5/4.55/4.6) is gated by explicit validation checkpoints — Required Coverage Gate, Artifact Resolution Gate, Fanout/Consolidation Gate, Approval Gate before paid runs — with feedback loops and a Finish Criteria checklist.	5 / 5
Progressive Disclosure	A 'Start Here' reading list signals one-level-deep references to real files (references/source-map.md, query-design.md, fanout-consolidation.md, last30days-gtm-corpus.md) and real scripts (query_design.py, evaluate_examples.py), with deep detail appropriately split out of the overview.	5 / 5
	Total	18 / 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.

The description is third-person, concise yet comprehensive, and explicitly pairs a 'Use when...' trigger with a concrete 'Triggers:' keyword list. It clearly states what the skill does and when to invoke it, with strong specificity and low conflict risk.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — 'discover the critical public, private, CRM, workflow, social, and web data sources', 'compare provider coverage', 'estimate Deepline credit cost', 'recommend the source plan', 'build custom language/messaging' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	It explicitly answers both 'what' (discover sources, compare coverage, estimate cost, recommend plan, build custom language) and 'when' (an opening 'Use when the user wants...' clause plus a dedicated Triggers list), with concrete trigger phrases.	5 / 5
Trigger Term Quality	An explicit 'Triggers:' clause provides comprehensive natural terms and synonyms a user would say — 'pre-research', 'source discovery', 'provider strategy', 'ScrapeCreators', 'X/Twitter data', 'Reddit comments', 'CRM data', 'custom language', 'messaging language', 'pain language'.	5 / 5
Distinctiveness Conflict Risk	The Deepline/GTM pre-research niche is clearly bounded with distinct triggers (last30days-style, ScrapeCreators, provider strategy) and minimal overlap risk with generic research skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: getaero-io/gtm-eng-skills
Path: skills/deepline-pre-research/SKILL.md
Commit: 3bb8968

Reviewed: 3 days ago

Table of Contents

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