deepline-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

89%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

81%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 highly actionable, well-sequenced orchestration skill with explicit gates and executable commands at every step. Its main costs are token weight — the artifact-resolution rule repeated ~5× and a long inline report template — plus a few references pointing at files that don't ship in the bundle.

Suggestions

State the artifact-resolution requirement once (e.g. in the §4.55 Artifact Resolution Gate) and reference it by section number from the Non-Negotiables, §3, the report template, and Finish Criteria, cutting ~4 restatements.

Move the ~80-line report template in §7 into a reference file (e.g. references/report-template.md) and keep only the section headings and cleanup rules in SKILL.md.

Ship or prune dangling references: evals/side-by-side.md, ../deepline-gtm/SKILL.md, ../deepline-analytics/SKILL.md, and THIRD_PARTY_NOTICES.md are cited but not present in the bundle.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient operational instruction (no explanations of concepts Claude already knows), but the artifact-resolution requirement is restated roughly five times (Non-Negotiables, §3, §4.55, the §7 report template, Finish Criteria) and an ~80-line report template is inlined, which is more than minor over-explanation. Not anchor 2 because the bulk is dense, non-padded domain guidance rather than several unnecessary explanations.	3 / 5
Actionability	Guidance is fully executable throughout: copy-paste bash for install/auth/preflight ("deepline preflight --json"), tool searches with the required "DEEPLINE_SKIP_SELF_UPDATE=1" prefix, "deepline tools describe <tool-id> --json", a complete SKILL_ROOT discovery loop invoking "python3 "$SKILL_ROOT/scripts/query_design.py" "$OBJECTIVE" --depth default", and concrete curl examples for the planning API. Specific examples cover the common cases.	5 / 5
Workflow Clarity	The Standard Flow is clearly sequenced (parse job → prefer APIs → public fanout → planning API → tool search → query design → coverage/artifact/consolidation gates → describe → probe → report → approval) with explicit validation checkpoints: preflight must complete standalone before fanout, the Coverage/Artifact Resolution gates must pass before recommending, and the Approval Gate requires an exact approval question before any paid run. Error-recovery behavior and failure conditions ("a report that names 'the SEC ADV bulk feed' fails this gate") are explicit.	5 / 5
Progressive Disclosure	The "Start Here" section routes by task type to four real, one-level-deep reference files (source-map.md, last30days-gtm-corpus.md, query-design.md, fanout-consolidation.md — all present) and the referenced scripts exist. It falls short of anchor 5 because the ~80-line report template and detailed gate rules are inlined in SKILL.md rather than split out, and several referenced targets are missing from the bundle (evals/side-by-side.md, ../deepline-gtm/SKILL.md, ../deepline-analytics/SKILL.md, THIRD_PARTY_NOTICES.md).	4 / 5
	Total	17 / 20 Passed

Description

92%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 description: it pairs concrete, comprehensive capability statements with an explicit use-when clause and a long natural-language trigger list. The only weakness is a handful of missing natural trigger variations that users might phrase differently.

Suggestions

Add natural trigger variations users commonly say, such as "data enrichment", "lead research", "prospect research", and "market research", to the Triggers list.

Consider trimming the trigger list's near-duplicates (e.g. "custom language", "messaging language", "pain language") in favor of broader natural phrasings, since concise and clear beats long and padded.

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. It clearly matches the anchor for multiple specific concrete actions rather than the anchor-4 case of minor coverage gaps.	5 / 5
Completeness	It explicitly answers both questions: the what is the series of concrete actions (discover sources, compare coverage, estimate cost, recommend the plan, build language), and the when is stated as "Use when the user wants…" plus a dedicated "Triggers:" phrase list. This matches the anchor for clearly and explicitly answering both with concrete trigger phrases.	5 / 5
Trigger Term Quality	The explicit trigger list ("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") covers many natural user phrases with synonyms. It falls short of anchor 5 because a few natural terms users would say are missing, e.g. "data enrichment" (enrichment appears only in the what-clause), "lead/prospect research", and "market research".	4 / 5
Distinctiveness Conflict Risk	The description occupies a clear niche (Deepline/GTM pre-research and source planning) with distinct triggers like "ScrapeCreators", "Deepline credit cost", and "CRM data" that would not fire for generic research or document skills. The only overlap is with intentionally related sibling skills (deepline-gtm, last30days), which is minor and doesn't create wrong-skill trigger risk.	5 / 5
	Total	19 / 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-research/SKILL.md
Commit: a9b9731

Reviewed: 6 days ago

Table of Contents

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