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deepline-scoring

Use when discovering niche signals, auditing ICP or won/lost evidence, rescoring accounts, or building account and lead scoring Plays. Triggers on fit scoring, engagement scoring, backtesting, holdout evaluation, predictive lift, external proxies, and scoring leakage. Skip pure outreach copy or contributor skill installation tasks.

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%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 dense, highly actionable skill body with executable commands, precise data contracts, and a well-structured reference routing table. Weaknesses are limited to readability of the overloaded workflow steps, implicit (rather than explicit) failure-recovery loops around the quality gate, and a few unrouted bundle files.

Suggestions

Split the overloaded workflow steps (especially 1, 3 and 6) into shorter sub-bullets so each rule is individually scannable.

Add an explicit quality-gate failure path (e.g. 'if the gate fails: fix coverage, re-run the pilot, re-gate before scaling') to close the feedback loop.

Either route references/pitfalls.md and the unrouted scripts from the task table, or remove them from the bundle so every file is discoverable.

DimensionReasoningScore

Conciseness

The body is dense and rule-per-sentence with no padding and no explanation of concepts Claude already knows ('lookalikes are not wins', 'Reviews are not calls, openings are not hires'), but workflow steps 1-3 and 6 are overloaded multi-clause paragraphs that could be split or trimmed, matching 'minor instances of over-explanation that could be tightened' rather than the lean top anchor.

4 / 5

Actionability

Fully executable guidance throughout: copy-paste-ready 'python3 scripts/analyze_signals_v2.py' invocations with realistic flags, an exact CSV column contract ('domain,status,website,jobs; optional account_id,parent_id,split,known_at,scored_at'), concrete JSON source-adapter shapes, and config schema examples covering the common discovery and validation cases.

5 / 5

Workflow Clarity

The 9-step workflow has a clear sequence and explicit checkpoints (pilot one or two rows then pass the quality gate, freeze artifacts before validation, blocked-state handling for missing prerequisites), but what to do when the quality gate fails is only implicit ('Imputation or dropping missingness flags can still encode collection bias') rather than an explicit fix-and-revalidate loop, keeping it below the top anchor.

4 / 5

Progressive Disclosure

Excellent one-level-deep structure: a task-indexed routing table pointing to 16 real reference files, all of which exist in references/, plus routed scripts. Minor gaps: references/pitfalls.md is an orphan never linked from the body, and several bundle scripts (scoring_contract.ts, replay-score.play.ts, dedupe_utils.py, technology_evidence.py, find_contacts_v2.py) have no body-level navigation.

4 / 5

Total

17

/

20

Passed

Description

87%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 with an explicit use-when clause, concrete niche triggers, and clear skip boundaries. The main gap is that several capabilities the skill body actually supports (backtesting, capacity and technology evidence, scorecard creation) are not reflected, leaving trigger coverage slightly short of comprehensive.

Suggestions

Add one or two capability phrases for the unrouted work the body covers, e.g. 'backtesting account populations' or 'building artifact-backed scorecards', to lift specificity and trigger coverage.

Include a couple of plainer-language variations users may say ('account scoring model', 'lead scoring', 'prioritize accounts') alongside the current technical triggers.

DimensionReasoningScore

Specificity

Lists four concrete domain actions ('discovering niche signals', 'auditing ICP or won/lost evidence', 'rescoring accounts', 'building account and lead scoring Plays'), but omits capabilities the body actually covers (backtesting delivery, capacity/technology evidence, scorecard creation), a minor coverage gap that keeps it below the comprehensive anchor.

4 / 5

Completeness

Explicitly answers both 'what' (discover niche signals, audit ICP/won-lost evidence, rescore accounts, build scoring Plays) and 'when' ('Use when...' plus a dedicated 'Triggers on...' clause with concrete trigger phrases), with bonus skip guidance; it clearly matches the top anchor rather than the 4 anchor whose 'when' is only adequately specific.

5 / 5

Trigger Term Quality

Good natural keyword coverage for the niche ('fit scoring, engagement scoring, backtesting, holdout evaluation, predictive lift, external proxies, and scoring leakage' plus 'ICP' and 'rescoring'), but misses common variations a user might say such as 'account scoring model', 'lead scoring', or 'prioritize accounts', so it falls just short of the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

Clear niche (GTM account/lead scoring) with distinct domain triggers and an explicit exclusion clause ('Skip pure outreach copy or contributor skill installation tasks'), giving minimal conflict risk with other skills.

5 / 5

Total

18

/

20

Passed

Validation

100%

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-scoring/SKILL.md

Reviewed

Table of Contents

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