resume-quantifier

Find opportunities to add metrics and estimate numbers when exact data unavailable

Quality

30%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/resume-quantifier/SKILL.md

Quality

Discovery

32%

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 conveys a general idea of what the skill does—adding metrics and estimating numbers when exact data is unavailable—but lacks the specificity and explicit trigger guidance needed for reliable skill selection. It has no 'Use when...' clause, uses somewhat abstract language ('find opportunities'), and misses common user-facing trigger terms that would help Claude match this skill to relevant requests.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to quantify claims, add data points to writing, or needs estimated figures for proposals, reports, or presentations.'

Include natural trigger terms users would say, such as 'quantify', 'add data', 'statistics', 'KPIs', 'benchmarks', 'approximate', or 'back up claims with numbers'.

Specify concrete actions more clearly, e.g., 'Identifies vague claims in text and replaces them with estimated metrics, adds quantitative benchmarks, and suggests data-backed alternatives when exact figures are unavailable.'

Dimension	Reasoning	Score
Specificity	The description names a domain (metrics/numbers estimation) and describes some actions ('find opportunities to add metrics', 'estimate numbers'), but lacks concrete specifics about what types of metrics, what contexts, or what outputs are produced.	2 / 3
Completeness	The description addresses 'what' at a high level (finding opportunities to add metrics and estimating numbers) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, missing 'Use when' caps completeness at 2, and the 'what' is also weak, so this scores a 1.	1 / 3
Trigger Term Quality	Includes some relevant keywords like 'metrics' and 'estimate numbers', but misses common variations users might say such as 'quantify', 'data points', 'statistics', 'KPIs', 'benchmarks', or 'approximate figures'. The phrase 'find opportunities' is not something a user would naturally say.	2 / 3
Distinctiveness Conflict Risk	The concept of adding metrics and estimating numbers is somewhat specific but could overlap with data analysis skills, writing improvement skills, or business reporting skills. The niche is identifiable but not sharply delineated.	2 / 3
	Total	7 / 12 Passed

Implementation

27%

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

This skill is far too verbose, spending most of its token budget on concepts Claude already knows (metric categories, role-specific metrics, why numbers matter on resumes). The actionable core—the estimation techniques, templates, and output format—is buried in excessive reference material. The content would benefit enormously from being reduced to ~50 lines focusing on the workflow, output format, and a few key templates, with role-specific details moved to separate files or eliminated entirely.

Suggestions

Cut the content by 70%+: remove 'Why Quantification Matters', the exhaustive role-specific metric lists, and the categories of metrics section—Claude already knows these concepts.

Extract role-specific metric discovery and common situations into a separate reference file (e.g., METRICS_BY_ROLE.md) and link to it from the main skill.

Add an explicit workflow sequence at the top: 1) Identify unquantified bullets → 2) Ask discovery questions → 3) Apply estimation technique → 4) Verify with user → 5) Format output.

Consolidate the estimation techniques into a compact table or decision tree rather than verbose sections with multiple examples each.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~300+ lines. Explains obvious concepts Claude already knows (why quantification matters, what categories of metrics exist, what sales/marketing/HR metrics are). The 'Studies Show' section, the exhaustive role-specific metric lists, and the lengthy estimation technique explanations are all things Claude inherently understands. Most of this content is reference material Claude doesn't need.	1 / 3
Actionability	Provides templates and before/after examples which are somewhat actionable, but there's no executable code or concrete step-by-step process. The templates use placeholder format strings rather than being fully fleshed out. The discovery questions and role-specific lists are more reference than instruction.	2 / 3
Workflow Clarity	The output format section provides a reasonable workflow structure (analyze → ask questions → quantify → note estimations), but the overall skill lacks a clear sequenced process with validation checkpoints. The quality checklist at the end is helpful but disconnected from the workflow. No explicit feedback loop for verifying estimates with the user.	2 / 3
Progressive Disclosure	Monolithic wall of text with no references to external files. The role-specific metric lists, estimation techniques, and common situations could all be split into separate reference files. Everything is inline in one massive document with no navigation structure beyond headers.	1 / 3
	Total	6 / 12 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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: paramchoudhary/resumeskills
Commit: 24c6edc

Reviewed: 12 days ago

Table of Contents

Discovery Implementation Validation

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