BF hr-resume-scorer
简历批量筛选打分工具。支持PDF/Word/TXT格式简历解析，基于JD进行匹配度分析，多维度评分，生成Excel/JSON/Markdown报告。当用户需要筛选简历、批量评估候选人、简历打分、匹配度分析、生成筛选报告时触发此技能。适用于HR、猎头、招聘团队。
简历批量筛选打分工具。支持PDF/Word/TXT格式简历解析，基于JD进行匹配度分析，多维度评分，生成Excel/JSON/Markdown报告。当用户需要筛选简历、批量评估候选人、简历打分、匹配度分析、生成筛选报告时触发此技能。适用于HR、猎头、招聘团队。
As a process F 35/100 · Will not run — References files that are not bundled: scripts/resume_scorer.py, references/scoring_criteria.md, references/resume_parsing_guide.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: scripts/resume_scorer.py - warning
missing-refreference to a missing file: references/scoring_criteria.md - warning
missing-refreference to a missing file: references/resume_parsing_guide.md - warning
missing-refreference to a missing file: references/personality_analysis_guide.md - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 35/100
- 0Tools and files. 4 referenced file(s) missing: scripts/resume_scorer.py, references/scoring_criteria.md, references/resume_parsing_guide.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Steps. 61 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3136 tokens
- 100Running it twice. No mutating operations
- low 15 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 130: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 61 items
- +4Has examples (23 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.