BC linkfox-amazon-opportunity-search-by-metrics
亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse search, niche metrics filter, low-competition niche, blue ocean niche, demographic-based selection, pain-point niche, price tier opportunity, sweet spot pricing, brand fragmentation时触发此技能。即使用户未明确说"反向选品"，只要其需求是按商业维度筛选符合条件的亚马逊赛道，也应触发此技能。
亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+…
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:24High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Market size & growth | `nich…Gte`, `nichePeakSearchVolumeAtLeastGte`, `nicheSearchVolumeYoyChangePctAtLeastGte`, `nichePeakMonthGte/Lte` | "Big enough market", "fast-growing"
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:25High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Competition density | `nicheBrandCountLte`, `nicheBrandCountYoyChangePctAtLeastLte`, `nich…Lte`, `feat…Lte` | "Newcomer-friendly", "brands fr
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:65High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)3. **Pair complementary signals**: Brand-level + product-level concentration (`feat…Lte` + `nich…Gte`) reveals "brands fragmented but products
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:93High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded){"revi…pic": "size", "revi…Gte": 70, "limit": 25}quoted
Files scanned: 1. 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")
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 35 steps, 3 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2852 tokens
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (7 tags): a typed call is more reliable
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
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 434: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.