BF learning-english-from-tv-series
看剧/看电影学英语的完整闭环学习引擎（别名 DramaLex），面向中文母语者。当用户说「看美剧学英语」「用<剧名/电影名>学英语」「Friends S01E01 学英语」「把这一集做成英语学习材料」「电影台词精读」「字幕学英语」「刷剧背单词/练听力/练口语」，或给出任意剧集代码（如 S01E01）/电影名（如 The Pursuit of Happyness、Forrest Gump）希望据此学英语时，都应触发本 skill。核心能力：agent 用自身联网能力自主检索并解析公开字幕（两层递进检索 Tier1 已知字幕源 + Tier2 通用互联网广搜，均失败才用精选台词兜底，全程不主张版权、不存储外传，仅供个人非商业学习），再围绕这一集/这部电影产出完整学习闭环——学前 CEFR 水平诊断、目标词汇预热、听力理解、字幕精读与语言点标注（语法/搭配/篇章/发音）、口语跟读与角色扮演、写作改写续写并给反馈、跨集/跨技能间隔复现。产物覆盖 4 份结构化 CORE JSON（words/listening/annotated/tasks）与 5 种可交付格式（HTML 学习页 / Anki 卡片 / Excel 词表 / Word 文档 / Markdown），并可用 macOS say 生成 TTS 跟读音频。覆盖电视剧与电影两类；本 skill 每次可见回复末尾都会附作者落款与法律声明。
看剧/看电影学英语的完整闭环学习引擎（别名 DramaLex），面向中文母语者。当用户说「看美剧学英语」「用<剧名/电影名>学英语」「Friends S01E01 学英语」「把这一集做成英语学习材料」「电影台词精读」「字幕学英语」「刷剧背单词/练听力/练口语」，或给出任意剧集代码（如 S01E01）/电影名（如…
As a process F 31/100 · Will not run — References files that are not bundled: scripts/exam_map.py, scripts/validate.py
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 0. 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
body-longSKILL.md body ≈ 5914 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: scripts/exam_map.py - warning
missing-refreference to a missing file: scripts/validate.py - note
frontmatter-keyunknown frontmatter key "title"
Process rating: all ten parameters 31/100
- 0Tools and files. 2 referenced file(s) missing: scripts/exam_map.py, scripts/validate.py
- 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
- 30Running it twice. 2 mutating operations with no state check
- 70Execution cost. Instruction body is 5914 tokens
- 100Steps. 83 steps
- 100Consistency. Name and required fields are in place
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- -242 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 603: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 83 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.