跳到正文
cccskills
免费在 GitHub 公开

topic-brainstormer

Generate blog topic ideas: problem mining, gap analysis, expansion.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(4)

  • SKILL.md5.0 KB
  • references/content-filter.md7.1 KB
  • references/priority-scoring.md8.1 KB
  • references/topic-sources.md7.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Topic Brainstormer

Generate blog topic ideas that align with a content identity built around solving frustrating technical problems. Three phases: ASSESS, DECIDE, GENERATE. Every topic must pass the three-question content quality filter before presentation.

Deep References

SignalLoadContent
Filtering topics: three-question test, category examplesreferences/content-filter.mdFull quality filter with green/red/yellow topic categories, salvage patterns
Scoring: impact, vex, resolution matrixreferences/priority-scoring.mdRubrics per dimension, score ranges, calibration, worked examples
Mining: problem sources, gap analysis, expansionreferences/topic-sources.mdMining prompts, signal strength, gap types, expansion strategies

Phase 1: ASSESS

Gather context about existing content and available sources.

  1. Read all posts in the content directory. Document post count, content clusters, technologies covered, last post date.
  2. Identify available sources: problem mining (recent debugging, errors, config struggles), gap analysis (cross-references leading nowhere), tech expansion (adjacent technologies).
  3. Extract all "see also" and cross-reference mentions. Flag any pointing to content that does not exist.

Gate: Content landscape documented, at least 2 sources identified with material.

Phase 2: DECIDE

Generate candidates and filter through the content quality test.

  1. Mine 5-10 raw candidates from at least 2 sources. Capture: source type, raw topic, initial vex signal.
  2. Apply the content quality filter -- every candidate must answer YES to all three:
    • Was there genuine frustration? Real time lost, failed attempts, unclear docs, unexpected behavior.
    • Is there a satisfying resolution? Clear fix, understanding gained, prevention strategy, "a-ha moment."
    • Would this help others? Reproducible problem, actionable solution, relatable frustration.
  3. Reject failing candidates. Document each rejection: topic, failed question, reason.

Failure modes to catch:

  • Tutorial-only topics: "How to Set Up X" with vex listed as "learning a new tool" is not genuine frustration. Find the specific friction. "Hugo Local Build Works But Cloudflare Deploy Fails" has real vex.
  • Opinion without experience: "Why Go Is Better Than Python" is debate, not experience. Ground in measurement.

Gate: At least 3 candidates pass. Fewer than 3 -- return to Step 1 with different sources.

Phase 3: GENERATE

Score, prioritize, and present the filtered list.

Scoring: Priority = Impact(1-5) x Vex(1-5) x Resolution(1-5). Thresholds: 60-125 HIGH, 30-59 MEDIUM, 15-29 LOW, 1-14 SKIP.

Titles: Replace vague categories with failure-mode titles. Bad: "Kubernetes Networking Issues". Good: "Pod-to-Pod Traffic Works But Service Discovery Fails".

Output format: Grouped by priority tier. Each topic: title, vex, joy, content cluster fit, word estimate, score breakdown. End with recommendations: top pick, quick win, deep dive.

Tie-breaking: prefer topics that fill an existing gap, complement recent posts, use already-covered technologies, have clearer narrative structure.

Gate: All topics scored, prioritized, presented with recommendations.

Angle Lenses Mode

When the task asks for angles on a single topic (not fresh candidates), apply these six lenses instead of the content quality filter:

LensQuestion
Perspective shiftWhose view changes the story?
Ladder of abstractionOne rung up (the trend) or down (the single case)?
News valuesWhich value carries it: conflict, proximity, novelty, impact, human interest?
Data angleWhat does the dataset say that the narrative misses?
ContrarianWhat if the consensus framing is wrong?
Timeliness pegWhy now? What event or deadline makes this urgent?

Rules: one lens per kept angle (distinctness is structural). Each kept angle must pass the "so what?" gate -- one sentence naming a concrete reader payoff. Log refused angles with reason.

Error Handling

ErrorCauseSolution
No existing postsContent directory emptyFocus on problem mining; ask about recent debugging sessions
All candidates fail filterSources lack frustration signalsAsk probing questions: "What broke recently?" Shift sources.
Topic too broadCategory, not a specific problemBreak into failure modes: "[A] works but [B] fails"
Resolution unclearOngoing issue, no fix yetDefer until resolved; or assess if "understanding the workaround" suffices

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

accounts

免费

Manage multiple Claude Code accounts: add, list, check, launch, and install shell aliases for 10+ isolated CLAUDE_CONFIG_DIR profiles.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

Improve architecture across modules by deepening interfaces.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

Assessment: read-only inspection, codebase overview, value analysis, health checks, ADR consultation, decision analysis, multi-perspective critique.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

Background memory consolidation — overnight review, merge, and injection payload for memory files.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

Jev-driven browser automation: Jev picks operations, programs execute, a text model writes field values only when Jev cannot pick one from the goal.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.

中文简介正在准备中。当前显示原文说明。

notque/vexjoy-agent4422026年10月17日 更新

查看 notque 的全部技能

报告此技能的问题