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loopforge
Loopforge:把模糊工作流整理成可复用的 AI Agent 工作流循环规范和提示词。
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Loopforge:把模糊工作流整理成可复用的 AI Agent 工作流循环规范和提示词。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
GEO 内容优化:调整文章结构,让内容更容易被 AI 搜索引用。
Grok X 实时抓取:让 Claude 调用本地 grok-build 获取真实 X (Twitter) 帖子。 X 模式必须优先驱动 Grok 原生 X 工具族:x_keyword_search、x_semantic_search、 x_user_search、x_thread_fetch;必要时再用 web_search/open_page 交叉验证。 返回带 @用户名、点赞/浏览数、链接、时间的真实帖子。复用用户已登录的 grok.com 订阅, 调用零额外成本(不像 MCP 方案要 xAI API key 按 token 付费)。 三种模式:x(X 实时抓取,主力)、ask(把 Grok 当独立第二意见)、continue(续接追问)。 当用户需要真实 X/Twitter 实时数据时使用,例如: 问问 grok X 上在聊什么、让 grok 搜 X 上对某事的实时讨论、grok 看看 @某账号最近发了什么、 X 上现在怎么说、X 实时热点、X 实时趋势、ask grok what X is saying about、 grok 第二意见、consult grok。 排除(不要触发本 skill):写 X 推文 → 用 x-twitter-writer;大规模历史语料采集 → 用 x-sousuo;泛网络调研 → 用 smart-research。本 skill 的差异点是 Grok 对公共 X 搜索 工具有原生路径,适合可复核的 X 话语采样;不是 firehose,也不是事实裁判。 没有"公共 X 搜索 / X 实时反馈 / thread 上下文"诉求时不要用它。
网红评估:分析社交账号公开数据,计算评分,辅助筛选 KOL 合作对象。
LinkedIn 帖子创作:根据品牌调性和主题生成专业帖子,并支持反馈优化。
SEO 分析:检查网站结构、技术指标和内容质量,输出搜索优化建议。
社交趋势监控:追踪多平台热点,生成趋势报告和内容机会判断。
| name | loopforge |
| description | Loopforge:把模糊工作流整理成可复用的 AI Agent 工作流循环规范和提示词。 |
| metadata | {"lifecycle_stage":"library","context_budget_tier":"production","source_inspiration":"Forward Future Loop Library, qiaomu-goal-meta-skill, yao-meta-skill"} |
把模糊工作流锻造成可复用的 AI Agent 工作流循环规范。
Choose one path:
Extract: convert notes, transcripts, prompts, artifacts, or history into a loop spec.Forge: create a new loop spec from the user's intended recurring workflow.Doctor: audit and minimally repair an existing loop spec.Package: prepare a loop spec for reuse or publication.Do not handle:
/goal writing; use a goal skill when availableAsk one high-leverage question by default; ask up to three only when answers materially change the spec. Default only when grounded or safe no-op. Ask for permissions, security, production writes, external messages, private data, and validation.
references/loop-vs-goal-sop-checklist.md.references/loop-spec-v1.md.references/loop-doctor.md.references/case-patterns.md and references/templates.md.Return one:
loop-spec-v1 YAML or JSON spec, followed by a copy-ready Loop Prompt companion unless the user asks for machine-only outputReady, Repair needed, or Not actually a loopEvery spec must satisfy references/loop-spec-v1.md: identity, trigger/exclusion, inputs, authority, state, round, next_action_rule, working_signal, acceptance_gate, terminal states, outputs, provenance, and optional execution_handoff.
When outputting YAML/JSON, also finish with the module-based Loop Prompt from references/templates.md. YAML/JSON is the source; the prompt is the copy/use entry.
For YAML/JSON specs, run:
python3 scripts/lint_loop_spec.py <spec-file>
Use --profile template for reusable templates with declared placeholders. Use --profile runnable when the loop is ready to run and placeholders must be gone.
If the linter fails, fix the spec before presenting it as ready.