flingjie

@flingjie source repo

43 skills
0 followers
6 hours ago last updated

▌
Web Article Summarizer · flingjie bundle
name: web-researcher

0
installs
▌
Ec Draw · flingjie bundle
Generate hand-drawn Excalidraw diagrams. Use when the user asks to draw, sketch, or create a diagram, flowchart, architecture diagram, concept diagram, or any visual diagram. Trigger on "draw", "sketch", "diagram", "flowchart", "visualize", "excalidraw".

0
installs
▌
Fde Gym · flingjie bundle
FDE Gym — a bilingual (zh-CN default, en-US selectable) Forward-Deployed Engineering training product. This Skill turns a learner's natural-language training intent into exactly one safe `fde-gym` CLI command and renders back only the learner-safe envelope.

0
installs
▌
Strategic Issue Tree · flingjie bundle
用 MECE 问题树方法帮助用户结构化复杂技术战略决策，找到关键杠杆点，生成行动方案。

0
installs
▌
Tavily Search · flingjie
使用 Tavily API 进行实时网页搜索。当用户需要搜索最新信息、查找资料、核实事实、研究话题时触发。支持自然语言如 "搜索XX"、"查一下XX"、"帮我找XX"、"search for XX"、"look up XX" 等。也作为 /tavily-search slash command 被调用。

0
installs
▌
Idea To Draft · flingjie bundle
把已确认的 Idea（ContentJob）写成一篇中文原创草稿（Draft），仅作 Assist 模式（代写）。用于「我没时间练习 / 已经想清楚，直接帮我写」类请求；只依据 job 语境（读者问题 / 作者立场 / 核心主张 / 边界）写正文，不搜索新来源、不绑定证据卡；草稿过 Critic（6 检查器， Safety 硬门禁）+ 有限 rewrite 后落库为 Draft + CriticReport，进入人工审核。

0
installs
▌
Voice Profile · flingjie bundle
初始化和更新个人表达画像（VoiceProfile）。输入历史发布内容、expression-practice 最终版、人工修改后的采用版本、明确拒绝的表达及理由，输出 preferred_patterns / avoid_phrases / rhythm_rules / approved_examples / rejected_examples，并展示可追溯 diff 后由用户确认写入。用于「更新我的声音画像」「把这篇采用稿记进我的风格」「这条为什么不像我」类请求。

0
installs
▌
Idea Discovery · flingjie bundle
把个人证据（Commit/PR/测试）、零散思考（用户片段）、真实交流（已验证 ConversationEvidence）或社区信号（同行共同主题 + 未解问题/分歧）提炼成一个值得继续发展的 Idea（IdeaCandidate）。四种来源只是输入不同，判断同一件事：这里有没有「读者值得知道」的真实工程决策或问题。用于「把我最近的提交 / 这个片段 / 这次交流 / 这些社区信号变成一个可写的想法」类请求。

0
installs
▌
Peer Discovery · flingjie bundle
从公开讨论（Twitter/X 搜索）中发现值得长期交流的同行与有实践证据的 builder，生成轻量交流机会（Opportunity）与 PeerProfile 候选。判断一条帖子背后的人是否值得持续交流—— 是否与用户问题/兴趣重叠、有实践深度、有可贡献空间、有延续潜力；新闻 / 融资 / 纯推广 / 最近已表达的内容跳过。用于「帮我看看今天有哪些人」「这个话题里有哪些人值得了解/交流」类请求。

0
installs
▌
Sticky Message · flingjie bundle
Transform complex ideas, products, technologies, and strategies into messages people understand, remember, and repeat, using the SUCCESs framework from Made to Stick (Chip & Dan Heath). Use when a message isn't landing — it feels abstract, hard to explain, hard to remember, or reads like a feature list ("帮我把这个方案讲清楚", "这个文案怎么优化", "make this clearer", "写个 hook / one-liner / 开场白", "position my product", "turn this into a story"). The goal is to rediscover what the idea should truly say, NOT to polish prose. It communicates an already-formed idea; it does NOT validate demand (→ idea-validation) or discover Jobs (→ job-discovery).

0
installs
▌
Feynman Practice · flingjie
Practice the Feynman Technique by making the user explain a concept, then probing for undefined terms, circular reasoning, missing causality, and false confidence, so hidden gaps in understanding become visible — the goal is to debug the user's understanding, not to explain the concept to them. Use when the user wants to test or deepen their own grasp of a concept ("帮我把这个概念真正搞懂", "检验我是不是真懂了", "用费曼技巧练一下", "test my understanding", "probe where my explanation breaks down", "explain it to me simply"). It turns "我以为我懂了" into "我能用简单的话讲清楚". It does NOT craft a message for an external audience (→ sticky-message) or analyze a sales/buyer conversation (→ human-selling).

0
installs
▌
Weekly Reflection · flingjie bundle
把一周的关系、表达、讨论和结果转成下一周的重点（LLM 定性复盘）。输入关系质量指标（有意义互动 / 继续对话 / 重复同行 / 协作信号 / 待跟进对话）、对话线索、Critic 报告、 VoiceProfile 变化与发布后反馈；只回答五个问题并给出一个下周训练重点。用于「帮我做本周复盘」「这周该练什么表达」类请求。

0
installs
▌
Expression Practice · flingjie bundle
Finch 核心表达训练 Skill：通过实际表达提升能力。选择 Idea → 用户先表达 → 诊断最大问题 → 追问一个问题 → 用户重新表达 → 对比前后 → 保存最终版。绝不先给范文、一次只问一个关键问题、默认不自动 rewrite、保存用户原文和每次修改。用于「帮我练一下这个想法的表达」「我想自己写、你来追问」类请求。

0
installs
▌
Conversation Follow Up · flingjie bundle
找出值得继续的对话，恢复上下文并提出下一步。输入 ConversationThread（open_questions / observation_notes / commitments / agreements / disagreements），输出需要跟进的理由与下一步建议。用于「有哪些对话该继续」「这条对话下一步怎么回」「把这段试用反馈记到对话」类请求。

0
installs
▌
Writing Style Analysis · flingjie bundle
分析一段文本或链接内容的可观察写作特点，包括开头、结构、节奏、用词、立场、具体性和读者关系，并提炼可借鉴但不复制的表达方法。用于“分析这篇文章的风格” “这段文字有什么特点”“我可以从这个作者身上学什么”等请求。只生成 StyleReport，不判断是否由 AI 创作，不直接改写文本或更新 VoiceProfile。

0
installs
▌
Interaction Preparation · flingjie bundle
基于具体同行、帖子和用户证据生成互动建议（回复 / 引用 / 私信 / 最小贡献）。输入 PeerProfile 与选中的 Opportunity，产出待批准的 InteractionProposal（含草稿或试用/复现清单）。用于「帮我给这个人准备一次有价值的互动」「先给一个二十分钟内能做的测试」类请求。

0
installs
▌
Digest · flingjie
ALWAYS use this skill when the user wants to verify their true understanding of a book, principle/theory, or code repository — not to learn, but to expose blind spots. Triggers: "/digest", "校验我对...的掌握", "验证我对...的理解", "我到底有没有真的懂", "test my understanding of", "费曼校验", "verify my grasp of". Runs a 5-layer adversarial interview (Core Concept → Reasoning Chain → Alternatives → Boundaries → Teach a Beginner), with Claude acting as a strict professor — no comfort praise, only precise gap detection. Each layer must pass three criteria (no fuzzy jumps, no circular definitions, withstand follow-up) before advancing. Supports re-test with `/digest [topic] --retest` for gap life-cycle tracking. Outputs a structured gap report to state/digest_gaps.jsonl for long-term blind-spot tracking. Do NOT use this skill when the user wants to learn new material, get a tutorial, or have a casual discussion about a topic — this is a rigorous verification tool, not a teaching tool.

0
installs
▌
Builderdna · flingjie bundle
ALWAYS use this skill when the user wants to analyze a GitHub developer or org, discover product/tool opportunities from developer activity, track tech trends in a domain (agent, LLM, MCP, etc.), or run BuilderDNA's analysis toolkit. Use when the user says "analyze X's GitHub", "what are people building in Y", "find opportunities in Z", "tech DNA", "builder insights", "trend radar", "what should I build", "developer landscape", "tech stack analysis", "competitive intelligence for X", or references BuilderDNA/builderdna directly. The skill wraps the core sandbox CLI commands (collect → trend → pain → opportunity → report) so the user never needs to remember flags — you translate intent into the right command chain. It does NOT replace the specialist skills (repo-trend, repo-awesome, twitter-learning, reddit-opportunity) or concept-radar (cross-source lifecycle). Reads state/hypotheses.json to track exploration across conversations. Uses goal-driven short-circuit pipeline to select the optimal execution path. A

0
installs
▌
Repo Trend · flingjie bundle
ALWAYS use this skill when the user wants to find, discover, search for, evaluate, compare, or track GitHub repositories — even if they don't explicitly say "trending" or "discover." Covers: discovering trending repos in any domain, searching for open-source tools by topic, evaluating individual repo quality (deep-dive), comparing repos side-by-side, tracking repos over time with watches, checking for new/updated repos since last scan, and any request that involves finding or assessing GitHub projects. Trigger phrases include "find me X repos", "trending repos in Y", "what's hot in Z", "discover X tools", "evaluate this repo", "compare top 3", "which repo is better for X", "I need a tool that does X", "track these repos", "check my watches", "what changed since last scan", "re-scan my watches", and any mention of repo-trend, repo discovery, or repo scout. Uses three-tier evaluation: quick API scan for discovery → full checklist for assessment → deep Claude reasoning for strategic recommendations. Pure gh CLI

0
installs
▌
Repo Awesome · flingjie bundle
ALWAYS use this skill when the user wants to find repos through community-curated lists, awesome lists, or expert recommendations — even if they don't explicitly say "awesome list." Mine hand-curated Awesome Lists on GitHub to discover high-quality repositories that human curators have vouched for. Use for: mining awesome-* lists for any topic, finding what experts recommend in a domain, discovering best-of-class tools from curated collections, extracting repos from markdown awesome lists, cross-referencing repos across multiple curated lists, and any request involving "curated", "hand-picked", "community-recommended", or "best-of" repos. Trigger phrases: "mine awesome lists for X", "awesome list for Y", "what do awesome lists recommend for Z", "curated X repos", "awesome X tools", "best X repos according to experts", "community-recommended X", "awesome-*", "what's in the awesome-X list". Unlike repo-trend (API search for trending repos), repo-awesome extracts repos from hand-curated markdown collections — ea

0
installs
▌
Concept Radar · flingjie bundle
Cross-source concept lifecycle radar that turns weak signals into validated, falsifiable builds. Use when the user wants to track an idea from a hunch to evidence across multiple sources ("validate an idea", "weak signals to validated builds", "hypothesis and evidence", "should I build or drop this", "concept radar", "track this concept", "cross-source signal", "雷达", "验证一个想法"), or asks to capture, scan, verify, review, build, or source-audit a concept. Owns cross-source synthesis and the Inbox → Watch → Verify → Build/Drop lifecycle. Single-source requests route to the specialist skills instead: X-only learning → twitter-learning, Reddit-only pain discovery → reddit-opportunity, GitHub-only discovery → repo-trend. X reply/engagement and customer outreach are out of scope for this project. Deterministic schemas, persistence, scoring, and gates live in the Python CLI; this skill orchestrates retrieval and semantic judgment through validated JSON contracts.

0
installs
▌
Trace Classify · flingjie
Classify raw tool-call traces into structured step-level trace files, and run periodic reviews to surface optimization insights. Works with traces captured by the PostToolUse hook (state/traces/capture.py).

0
installs
▌
Twitter Learning · flingjie bundle
Daily Twitter/X learning on a specified topic (default: agent): discover, filter, and score high-signal posts by Learning Score, then deeply analyze the Top 10 with a 7-angle framework to extract transferable insights, and grow a personal knowledge asset. Use when the user wants to LEARN from Twitter/X on a topic ("做今天的 X 情报", "帮我研究 Y 的推特", "今天 X 上有什么值得学的", "有哪些值得学习的高质量推文", "twitter learning", "daily twitter learning"), or to build a topic-specific knowledge base from daily signal. Defaults to the agent domain (Agent Engineering / Agent Solution / FDE / AI Engineering). Focuses only on learning (Top 10 high-value posts).

0
installs
▌
Concept Radar Loop · flingjie bundle
Resumable, command-driven orchestrator for the cross-source concept radar loop. Use when the user wants to run an end-to-end radar cycle over a configured radar ("run the concept radar loop", "使用 concept-radar-loop", "自动跑一遍 … 概念雷达", "resume my radar run", "继续跑概念雷达", "概念雷达循环"), or asks to start, resume, import, decide, or finalize a radar-cycle run. Drives the deterministic `builderdna radar-cycle` state machine (start → import source handoffs → decide → finalize), loading only the specialist skill each NextAction names (twitter-learning / reddit-opportunity / repo-trend). Single-source requests do NOT enter the loop — route them to the specialist skill; recurring-schedule requests route to automation. This skill never schedules future runs (no cron/daemon).

0
installs
▌
Reddit Opportunity · flingjie
ALWAYS use this skill when the user wants to discover product opportunities or pain points from a Reddit community and they do NOT yet have a product. Use when the user says "find problems people will pay to solve", "what should I build from r/...", "reddit opportunity", "discover product ideas from a subreddit", "需求发现", "从 Reddit 找商机", or asks to monitor/analyze a subreddit for recurring complaints. Monitors a subreddit's public RSS feed (no API key, no scraper), builds and updates a 7-section Subreddit Profile (the community profile), finds recurring problems, judges willingness to pay, and generates a product concept. Supports a single subreddit or a versioned feed preset, including the Agent startup opportunity radar. RSS returns posts only — no comments, no scores; analysis works on post bodies. After every run, present a ranked list of problems and ask whether to deep-dive.

0
installs
▌
Eval · flingjie
Evaluate agent behavior changes after configuration updates. Trigger on "/eval", "evaluate", "benchmark", "run eval", "测试效果", "评估".

0
installs
▌
Goal · flingjie
Goal and task management with superpower skills integration. Define goals, break them into typed subtasks with dependencies, and each task auto-maps to the right superpower skill for execution. Triggers on: "/goal", "/tasks", "set a goal", "create task", "todo list", "任务列表", "查看任务", "what's next", "track progress", "define goal". Tasks persist in state/goal.json across sessions. When executing tasks, invokes the matching superpower skill (brainstorming, tdd, systematic-debugging, subagent-driven-development, etc.) automatically.

0
installs
▌
Note · flingjie
Lightweight personal record keeper — capture moments that feel significant, amplify them with meaning, and let them feed into /reflect for pattern discovery. Triggers: "/note", "记一下", "take a note", "capture this", "weekly review", "周回顾", "展开记录", "amplify this", "看记录", "note list", "daily review", "日复盘". RAL model: Record (10s capture) → Amplify (2min meaning) → Layer (active|accumulating|archived). Records are stored in state/records.jsonl and loaded by /reflect as extra signal sources.

0
installs
▌
Distill · flingjie
Use when the user wants to synthesize accumulated reflections into a growth narrative and propose self-model updates. Triggers: "/distill", "synthesize my reflections", "growth report", "what have I learned recently", "aggregate insights", "蒸馏", "阶段性复盘", "总结最近的复盘", "这段时间有什么变化". Can also be auto-suggested after /reflect when the cumulative impact score crosses the threshold. Gathers all unprocessed reflections, produces a Tension + Resolution narrative, proposes user_dna.json diffs (including cognitive_patterns). Writes a markdown report to state/distill_reports/ and presents a conversational summary for user confirmation.

0
installs
▌
Reflect · flingjie
Use when the user wants to reflect on a conversation or experience to extract personal insights — values, abilities, and patterns. Triggers: "/reflect", "reflect on this", "analyze this conversation", "what did I learn here", "extract insights from this", "复盘". Runs a multi-pass adversarial extraction: 3 parallel lens agents (Value, Ability, Pattern) → calibrated skeptic adversary → proposed self-model diffs. Output is saved to state/reflections.jsonl. The user confirms/rejects each proposed diff inline before any file is written. Also loads unprocessed RAL records from state/records.jsonl as additional signal sources.

0
installs
▌
Show Me · flingjie
Help the user understand the current topic of conversation visually with concise diagrams, code-shape sketches, and focused HTML artifacts. Use whenever the user wants something explained visually, or asks to "show", "draw", "diagram", "sketch", "画个图", "画图", "示意图", "帮我理解", or wants to see the shape of code, data flow, control flow, or UI structure — even if they don't explicitly ask for a diagram.

0
installs
▌
Grilling · flingjie bundle
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

0
installs
▌
Optimize · flingjie
Diagnose→Propose→Apply→Verify loop for continuous improvement. Use when the user says "/optimize", "optimize", "improve", "优化", "提升", "fix", or wants to iteratively improve any output, configuration, or analysis result. This skill reads diagnostics from observability or recent session outputs, generates concrete improvement proposals, gets user confirmation, applies changes, re-runs verification, and records successful patterns. General-purpose optimization — not limited to any specific domain.

0
installs
▌
Visualize · flingjie
Generate interactive HTML visualizations from PiForge trace data. Use when the user wants to visually explore agent execution history, decision debates, tool call traces, or get a comprehensive dashboard. Triggers on: "/visualize", "/viz", "visualize", "可视化", "生成可视化", "show me the pipeline map", "visualize the execution", "生成流程地图", "工作流可视化", "可视化地图", "execution map", "trace map", "dashboard", "see the pipeline", "view execution history", "看看管道", "可视化管道", "生成仪表盘", "流程图".

0
installs
▌
Design Arena · flingjie
Multi-agent adversarial design review. Use when the user wants to debate design decisions, review an implementation plan, resolve architecture tradeoffs, or generate a TODO execution graph from a plan. Triggers on: "/arena", "design arena", "design debate", "design review", "方案辩论", "设计评审", "设计决策", "架构评审", "multi-agent design", "debate this plan", "review my architecture". Also use when a plan has "## Design Decision:" sections that need resolution.

0
installs
▌
Smart Commit · flingjie
Smart git commit workflow — auto-classify changed files into "commit" vs "ignore" buckets, present for user confirmation, generate a proper commit message, then commit and push. Use when the user says "commit", "提交", "push", "commit and push", or wants to save their work to git. This skill reads git status, applies project conventions (.gitignore, CLAUDE.md rules), classifies every changed file, and stages only explicit paths. Never stages build artifacts, personal data, or system files. User must confirm the file list and message before anything is committed.

0
installs
▌
Observability · flingjie
Use this skill when the user wants to check whether their actual behavior aligns with their stated values — mismatch detection between user_dna.json and observed patterns. Also use when the user says "check my predictions", "validate assumptions", "anything changed?", "verify past analysis", "我之前猜的对不对", "任何东西变了吗", "验证一下之前的预测", "观测", "行为对齐", "value drift", or references observability directly. This skill compares recent behavior (conversation patterns, decisions, expressed preferences) against the cognitive model in state/user_dna.json, flags value drift, and surfaces blind spots. Auto-suggest after every 3-5 value-discovery or significant decision-making conversations.

0
installs
▌
Value Discovery · flingjie
ALWAYS use this skill when the user wants to discover their own values, beliefs, and decision patterns — or when another skill triggers it for user onboarding. Also use when the user says "value discovery", "what do I value", "help me understand my preferences", "analyze my decision style", "cognitive model", "personal DNA", "了解自己", "价值发现", "我的偏好", "我做决策的模式", or references value-discovery directly. This skill runs a structured Meta Model interview to extract the user's cognitive decision model (Values → Beliefs → Criteria → Preferences) and writes it to state/user_dna.json. Important: if the user asks about understanding their own values, decision patterns, or preferences — use this skill. Don't try to extract cognitive models without it.

0
installs
▌
Pi Permission Config · flingjie
Read and configure Pi's project-level permission rules (.pi/permissions.json). Use when the user asks about Pi permissions, wants to view/edit permission rules, block/allow specific tools or commands, toggle auto mode, configure the judge LLM, or set up security policies for the coding agent. Triggers on -- "pi permission", "permission config", "权限", "permission rule", "allow/deny tool", "auto mode", "judge model", "安全策略", "block bash", "protect file", and any request involving Pi tool access control.

0
installs
▌
Writing Plans · flingjie bundle
Use when you have a spec or requirements for a multi-step task, before touching code

0
installs
▌
Executing Plans · flingjie
Use when you have a written implementation plan to execute in a separate session with review checkpoints

0
installs
▌
Subagent Driven Development · flingjie bundle
Use when executing implementation plans with independent tasks in the current session

0
installs
▌
Verification Before Completion · flingjie
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

0
installs

43 published skills