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Found 432 Skills
People.ai (now Backstory) platform help — automatic activity capture, deal intelligence, pipeline health, revenue forecasting, MCP integration, Salesforce/Dynamics/Oracle CRM sync. Use when reps aren't logging activities and CRM data is stale, deals are slipping without warning and you need early risk signals, forecast accuracy is poor because it's based on gut not data, evaluating People.ai vs Gong vs Clari vs Revenue.io for revenue intelligence, activity data isn't tying back to pipeline or revenue outcomes, or you want to connect People.ai to AI agents via MCP. Do NOT use for conversation intelligence with call recording and transcription (use /sales-gong or /sales-note-taker), building outbound sequences (use /sales-cadence), or general CRM data cleanup strategy (use /sales-data-hygiene).
Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".
Assess chemical and drug toxicity via adverse outcome pathways, real-world adverse event signals, and toxicogenomic evidence. Integrates AOPWiki (AOPWiki_list_aops, AOPWiki_get_aop) for mechanism- level pathway tracing, FAERS for post-market adverse event quantification, OpenFDA for label mining, and CTD for chemical-gene-disease evidence. Produces structured toxicity reports with evidence grading (T1-T4). Use when asked about toxicity mechanisms, adverse outcome pathways, AOP mapping, FAERS signal detection, or chemical-disease relationships for drugs or environmental chemicals.
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".
Generate, refine, research, and validate startup ideas through an interactive founder-specific idea machine. Use when Codex needs to ask a few focused questions, search current public web signals, rapidly brainstorm startup or SaaS ideas, learn from love/maybe/no reactions, produce additional idea rounds, compare finalists, investigate competitors and existing workarounds, select a promising opportunity, or create an evidence-linked Markdown startup-idea report.
Guided journey from a raw app idea to a validated, cleanly architected first version that ships on a sustainable cadence. Orchestrates ten skills phase by phase - lean-startup, design-sprint, clean-architecture, domain-driven-design, clean-code, pragmatic-programmer, system-design, ios-hig-design, 37signals-way, software-design-philosophy - asking the user questions at every decision point and recording results in the project docs/ folder (PRODUCT.md, ARCHITECTURE.md, EXPERIMENTS.md, CREATE-APP-PLAN.md) so the journey resumes across sessions. Use when the user wants to build a new app, validate an idea before writing code, architect an MVP that will not need a rewrite, or says 'help me build my app the right way'. App already exists: use improve-app or grow-app. Business idea not yet validated: run create-business first. Marketing site only: use create-website. Architecture-only question: design-code-architecture. For one framework in isolation, invoke that skill directly.
Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated tweet actions. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing guarded tweet actions. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
The idea-system craft — never run out of content ideas by running a system instead of waiting for inspiration. Use when someone is out of ideas, stares at a blank calendar, asks where good ideas come from, wants an idea bank/backlog, feels their AI-generated ideas sound like everyone else's, or thinks their niche is "boring." Uses the SPARK framework. Reads brand-profile + social-strategy + content-pillars + audience-research first. The audience is the idea engine (comments/DMs/FAQs clustered into themes); AI expands real signals, it doesn't discover them; systems beat muses; trends are a timing filter, not a source. The agent clusters/expands/scores; the HUMAN supplies proprietary signals and decides; WoopSocial publishes the content, not ideas. Never rewords competitor posts, invents audience questions, or fabricates probe results. Distinct from social-strategy/content-pillars, audience-research, cross-platform-repurposing/ content-recycling, and the format skills.
Review AI-generated or human-written code changes with fallow's graph-grounded review brief. Subtracts deterministic concerns (unused code, complexity, duplication, styling) from the loop, ranks what to look at by blast radius and risk, and surfaces the few consequential structural decisions (new public-API contracts, coupling/boundary crossings, new dependencies) as framed judgment questions anchored to verifiable signals. Drives a closed agent-contract loop: fetch the walkthrough guide, return a judgment, and have fallow post-validate it against the live graph (hallucinated or stale judgments are rejected). Use when asked to review a PR, review a branch, review a diff, do a code review, or check changed code before merge.
Detects entropy signals in a codebase: stale TODOs, disabled tests, lint suppressions, commented-out code, dead imports, empty catch blocks, and deprecated API usage. Designed for daily runs to catch quality erosion early. Do NOT use for feature work, refactoring planning, or security audits.
Bun runtime API reference for TypeScript scripts. Covers Bun.file(), Bun.write(), Bun.$() shell, Bun.spawn(), Bun.Glob, Bun.env, bun:sqlite, Bun.sql() for PostgreSQL/MySQL via DATABASE_URL, Bun.s3 for S3-compatible storage, Bun.redis for Redis/Valkey, Bun.Archive for tarballs, Bun.Image image processing, Bun.WebView headless browser automation, Bun.cron in-process scheduler, JSONC/JSON5/JSONL/markdown (named imports), Bun.hash, Bun.password, compression, and scripting utilities. Use when writing scripts, automating tasks, querying databases, working with S3 storage, Redis caching, processing images, automating a headless browser, parsing markdown/JSON variants, or doing file processing in a Bun project. Signals: bun.lock, bunfig.toml, DATABASE_URL, REDIS_URL, AWS_ACCESS_KEY_ID, Bun.$ usage Not for bun CLI commands (bun-cli skill), non-Bun runtimes, or ORM CLI tooling