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Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.
A copyable data-analysis prompt template for small teams and solo creators: fill in dataset description, business goal, and tool, and the LLM produces an analysis approach, SQL/pandas code, key insights, and visualization suggestions, with a pitfall quick-reference and FAQ.
A full self-hosting SOP for block/buzz (paired with the buzz-hive-mind hotspot piece): local dev stack (just setup/build/dev) plus production single-node (deploy/compose Docker, Postgres/Redis/MinIO) plus configuration (.env: RELAY_URL/BUZZ_RELAY_PRIVATE_KEY/RELAY_OWNER_PUBKEY) plus agent onboarding (Nostr keypair NIP-98 signing, buzz-admin manages members) plus closed relay plus 5 FAQ. All deployment commands are sourced from README/compose/.env/CLI/ARCHITECTURE, nothing fabricated.
A full SOP for building a tool-calling AI agent workflow inside the n8n canvas: one-command Docker self-host deployment, AI Agent node four-piece anatomy (Language Model, Memory, Tools, System Prompt), step-by-step build (pick trigger, configure node, add tools, output, test and publish), five pitfalls (amnesia from missing Memory, hardcoded API keys, over-engineering, context drift, data format mismatch) plus 5 FAQ. Node parameters per n8n official docs; gives config logic, no fabricated full JSON.
A full SOP for taking Ollama local LLMs from "can run" to "usable": why quantization (q4_K_M/q8_0) decides model intelligence, why KV Cache makes you slower the longer you chat, the hardware passing line, picking models by task, Open-WebUI deployment + Ollama API automation (with Python code + JSON mode), 6 pitfalls + FAQ. Speed figures are community reference magnitudes, not hands-on.
Receiving two to three hundred resumes per JD is slow and error-prone by hand. This n8n workflow funnels resume screening into one pipe: Webhook/email intake -> extract text -> LLM scores skill match/experience/stability/risk against the JD and outputs a tier plus reason -> IF checks whether it clears the bar -> Switch routes to interview queue/talent pool/rejection email -> write to Feishu Bitable or ATS -> notify recruiter. The Prompt explicitly forbids using gender/age/marital status/race/household registration as factors; resume handling must comply with PIPL/GDPR; AI scoring is assistive only and the final call belongs to a human.
An n8n workflow for email marketing automation: scheduled pull of subscribers -> segment by tag/behavior -> LLM generates A/B personalized emails (subject + body) per segment -> SMTP send -> Webhook tracks opens/clicks -> write back metrics -> IF open rate below threshold with sufficient sample -> trigger LLM copy regeneration. Includes compliance notes (subscribed/authorized only, one-click unsubscribe, CAN-SPAM/GDPR/E-Commerce Law, no sensitive-attribute inference). Includes a .json template download.
A three-level user research & interview prompt pack: beginner semi-structured interview outline (anti-leading-questions), intermediate single-transcript structured insights (quote-tagged, no fabrication), expert N-transcript cross-sample synthesis into personas + JTBD + opportunity map (affinity mapping, frequency counts, conflict flagging). 4 general constraints (anti-fabrication/de-identification/human review) and 5 pitfalls.
A three-level code testing prompt pack: beginner generates unit tests per function (signature and boundary list first, N cases each for normal/boundary/error, pytest/jest parameterized), intermediate parameterized batch cases with a test plan (coverage gaps, minimal mocking), expert integration/E2E strategy and test refactor (test pyramid, contract tests, redundancy pruning). Includes a 5-minute cheatsheet. Anti-fabrication constraints embedded (no invented APIs, no fabricated coverage numbers). Differentiated from the general coding and review/debug packs--this one handles writing tests only.
A six-step SOP for AI automated test generation: when to use + baseline (coverage/defect escape) -> unit tests from code -> regression tests from issues -> batch boundary/parameterized cases -> test maintenance (stale assertions/redundancy) -> human gate + CI. Each step ships copyable pytest and jest code, plus 5 pitfalls (loose assertions/interdependencies/fake coverage/mock drift/not testing the real path) and 5 FAQ. Iron rule: AI only drafts, human review + CI green before merge.
A complete SOP for production AI Agent observability and online evaluation in six steps: defining the baseline (latency/token cost/tool success rate/error rate), distributed tracing (Langfuse @observe decorator + Phoenix register + OpenTelemetry spans), token and cost tracking, tool-call artifact logging, online evaluation (LLM-as-judge automated scoring + user thumbs feedback via create_score), and alerting with iteration (threshold rules + dashboards). Each step includes copyable Python code, 6 pitfall entries, and 5 FAQ items. Open-source tools first; LangSmith mentioned as a SaaS option.