{"id":83655,"plugin_id":"plugin_asdk_app_6ad963ac7c8c8191ad0f23cd3beb563f","kind":"skill","collection_source":"plugin_package","comparison_source":null,"observed_at":"2026-11-06T00:15:55.097Z","digest":"b3f1e411b3150be267820be7965e7f22e6e84b9178c16acb193066a8bed990d7","against":null,"payload":{"description":"Start or resume Fievel onboarding and choose a beginner, existing-guidelines, or AI-experience workflow.","included_files":[],"name":"getting-started","skill_md_contents":"---\nname: getting-started\ndescription: Start or resume Fievel onboarding and choose a beginner, existing-guidelines, or AI-experience workflow.\n---\n\n# Getting Started\n\n## Using this skill from the Fievel plugin\n\nThis plugin carries Fievel's workflows and Python tools. The user's project is\nseparate from the installed plugin. Find the plugin root two directories above\nthis SKILL.md; its `runtime/` directory is the bundled resource root, called R below.\nResolve R from this skill's actual installed path, never a development checkout.\n\n- Prefer Fievel's MCP tools when they are available (names start with `fievel_`).\n  Local tools such as `fievel_init`, `fievel_validate`, `fievel_check` and\n  `fievel_view` work only inside the user's workspace and never overwrite files;\n  call `fievel_setup_status` first. Remote tools such as `fievel_validate_package`\n  and `fievel_check_responses` receive package content, store nothing, and return\n  results to save as new files; tell the user that content is sent to the Fievel\n  server for processing. Without tools, use the CLI as described below.\n- Read `R/AGENTS.md` for Fievel's conversation and workflow rules. References in\n  this skill and in AGENTS.md to AGENTS.md, docs/, knowledge/, templates/, examples/,\n  schemas/, scripts/ and .agents/skills/ are relative to R, not the user's working directory.\n- Keep the current working directory in the user's chosen workspace. Run the CLI\n  by its absolute path: `python \"R/scripts/fievel.py\" <command> <project> ...`.\n  Replace R with the resolved path. Resolve project and output paths in the user's\n  workspace. Run one simple command per step: the Python interpreter\n  with the CLI path and its arguments, without chained commands, shell variables\n  or PowerShell scripts. Read and list files with the host's file tools. Simple\n  commands keep the user's permission prompts clear and predictable. Never create\n  projects, install dependencies, or edit inside R or the plugin cache. Never copy Fievel AGENTS.md over a user's repository instructions.\n- Check Python 3.10+ and `python -c \"import jsonschema\"`. If missing, explain the\n  prerequisite. When setup is authorized, create a new `.fievel-venv` in the user's\n  workspace and install `R/requirements.txt` with its Python. Reuse a working\n  environment; do not overwrite an existing environment. Use that interpreter for\n  every CLI command. Do not install globally or claim the plugin bundles Python.\n- Follow existing workspace instructions. Explain that project files remain local,\n  but material read into the conversation is handled by the user's coding agent.\n- The repository-maintenance and adapter-sync instructions in R/AGENTS.md are for\n  Fievel development, not for modifying the installed plugin during a user session.\n- Other Fievel skills in this plugin: getting-started, content-system-builder,\n  source-discovery, ai-behavior-designer, evaluation-designer and content-audit.\n  Switch to the one that fits when the user's request changes.\n\nSave projects under the user's chosen folder (default `my-content-system/` in\ntheir workspace). The user approves content; validation does not. Do not run\nmodel evaluations unless the user authorized them.\n\n## Workflow\n\nRead AGENTS.md and docs/WORKFLOW.md. If a project exists, read its progress.json\nand validate it before asking for information again. Briefly summarize the saved\ndecisions, open questions, and next stage. Do not re-onboard an experienced user.\n\nFor a new user, introduce Fievel as an AI content systems architect, then offer:\n“I’m new to this”, “I have existing content guidelines”, or “I’m building an AI\nexperience”. Ask which path fits. Explain that a content system connects product\ncontext, rules, sources, behavior, checks, and ownership. Offer the Wayfarer example\nwithout requiring company data. Ask one useful question at a time and say why.\n\nUse knowledge/INDEX.md to load one relevant local explanation; fundamental concepts\nand the fictional exercise should not depend on web requests. If the user asks how\nthe parts connect, read knowledge/content-design/system-relationships.md. If they\nask how another AI uses the files, read knowledge/ai-fundamentals/instructions-and-skills.md.\nAfter the first useful saved artifact, offer a local overview using\n`python scripts/fievel.py view <folder> --out <new.html>`. It works with partial\ndrafts, displays diagnostics, and does not edit files. It is a snapshot: regenerate\nafter edits using a new filename. Do not describe it as live sync or automatic\nconsumer integration. Do not load execution reports unless the user selects one.\n\nCreate a NEW folder with `python scripts/fievel.py init my-content-system` (add\n`--example` only if the user chooses fictional practice). Record path and explanation\npreference in progress.json. For existing guidelines ask for their approved local\nlocation; discovery does not authorize ingestion or external access. Experienced\nusers can skip lessons and supply a product brief directly.\n\nOutput: project folder and saved next_action, decisions, open_questions. Check:\nthe user understands what will be created; no private sources copied without scope.\nRecovery: if the destination exists, resume it or choose a new name; never replace it.\nExample: “I’m new” -> offer Wayfarer -> stage 1, one product task.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}