skills/feature-architect/SKILL.md
FE feature analysis from raw idea (or YouTrack ticket) through to a split-aware developer briefs. Produces context-map.md, requirements.md, task-brief-{side}.md. Generic — project knowledge is auto-discovered.
npx skillsauth add olamedia/analytics-skills feature-architectInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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CRITICAL: MUST NOT advance to the next step without explicit user permission. Complete the current step, present the result, and wait. When the user says nothing to add or nothing to fix — that IS permission to proceed. Do not ask again.
CRITICAL: During Step 1 (collecting intent), MUST NOT read any codebase files, scan directories, check project docs, or run research. The ONLY action allowed is writing the Intent block into requirements.md from the user's words. Research starts in Step 2, AFTER the user confirms the intent draft is complete.
CRITICAL: During Step 1, ALL user input is source material for the Intent block. References to folders, files, prior artifacts, context hints — everything goes into Intent. Record it, update requirements.md, and ask if there is more. Do not act on references until research starts.
CRITICAL: Each step has its own scope. Do NOT mix. Intent collects words. Research collects facts. Analytics loop resolves questions. Brief documents decisions. No analysis in intent, no design in research, no research in brief.
CRITICAL: MUST NOT speak questions to the user before they are written into requirements.md. Write first, present after. No exceptions.
CRITICAL: Any change mid-pipeline MUST propagate upstream-first. Go back to requirements.md, update intent/stories, then propagate to context-map.md. Never patch a downstream artifact without updating upstream. Describe impact, wait for agreement, apply in one batch.
Turn a raw idea (or YouTrack ticket, or testing report) into analysis artifacts: context-map.md, requirements.md, task-brief-{side}.md. The skill stops at the briefs — design, tasks, and implementation are downstream.
Detail sits in references/ (research.md, scan.md, splitting-checklist.md, brief-format.md, formats.md, issues-as-intent.md).
Apply human-techtalk subskill to all prose output.
Skip when: obvious single-file fix, only one step applies, or upstream artifacts already exist and you resume from the right step.
docs/plans/feature-name/) and create itAn artifact folder containing:
workspace.md — repo index with paths to structure files (or scan notes)context-map.md — living document, updated throughoutrequirements.md — intent, goal, stories, questions logtask-brief-{side}.md — one complete task brief per split part (e.g. task-brief-fe.md, task-brief-be.md; single task-brief.md for frontend-only)Step 1: Collect Intent → Step 2: Initial Research → Step 3: Analytics Loop → Step 4: Brief
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(return if gap found)
All research and open questions MUST be settled before the brief. If a gap is found during Step 4, return to Step 3 to resolve it, then resume.
No codebase reading. No research. Only write requirements.md.
get_issue) if available. If MCP is not available, report it missing — user decides next step (paste text manually, provide URL, or skip).issues-as-intent.md — classify rows, deduplicate, separate blockers from product goals.Gate: confirm, then proceed to research. Use structured questions.
Mandatory first pass. Follow research.md (a standalone skill/reference) completely. Produces workspace.md and context-map.md. Later steps may invoke research.md again to fill gaps.
workspace.md: for each repo in the workspace, check for docs/TechStack.md, docs/ProjectStructure.md, docs/CodeStyle.md. Record paths found.scan.md), mark entry as scanned.workspace.md.workspace.md.research.md).context-map.md.Gate: confirm, then proceed to analytics loop. Use structured questions.
Starts with stories + goal. Loops through questions until all are resolved. Follow formats.md for artifact structure.
[to-ask] questions into requirements.md before speaking them. When proposing answer choices, use structured questions.context-map.md.[resolved] with rationale.[not-planned] with reason, deferred items [deferred] with reason.splitting-checklist.md to identify split points (FE/BE/migration/cross-service).[to-ask] remain.requirements.md.Gate: confirm, then proceed to brief. Use structured questions.
Produces one task-brief-{side}.md per split part. Follow brief-format.md completely.
task-brief.md, skip split overhead.task-brief-{side}.md files.Gate: confirm via structured questions. List artifact folder contents. Skill is done — downstream flows take over.
Across all steps:
When presenting choices to the user, use the IDE's structured question tool (AskQuestion in Cursor, or equivalent in other IDEs) if available. Applies to:
Always include an open-ended escape option. Multi-select when the question genuinely allows it. Fall back to conversational text only if no structured question tool is available in the IDE.
Refuse to: produce brief without written requirements, skip gates, accept "I know the repo" without an updated map, skip the first research pass, emit brief with unresolved questions.
context-map.mdworkspace.md has an entry per repo in scopecontext-map.md matches research findings, append-onlyrequirements.md holds intent in user's words, stories, and full questions logtask-brief-{side}.md files exist per split part, no uncertain language, no placeholderstesting
Rebase current feature branch onto master (or configured base) with automated conflict resolution. Handles pre-checks, conflict classification, and post-rebase verification. Use when the user asks to rebase, update a branch, sync with master, or resolve rebase conflicts.
testing
Concise technical communication that stays readable and honest. Cuts fluff about fifty to seventy percent while keeping natural flow, uncertainty markers, and human tone. Levels lite (default), mid, tight. Short SKILL body; rules below are complete.
documentation
Strip LLM tells from text so it reads human. Triggers: humanize, sounds like AI, too robotic, natural rewrite, AI slop, or obvious LLM patterns. Reference: https://en.wikipedia.org/wiki/WP:Signs_of_AI_writing
development
Guide frontend development toward websites that feel premium and trustworthy, grounded in cognitive science research (processing fluency, prototypicality). Two modes: active guidance during build, and audit (problems + suggestions). Use when building UI, reviewing frontend code, or when the user mentions premium, trust, design quality, audit, review, or frontend polish.