suggest

name	suggest
description	Use when the user wants feature or improvement ideas for a corgi workspace — "suggest features", "what should we build next", "ideas to improve X", "how do we make this faster/safer/cheaper", "what's missing", "any new business cases". Scans the stack + its business domain + existing features and proposes RANKED, evidence-backed suggestions across two lenses — product/business (new features, new business cases, UX) and engineering (performance, reliability, security, cost, tech-debt, even a language rewrite when the ROI is high) — each tied to a measurable outcome. Specs the chosen one and offers to create a tracker story (asks where). NOT for implementing it (use the stories skill) or authoring corgi-compose.yml (use the corgi skill).
metadata	{"author":"Andriiklymiuk"}

Corgi suggest

Propose real, measurable improvements for a corgi workspace — the way a product manager and a senior engineer would after actually reading the code and the business. Two lenses, every idea grounded in evidence and tied to an outcome, then spec the chosen one and offer a story (ask where). Suggests + specs; does not implement (that's stories).

Guardrails (non-negotiable)

• Evidence or it doesn't ship. Every suggestion cites a real signal — a file:line, a dependency, a metric, a concrete domain gap. No generic "add dark mode / add AI / add notifications" without a reason rooted in this stack. Slop is the failure mode; kill it.
• Measurable outcome required. Name the metric, a rough magnitude, how you'd measure it (p95 latency, signup completion, error rate, bundle size, infra $/mo, test coverage, MTTR). Can't tie it to an outcome → drop it.
• Suggest, don't build. Output = ranked shortlist → one spec → optional story handoff. No code changes here.
• A rewrite is a claim, not a vibe. "Rewrite service X to Go/Rust" only with a feasibility + ROI case: a hot path, a large measured/estimated gain, a migration path. Never for novelty.
• Metrics/analytics on demand only. Any perf/cost claim needing runtime data → reuse the debug skill's provider detection; ask before querying, scoped, read-only.
• Honest effort + ranking. Rank by impact/effort; don't bury a big lift as a "quick win".
• No AI attribution. Everything posted or committed — ticket comments, PR/MR bodies, commit messages, store listings/notes — reads as if a human wrote it. Never add "generated by AI / Claude / corgi", a co-authored-by or AI trailer, a bot signature, or a robot-emoji badge to any outward text.

Phase 0 — Map the stack + the business

cwd must hold corgi-compose.yml (else tell the user to open the workspace folder). Read once:

• Stack — services/db_services, depends_on, ports, each service's language/runtime (from package.json/go.mod/Cargo.toml/pyproject/ Gemfile…), manualRun (reference-only). Schema: ../corgi/references/yml-schema.md.
• Business + existing features — workspace README + per-service READMEs: what the product does, who the users are, the business model, what already exists. Product suggestions anchor to this domain — don't invent a generic SaaS.
• Workspace memory — if .corgi/memory/ exists, run corgi memory list --json (or read index.md) and open the matching facts (see the memory skill). Don't propose what a decision rejected; ground a product idea in a domain fact; cite a past incident as evidence. Absent → skip silently.

Phase 1 — Gather evidence (investigate once, parallel lenses)

Dispatch one Explore/Task per lens (not per idea; scan each area once, orchestrator holds the evidence ledger). Each returns cited signals, not opinions:

• Product / business — table-stakes features missing for this domain; activation/retention/revenue levers; UX friction; per-service capability gaps vs domain norms. Cite the README/route/screen.
• Performance — hot/heavy paths, N+1, missing indexes/caching, big bundles, sync-where-async, chatty cross-service calls. Cite file:line. Need runtime numbers → debug provider (on demand).
• Reliability / safety / security — missing healthchecks, tests, structured logging/observability, error handling, retries/timeouts; authz gaps, secret handling, vulnerable/deprecated deps, data-loss risks. Cite the gap.
• Cost / DX / tech-debt — outdated deps, oversized files, duplication, slow CI, a hot service on a runtime costing latency/$$ (rewrite candidate). Cite it.

Phase 2 — Turn each signal into a REAL suggestion

One card per candidate; any hand-wavy field → cut the card:

• Title + lens (product | eng).
• Evidence — the signal (file:line / dep / metric / domain gap).
• Change — concrete, which service(s).
• Measurable outcome — metric + rough magnitude + how measured. e.g. "p95 of /search 1200ms → <400ms (add the missing index + a 60s cache)", "signup completion +X% (remove the dead address step)", "JS bundle 2.1MB → <900KB", "MTTR ↓ via structured logs + one dashboard", "infra $/mo ↓ moving the cron worker off the always-on dyno".
• Effort — S/M/L + the main risk.
• Why now — the business/engineering reason it matters for this stack.

Phase 3 — Rank + present the shortlist

Adversarial pass on every card: "real, measurable improvement, or generic slop?" Drop the slop. Rank by impact/effort. Present a tight shortlist (~5–8), mixing product + engineering, a rewrite candidate only if it earns its place:

[eng] Add composite index on orders(creator_id, created_at)
   evidence: api/queries/orders.rb:88 — full scan; logs show /orders p95 ~1.2s
   outcome:  /orders p95 1.2s → <300ms; ~0 risk; effort S

[product] Email-digest of weekly activity
   evidence: notification-service exists but only sends transactional; README
             says retention is the Q3 goal
   outcome:  W2 retention lever — measure via cohort open→return; effort M

Offer the user to pick one (or a few).

Phase 4 — Spec the chosen suggestion

Write docs/suggestions/<slug>.md — reuse the stories spec shape so it drops straight into implementation: problem + evidence (file:line), change grouped by service, the measurable outcome and how to verify it, effort/risk, rollout. Multi-service → add ## Contract + producer→consumer order.

Phase 5 — Offer a story (ask where)

Ask: "Create a story for this?" If yes, ask where:

• Tracker — detect Linear vs Jira from the workspace (tracker URLs in README/compose, git remote); confirm. Namespace: Linear → mcp__linear-server__*, Jira → mcp__atlassian__*.
• Project / service — which tracker project, which service(s) the work lands in (paths from corgi-compose.yml).

Preflight: confirm the matching tracker MCP is actually connected before creating anything. Missing → don't silently create in the wrong tracker: keep docs/suggestions/<slug>.md as the deliverable + a paste-ready issue body + the tracker's new-issue URL, and stop.

Connected → create the issue from the spec (Linear mcp__linear-server__save_issue with title+team and no id / Jira mcp__atlassian__createJiraIssue) at the chosen location, link the spec, report the key/link. Post one terse comment, not several — don't spam the ticket; update an existing comment in place rather than adding new ones. Then offer "Implement it now?" → hand the approved spec and the created issue key to the stories skill (it branches per service off that key — don't let it re-create the issue). Declined → leave the spec; done.

Scenarios & scaling

• Big workspace → lens agents per service-cluster in parallel; orchestrator merges + dedups the ledger before carding.
• Perf/cost claims needing real numbers → debug provider, on demand, ask first; else mark the magnitude an estimate and say so.
• One spec → one story at a time; batch more later via stories.

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Source: Andriiklymiuk/corgi — distributed by TomeVault.