# Spec-Discovery Brief — m2-market (M2 Marketplace)

## Mission
Produce implementation-ready SPECs for the M2 Marketplace: operators package repeatable
outcomes as installable **Solutions**, sell them for **M2 credits**; a **Solution Scout**
proposes packages in-session at the moment of need; Hermes composes build-vs-install
proposals. "The unit is not software. The unit is completed work."

READ FIRST (in order):
1. /home/m2/m2-market/CONCEPT.md          — the unified concept v0.2 (your requirements source)
2. /home/m2/m2-market/context/SYSTEM-MAP.md — verified map of m2-gpt, agent.memory.system, m2o
3. /home/m2/m2o/.planning/federated-learning/PLAN.md — the fedlearn MVP rails you build upon

## Locked decisions (operator-accepted; do NOT relitigate — design WITH them)
1. **Ledger:** standalone `m2-ledger` service, BUT architected to evolve into a crypto-capable
   ledger managed under extended **m2-gpt billing** (tenant keys/metering federate; design the
   migration path now, ship internal credits first).
2. **Pricing v1:** fixed price per install AND job-based pricing where applicable — sometimes
   another m2o operator takes a JOB to deliver a solution (service-style engagement settling
   through the same ledger).
3. **Storefront:** MVP = **cargstore Electron revival** (github.com/machine-machine/cargstore)
   that connects to Coolify and other parts using credentials from **m2-gpt** (gateway-issued
   identity/keys). Web variant later from cargstore's web/ seed.
4. **Scout:** EXPLORE the solution space — standalone supervised watcher vs Hermes plugin vs
   herdr plugin. Compare honestly, recommend one, spec it.
5. **Seed Solutions:** mm-pdf (branded PDF generator), agent-scaffold (workspace generator),
   competitor-scan (research report).
6. **Economy defaults:** platform cut 10%, starter grant 100 credits.

## Hard constraints (inherited — every spec must respect)
- Tenant isolation: client data (sdjs=GST, nasr, parlobyg, peter) never crosses into shared
  catalog; commercialization of tenant-derived work is owner-initiated + doubly-scrubbed.
- No secrets in repos/images; keys injected at runtime (M2_GPT_API_KEY pattern).
- Mixed fleet images: runtime-sync (fedlearn m2-core-sync) is the universal install path.
- Host fragility: canary-first, idempotent, reversible; new services deploy as Coolify apps
  on the `coolify` network. Local registry route is broken (302) — no registry push/pull.
- Harness-agnostic where cheap: Hermes is primary, openclaw stays supported; anything speaking
  OpenAI-wire through m2-gpt should work.
- Forgejo (git.machinemachine.ai, org m2) = system of record; memory-api = semantic index.

## Your output — a SPEC, not a plan
Write to /tmp/mktspec/specs/<YOUR_ID>.md. Structure:
1. **Scope & non-goals** (MVP vs later)
2. **User stories + acceptance criteria** (Given/When/Then where useful)
3. **Interfaces & data contracts** — concrete: schemas (JSON), API endpoints (method+path+
   payload), CLI verbs, file paths, DB tables, events. This is the heart.
4. **Integration contract** with the other subsystems (name exact touchpoints)
5. **Options compared** where your lane has a genuine fork; recommend one
6. **Risks/edge cases** (tenant, secrets, fragility, abuse)
7. **[NEEDS CLARIFICATION]** markers for anything genuinely undecidable (max 3)
Inspect the LIVE system (docker, curl, repos in /home/m2/) — ground every contract in reality.
Read-only on all existing repos; your ONLY write is your spec file.
Last terminal line: SPEC_DONE_<YOUR_ID>