m2-market/README.md

m2-market

Working title for the M2 Marketplace — the platform for packaged work. Operators package repeatable outcomes as installable Solutions, sell them to other operators for M2 credits; a Solution Scout proposes the right package in-session at the moment of need; Hermes composes build-vs-install proposals with cost evidence. "The unit is not software. The unit is completed work."

Status: spec discovery (kickstarted 2026-07-01)

Doc	Purpose
CONCEPT.md	Unified concept paper v0.2 (pitch v0.1 + fedlearn rails + cargstore + Scout)
context/SYSTEM-MAP.md	How m2-gpt, agent.memory.system, m2o/Coolify, Hermes/OpenClaw actually connect (verified)
specs/	Spec-kit style discovery output lands here (spec → clarify → plan → tasks)

The one-paragraph architecture

Marketplace = protocol + registry, not one app. Forgejo (git.machinemachine.ai) is the system of record (listings = releases, review = PRs, veto = labels); memory-api indexes it semantically (market:catalog partition, same BGE-M3 hybrid search as agent memory); cargstore (revived → M2 Store) renders it in-desktop; Hermes speaks it in-session (backed by m2-gpt, whose tenant keys/budgets/metering seed the m2-ledger credit economy); the fedlearn sync path installs it (signed, idempotent, tenant-aware). The only genuinely new services: m2-ledger (append-only internal credits) and the Solution Scout (per-desktop watcher → semantic match → toast + deep link → few-clicks deploy).

Dependencies

• fedlearn MVP (in execution): schemas, m2/m2-core, memory-api auth, curator+veto, m2-core-sync. The marketplace is its commercial tier — same rails, plus price/seller/license.
• Existing: m2-gpt (tenancy+metering) · agent.memory.system (catalog+evidence) · m2o fleet (surface) · cargstore (storefront) · Forgejo (registry) · Coolify (deploys).

Open forks (need operator decision — CONCEPT.md §14)

1. Ledger substrate: standalone m2-ledger , could become a crypto ledger that is managed in extended m2-gpt billing
2. Pricing v1: fixed per install (rec.) vs metered vs both (when applicable, sometimes othe m2o take a job for a solution)
3. Storefront: cargstore Electron revival (rec.) vs web-first vs both MVP electron app that connects to coolify or other parts with credentials from m2-gpt
4. Scout host: standalone supervised watcher (rec.) vs Hermes plugin vs herdr plugin (explore solution)
5. Seed Solutions: mm-pdf · agent-scaffold · competitor-scan (proposed)
6. Platform cut % + starter grant (defaults 10% / 100 cr)

Next steps

1. Resolve the 6 forks (or accept recommendations).
2. Run spec discovery per subsystem → specs/ (spec-kit or GSD; herdr herd like the fedlearn discovery: one angle-specialist per subsystem — ledger, catalog, store UI, Scout, packaging).
3. Wait for fedlearn MVP convergence (schemas + sync path are hard inputs to solution.schema).