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M2 Marketplace — schemas, m2-ledger, m2-market CLI, catalog indexer (SDD factory loop)
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m2 (AI Agent) e8ee3920de factory-loop: onboard spec-kit, ratify constitution v1.0.0, draft 001-market-first-wedge spec 
- spec-kit initialized (claude integration; /speckit.* skills in .claude/skills/)
- constitution: 7 principles from CONCEPT.md v0.2 + inherited fleet constraints
- spec 001-market-first-wedge: 5 prioritized stories, FR-001..015, SC-001..007
- operator steer from README §open-forks folded into assumptions (ledger standalone
  now / m2-gpt-billing crypto later, fixed pricing + job headroom, Electron MVP
  store, Scout host = explore spike)
- .m2herd/ context fabric initialized (gitignored)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-02 02:03:14 +02:00
.claude/skills factory-loop: onboard spec-kit, ratify constitution v1.0.0, draft 001-market-first-wedge spec 2026-07-02 02:03:14 +02:00
.specify factory-loop: onboard spec-kit, ratify constitution v1.0.0, draft 001-market-first-wedge spec 2026-07-02 02:03:14 +02:00
context kickstart: m2-market spec discovery — concept v0.2 + verified system map 2026-07-02 01:03:39 +02:00
specs/001-market-first-wedge factory-loop: onboard spec-kit, ratify constitution v1.0.0, draft 001-market-first-wedge spec 2026-07-02 02:03:14 +02:00
.gitignore factory-loop: onboard spec-kit, ratify constitution v1.0.0, draft 001-market-first-wedge spec 2026-07-02 02:03:14 +02:00
CONCEPT.md kickstart: m2-market spec discovery — concept v0.2 + verified system map 2026-07-02 01:03:39 +02:00
README.md kickstart: m2-market spec discovery — concept v0.2 + verified system map 2026-07-02 01:03:39 +02:00

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 (rec.) vs extend m2-gpt billing vs Forgejo-as-ledger
  2. Pricing v1: fixed per install (rec.) vs metered vs both
  3. Storefront: cargstore Electron revival (rec.) vs web-first vs both
  4. Scout host: standalone supervised watcher (rec.) vs Hermes plugin vs herdr plugin
  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).