Machine.Machine
model = "provider/model-id" and dispatches:
mm-model — forge --model anthropic/... or openrouter/... or ollama/llama3.3:70b or custom/http://dgx1:8080/v1. One interface, any model. On DGX: FORGE_MODEL=custom/http://localhost:8080/v1 → 70B local, zero cloud spend.mm-intent — classify → Redis bus → inject into system promptmm-dark-factory — Markdown workflows → typed execution plans + gap detectormm-fleet — heartbeat, escalations, alive agent registryforge binary — REPL + one-shot + factory run + fleet ops + doctormm-kairos — KAIROS dreaming daemon, fully per the PRD Peter wrote. 4-phase cycle, in-process scheduler (no cron), hard safety constraints, morning brief to Telegram. Time gate 24h, session gate 5 sessions.docs/MM_FORGE_PRODUCTION_SPEC.md (covers hardware, crate roadmap, DGX deployment model, LLM stack, production checklist, open questions)docs/PRD-kairos.htmlmachine.machine/mm-forge branch mm-mainmm-memory (Qdrant + BGE-M3 + consolidation hook into KAIROS) and mm-identity (AIEOS profiles). Then Phase 3: mm-tools (Planka/Coolify/Forgejo/Telegram as native Rust tools) and the hook pipeline.cargo build --release --target aarch64-unknown-linux-gnu, copy the binary, set FORGE_MODEL=custom/http://dgx1:8080/v1. Every agent on the fleet gets it.
mm-kairos and compiles clean. Let me fetch §19 ULTRAPLAN to understand it before merging with dark factory.
/plan and /ultraplan into the forge REPL and update the spec:
mm-kairos compiles, 4-phase cycle, in-process daemon (no cron), Telegram brief, session gate + time gate. Per Peter's PRD exactly.Plan output IS a WorkflowSpec.Vanilla ULTRAPLAN gives you text that disappears. Ours gives you an executable Markdown workflow that gets parsed, archived to
.forge/plans/, optionally run immediately by the Dark Factory executor, and auto-creates a Planka card. Approval via Telegram inline buttons, not a browser tab. Operator feedback from rejection gets injected into the next planning iteration (max 5 rounds).forge> /plan "migrate GMI agents to DGX Spark cluster"
⚡ ULTRAPLAN — goal: migrate GMI agents to DGX Spark cluster
# Workflow: Migrate GMI Agents to DGX Spark
Description: Move all GMI fleet agents from Coolify to DGX Spark nodes
Steps: 1.health_check (bash), 2.build_arm64 (bash), 3.copy_binary (bash),
4.update_fleet_env (model), 5.health_verify (bash), 6.notify (model)
Approve? [y/N/feedback]: y
✅ Plan approved — archived to .forge/plans/2026-04-01-migrate-gmi-agents.md
Execute now? [y/N]:
TelegramApprover to actual inline buttons. That's the mm-tools crate (Phase 3 on the roadmap) — right now it falls back to terminal readline in dev.
grafana-observability (Port 3000) vllm-coder-32b (Server A, mapping --gpus '"device=0,1"', Port 8000) vllm-planner-7b (Server B, --gpus '"device=0"', Port 8001) tei-embedding-rerank (Server B, --gpus '"device=1"', Port 8002) machine.machine cognitive architecture. This integrates your current 4×3090 hardware and the upcoming 8× DGX Spark cluster into a unified, agentic operating system where routing, memory, and reasoning each have dedicated, right‑sized hardware.[1][2] litellm-gateway (Port 4000) qdrant-vector-db (Port 6333) agent-orchestrator (Your custom Python/Go app)
outputs/mm-cognitive-architecture.pdf
outputs/mm-cognitive-architecture.pdf
What if your AI is brilliant online—but completely lost on your own desktop? Even top-tier AI models fail to navigate the messy reality of personal computing. HippoCamp, a new benchmark, reveals the gap: these agents score just 48.3% on user profiling across 42.4GB of real-world data—logs, PDFs, im...Blog URL:
results/hippo-camp-benchmark-agents_linkedin.txt for manual publishing.
forge prompt "What is 42*37?" → 1554 ✅forge prompt "Name 3 Rust async runtimes" → tokio / async-std / smol ✅git clone http://git.machinemachine.ai/machine.machine/mm-forge.git
cd mm-forge && git checkout mm-main
cd mm && cargo build --release -p forge
# Any model via --model flag:
OPENROUTER_API_KEY=sk-or-... \
./target/release/forge --model openrouter/qwen/qwen3.6-plus-preview:free \
prompt "your question here"
# Or with Anthropic:
ANTHROPIC_API_KEY=sk-ant-... \
./target/release/forge --model sonnet \
prompt "your question here"
# Or local Ollama:
./target/release/forge --model ollama/llama3.3:70b prompt "hello"
forge prompt "...") — any modelforge intent "...")forge factory run workflow.md)forge fleet)forge with no args) — includes /plan <goal> for ULTRAPLANdocs/mm-intent-layer.md after the Peter conversation. That founding doc is worth revisiting as a starting point.docker pull ghcr.io/machine-machine/mm-forge:latest
docker run --rm \
-e OPENROUTER_API_KEY=sk-or-... \
ghcr.io/machine-machine/mm-forge:latest \
prompt "What is 42*37?"
ghcr.io/machine-machine/mm-forge:latestOPENROUTER_API_KEY (or ANTHROPIC_API_KEY)SERVICE_URL_REGISTRY_5000 has https://... with port 5000. Docker registry needs https:// on the domain (no port suffix). Let me fix the FQDN:
docs/THE_SURFACE_SPEC.md — 18KB founding spec.surface.machinemachine.ai? Something else?apt-get update first, and use Docker buildx's native multi-arch instead of cross-compilation:
room.machinemachine.airoom.machinemachine.ai on Coolify once it builds clean. Should have something for you to click in ~15 minutes.
cp target/release/forge fails — the cd mm puts us in the wrong relative path. Quick fix:
Cargo.toml workspace might be putting it in target/ instead of mm/target/. Let me check the workspace structure:
Cargo.toml — so cargo build -p forge from the root puts the binary in ./target/release/forge, not mm/target/. Fix the workflow:
http:// in Coolify, Traefik handles the SSL. No ports needed.registry.machinemachine.ai as the image source, the FQDN should be http://registry.machinemachine.ai in Coolify, full stop.MEMORY.md. I just ignored it — won't happen again. The Coolify rule is: http://, no ports, always. Traefik does the rest.
AVmJHBhE3JfVD8tFexited:unhealthy — might be the initial build still running, or it built and crashed. Let me check Docker directly.
AVmJHBhE3JfVD8tF| Layer | System | What it knows |
| ------------- | ------------------ | -------------------------------------------------------- |
| Tasks | Planka | What needs doing, who's assigned, blocked/done |
| Knowledge | Qdrant (m2-memory) | Everything that was ever discussed, decided, learned |
| Intent | Intent Registry | Typed signals — goals, preferences, decisions per person |
| Code | Forgejo repos | What was actually built, commit history, PRs |
| Relationships | Memgraph | How entities connect — people↔projects↔decisions↔agents |
surface-api.machinemachine.ai)/missions, /fleet, /attention, /chat, /graphgit.machinemachine.ai/machine.machine/mm-forge (branch: mm-main)ghcr.io/machine-machine/mm-forge:latest ✅ live| Crate | Status | Description |
| --------------- | ---------- | --------------------------------------------------------------------------------------------- |
| forge | ✅ Working | CLI binary — prompt, factory run/list, fleet status, intent, models, doctor |
| mm-model | ✅ Working | Model router — Anthropic, OpenRouter, Ollama, any provider via env |
| mm-intent | ✅ Working | Intent classifier — bus, envelope, injector, classifier (ROOT goal detection before planning) |
| mm-dark-factory | ✅ Working | Markdown workflow compiler + executor + ULTRAPLAN planner |
| mm-fleet | 🔧 Stubbed | Fleet status — structure exists, needs Coolify/Docker socket wiring |
| mm-memory | 🔧 Stubbed | Qdrant memory — wired in spec, not yet connected |
| mm-identity | 🔧 Stubbed | Agent identity + AIEOS profiles — structure only |
| mm-kairos | ✅ Written | Nightly dreaming daemon, morning brief, memory consolidation — logic complete, not deployed |
| mm-tools | 🔧 Stubbed | Tool execution layer (bash, read, write, grep) — structural |
forge prompt "..." — one-shot to any modelforge intent "..." — classifies to root goalforge factory run workflow.md — executes Markdown workflow specsforge factory list — lists available workflowsforge doctor — checks env/keys healthWorkflowSpec, archives to .forge/plans/TODO Phase 3 in planner.rs)Image: ghcr.io/machine-machine/mm-forge:latest
Env: OPENROUTER_API_KEY=sk-or-...
CMD: prompt "What can you do?"
reqwest already in workspace deps. Now I'll wire Phase 3: Telegram inline approval buttons + Planka card creation:
planner.rs — TelegramApproval::approve() now sends a real Telegram message with [✅ Approve] [❌ Reject] [✏️ Revise] inline buttons, then long-polls /getUpdates until a response arrives (30min timeout)planka.rs (new) — PlankaClient that creates a Planka card post-approval. Optional — skips silently if PLANKA_TOKEN not setPlanResult — gets a planka_card_url: Option<String> field📋 Planka card: {url} after a plan is approved and createdFORGE_TELEGRAM_BOT_TOKEN=... # bot token for approval messages
FORGE_TELEGRAM_CHAT_ID=... # your chat ID (437589940)
PLANKA_URL=http://planka.machinemachine.ai
PLANKA_TOKEN=... # your Planka JWT
PLANKA_LIST_ID=... # which list to create cards in
SKILL.md → install). Hermes does it autonomously. That's the gap worth closing.git.machinemachine.ai/machine.machine/mm-forge (branch: mm-main)ghcr.io/machine-machine/mm-forge:latest ✅ liverusty-claude-cli, a workspace with api, runtime, tools, commands, compat-harness crates. The Python side (src/) appears to be reference/parity tooling.PARITY.md: solid foundation (API, session, tool loop, MCP stdio), but major gaps — no hooks execution, no plugins, no remote transport, no skill registry, thin tool set (MVP only).| Hermes feature | mm-forge gap it addresses | Effort |
| -------------------------- | ---------------------------------------------------------------- | ---------------------------------------------- |
| FTS5 memory | No memory crate exists at all in Rust yet | Low — pure SQL, no deps |
| Skill-from-experience loop | skills in Rust is local-file only, no registry, no auto-creation | Medium |
| Hook pipeline | Hooks parsed but not executed in Rust (rcc/hooks branch exists) | Already in progress — reference Hermes pattern |
| Subagent delegation | rcc/subagent branch exists | Already in progress |
| Cron scheduling | ScheduleCronTool missing in Rust | Low — Hermes does it cleanly |
rusqlite.memory crate to mm-forge (rusqlite + FTS5) — directly inspired by Hermes's schema. This fills the biggest runtime gap for a usable agent.rcc/hooks branch — get hooks executing before adding new tools. Hooks unlock the skill-from-experience pattern downstream.rcc/memory branch has so far?
machine.machine/m2-rust (private) — Rust workspacemachine.machine/mm-kernel (private) — TypeScript kernelmm.sh CLI Wrappermm-license-server (Rust/Axum/SQLite) — the licensing API prototype you mentioned. Using it as reference.mm-crypto (Ed25519 manifest signing) + mm-audit (Merkle hash-chain audit trail) — both with N-API bindings for TypeScriptmm.sh CLIULTRAPLAN flow end-to-end:forge factory plan "your goal" — iterates the plan (up to 5 rounds), classifies root intent[✅ Approve] [❌ Reject] [✏️ Revise]📋 Planka card: {url} in outputFORGE_TELEGRAM_BOT_TOKEN=
FORGE_TELEGRAM_CHAT_ID=437589940
PLANKA_URL=http://planka.machinemachine.ai
PLANKA_TOKEN=
PLANKA_LIST_ID=
PLANKA_TOKEN isn't set it skips silently. Telegram approval is always active when the bot token + chat ID are set.ghcr.io/machine-machine/mm-forge:latest.
GET /health → {"status":"ok"}/fleet, /missions, /missions/{id}, /attention, /chat, /graph/{topic}viewport-fit=cover + safe area insets (notch + home bar handled)touch-action: manipulation, scale feedback on tapAVmJHBhE3JfVD8tF.mm-kairos — we're ahead of most.
mm-kairos — we're ahead of most.
docs/PHASE4_SPEC.md)mm-memory crate — Qdrant pointer index, staleness tracking, verification gate. Facts tagged [VERIFIED] or [UNVERIFIED] before reaching any model callmm-kairos already has the 4-phase cycle — adding contradiction detection + tentative→verified promotionmm-tools/risk.rs — every action tagged LOW/MEDIUM/HIGH, HIGH gates through Telegram approval (reuses Phase 3 buttons)forge daemon command, 15-second blocking budget, daily activity logWhat if the best way to make AI think efficiently isn't to punish waste—but to force it to share space? Forcing LLMs to solve 16 tasks at once slashes token use by 62.6%—without sacrificing accuracy. That's the breakthrough of Batched Contextual Reinforcement (BCR), a new method where models learnBlog URL:
results/batched-reasoning-efficiency_linkedin.txt for manual posting./v1/embeddings endpoint, 1024-dim BGE-M3. Now let's build.
3ba5723)MemoryClient — async Qdrant HTTP + BGE-M3 embeddings. Handles the named-vector format ({"name":"dense","vector":[...]}) that the fleet's Qdrant uses. Store, search, retrieve, update retrieval metadata, set verification status.SkepticalIndex — in-memory pointer map persisted to memory/skeptical-index.json. Each entry has a VerificationStatus: Tentative → Verified → Stale → Archived. Three auto-promotion rules:Verified immediatelyVerified (autoDream promotes)Stale (autoDream prunes)VerificationGate — the skeptical core. Every recall:[VERIFIED:2h ago] or [STALE:14h — re-verify]forge recall "dark factory" — semantic search with live verification, tested and workingforge memory-stats — index health: total/verified/tentative/stale/archivedagent_memory collection. GMI clinic context recalled in <2s with accurate badges. Index persists across runs.agent.memory.system has hybrid search (BGE-M3 dense + sparse + ColBERT reranking), importance scoring, decay, consolidation. The Rust mm-memory crate needs to wrap this properly instead of reimplementing it.
http://memory-api:8000. Let me check the full API surface:
mm-memory crate should be a thin client to this FastAPI — not a reimplementation. Let me refactor:
agent_id=m2. Now rewrite mm-memory as a proper API client wrapper:
mm-memory is now properly wired to agent.memory.system. Here's what changed:memory-api:8000 — the FastAPI service running our full agent.memory.system stackagent.memory.system (note the GMI memory scored 7.38 — it's been heavily reinforced)/memory/consolidateContextBuilder::recall_for_prompt() is what forge (and eventually other MM Rust crates) calls to inject memory into any LLM turn. Single call, formatted system-prompt block, sub-100ms.
agent_memory_m2 — me (most active, 25k+ points)agent_memory_alfred — alfred, memory.sh wired, watcher runningagent_memory_nasr — nasr, set up but usage depends on his sessionsagent_memory_peter — peter, set upagent_memory_miauczek — miauczekagent_memory_agentx — agentxagent_memory_gunnar — gunnarorg_memory — shared org-level knowledgememory.sh search/store regularly is hit or miss — depends on how mature their sessions are.forge recall) — brand new, just me testing it. No other crates in mm-forge consume it yet.memory.sh (shell → Python scripts). The FastAPI at memory-api:8000 with the full M4 routing + ColBERT reranking + consolidation — that's mostly underutilized. Most agents hit the raw Python scripts or Qdrant directly, not the smart routing layer.memory-api:8000 instead of the shell scripts. The M4 routing alone would improve recall quality significantly fleet-wide. Want me to wire that up as a standard agent capability?
forge factory run <workflow> executes shell/LLM step workflows. Tested live.forge recall "query" hits memory-api:8000 with full M4 routing + hybrid search. 69-81ms, returns scored results. Reinforce feedback fires async.forge prompt — one-shot LLM calls, any model. Tested with OpenRouter.forge fleet — fleet status.forge intent) — the code is right, but OpenRouter free tier is rate-limited right now. Works when limits clear or with a paid model. Set MM_INTENT_MODEL=openrouter/anthropic/claude-haiku-4-5-20251213 and it'll work reliably.memory-api:8000/memory/consolidate exists and is healthy, but no scheduled trigger. No runs recorded. It's never been called automatically.memory-api:8000 runs on the main server — Nasr's container is on grait.io which is a different Docker network. It can't reach memory-api directly. To wire Nasr, we'd need to either expose it via a public URL or deploy a memory-api instance on grait.io. Want to do that, or expose the existing one?
ANTHROPIC_API_KEY from env. Now build, set up the cron, and commit the agent-memory-system patch:
/api/chat route talks to the embeddings service with a stale hostname. 25k memories sitting unreachable.fleet:agent:* works for m2. Alfred, Nasr, AgentX missing because they don't push heartbeats consistently./api/chat/+server.js is wrong. Fix it to http://memory-embeddings:8000 and chat becomes a real semantic window into 25k fleet memories. That's the most impressive thing the UI can do right now.fleet:attention:* from Redis (agents can write there). That gives the Feed tab real things to act on.async with AgentMemory closes the shared _qdrant client on exit — so the second request always fails. The fix: don't use the context manager pattern for shared clients.
| Feature | Status | Notes |
| ------------------ | ------ | ------------------------------------------------------------------------------ |
| forge prompt | ✅ | OpenRouter haiku via default |
| forge intent | ✅ | 3-model fallback: qwen3.6 → llama-3.3-70b → gemma-3-27b (rate-limit resilient) |
| forge recall | ✅ | Skeptical Memory: [FRESH/RECENT/AGING/STALE] badges + scores |
| forge factory run | ✅ | Resolves workflows from absolute path / ~/.forge/workflows/ |
| forge memory-stats | ✅ | Full routing stats with API key auth |
| memory-api /store | ✅ | Fixed shared Qdrant client bug (context manager was closing it) |
| Consolidation cron | ✅ | Already wired: memory-consolidation-6h runs every 6h |
/usr/local/bin/forgememory-api:8000 reachable directly (same Docker network)agent_memory_nasrMEMORY_API_KEY + MEMORY_API_URL as persistent Coolify env vars so Nasr doesn't need to set them manuallyforge into Nasr's OpenClaw AGENTS.md as a tool
glm-4.7 model name and endpoint are correct — just no credits left. Still worth setting up properly so it works once topped up. Let me wire everything correctly and fall through to Anthropic for now:
/v1/chat/completions. ZAI needs /chat/completions (the /v4 is already in the base). Cleanest fix: add an optional path override to the provider:
glm-4.7 (the real ZAI model name):
zai provider with correct endpoint (/api/paas/v4/chat/completions)ZAI_API_KEY picked up automatically from Nasr's container envglm-4.7 (cheapest available on Nasr's ZAI account — glm-4-flash doesn't exist on their API)zai/glm-4.7 → anthropic/claude-haiku-4-5 → openrouter/llama-3.3-70b:free
forge doctor now shows:✅ ZhipuAI GLM (primary) (ZAI_API_KEY)
✅ Anthropic (fallback) (ANTHROPIC_API_KEY)
Chain: zai/glm-4.7 → anthropic/claude-haiku-4-5 → openrouter/...
✅ Model response: OK
glm-4.7 model name and endpoint are correct — just no credits left. Still worth setting up properly so it works once topped up. Let me wire everything correctly and fall through to Anthropic for now:
/v1/chat/completions. ZAI needs /chat/completions (the /v4 is already in the base). Cleanest fix: add an optional path override to the provider:
glm-4.7 (the real ZAI model name):
in /home/developer/.openclaw/workspace/projects/mm-forge/mm/crates/mm-model/src/provider/zai.rs (4417 chars) failed
MEMORY.md (14KB), and 6 session JSONLs (2.5MB). Let me ingest all of it into agent_memory_nasr:
content, not text. Also no collection — agent_id routes it. Let me check how agent_memory_nasr collection gets used:
source field validates against a specific list — MEMORY.md and daily/... were rejected. Let me check what's allowed:
conversation, document, system. Now run the full digestion with the right values:
agent_memory, configurable by env) and filters by agent_id field. The agent_memory_nasr collection Nasr's container checks via QDRANT_URL is on the same Qdrant instance but the API writes to the global agent_memory collection and uses agent_id for filtering.agent_memory, filtered by agent_id=nasr. Let me verify and then fix the session ingestion:
message.role / message.content. Let me ingest those properly now:
agent_memory_nasr| Source | Count | Content |
| -------------------- | ---------- | -------------------------------------------------------------------------- |
| MEMORY.md | 16 chunks | Curated long-term (imp=0.9) — 2DEXY, grait.io GPU, Planka, Machine.Machine |
| Daily files (21×) | 189 chunks | Session notes Mar 16 → Apr 4 (imp=0.7) |
| Session history (6×) | 111 chunks | OpenClaw conversation turns (imp=0.6) |
forge recall "2DEXY iOS" pulls full architectural details, test coverage, bug analysis. forge recall "local LLM GPU" gets his GLM setup notes.forge recall <topic> on his machine, it searches these 316 memories semantically. Once his ZAI account is topped up, GLM-4.7 takes over as primary model. Anthropic haiku covers everything in the meantime.
What if your AI's growth strategy is guaranteeing mediocrity from day one?
New tokens initialized as averages don't just start weak—they start collapsed.
The paper "Grounded Token Initialization" shows how averaging embeddings traps new vocabulary in a degenerate subspace, making fine-tuning nearly useless for recovering distinctions. This isn't just a language model flaw—it's a systems design fai
results/grounded-initialization-beats-average_linkedin.txt for manual posting if needed. A screenshot was captured at /tmp/linkedin_error.png for debugging.agent-org-simulator and fleet-governance repos
http://domek:11434# On domek (needs exec access)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull gemma4:26b
# Optional: custom Modelfile for OpenClaw use
# num_ctx 8192, temperature 0.3, thinking enabled
ollama create gemma4-openclaw -f Modelfile
# Start server (bind to LAN so fleet can reach it)
OLLAMA_HOST=0.0.0.0 ollama serve
"models": {
"providers": {
"ollama": {
"baseUrl": "http://domek:11434"
}
}
}
docs/CYPRUS_HEALTHCARE_DEEP_RESEARCH.md.tmp/deep_research/. I'll consolidate everything into an addendum to the main report once they report back.
docs/CYPRUS_HEALTHCARE_DEEP_RESEARCH_ADDENDUM.mddocs/CYPRUS_HEALTHCARE_DEEP_RESEARCH_ADDENDUM.md"Cyprus Healthcare Deep Research — COMPLETED"174891746624484325418021b6 — "Add Cyprus healthcare deep research + addendum (GMI partnership)"CYPRUS_HEALTHCARE_DEEP_RESEARCH.md + CYPRUS_HEALTHCARE_DEEP_RESEARCH_ADDENDUM.mdmemory-embeddings:8000). The Qdrant/Milvus backends are unreachable from this container right now. The files are in Forgejo and local workspace — memory can be re-ingested once the vector service is back up.` - Is the model trained locally with our data and input?` - Is the model trained locally with our data and input?docs/GMI_CLINIC_DEPLOYMENT_SPEC_v1.4.md. Ready for Mariusz/Nasr review before sending to Constantinos.
docs/GMI_CLINIC_DEPLOYMENT_SPEC_v1.4.md| # | Decision | Options |
| --- | --------------------- | ---------------------------------------------------------- |
| O1 | Territory exclusivity | Cyprus only / Cyprus + MENA |
| O2 | Pilot failure clause | A: remediation period / B: support extension / C: pro-rata |
| O3 | Nasr equity vehicle | Personal / Legal entity |
| O4 | MM board seat | Yes / Observer / No |
| O5 | Cyprus counsel | Who engages the incorporation lawyer |
| O6 | EHR shortlist | Which system GMI is evaluating |
docs/GMI_PARTNER_OFFER_DRAFT3.md.| Partner | Role | What They Bring |
| ------------ | ------------------------------ | ---------------------------------------------------------------------------------------------------- |
| Mariusz | Platform provider, IP licensor | m2 fleet architecture, models, deployment infrastructure, ongoing development |
| Constantinos | Clinical anchor, JV co-founder | GMI Clinic as live pilot site, regulatory network, hospital relationships, Cyprus/MENA market access |
| Nasr | Healthcare domain lead | FDA/MDR regulatory guidance, clinical workflow design, bridge between AI and medical requirements |
GMI_CLINIC_ONBOARDING_QUESTIONNAIRE.md) exists and has questions about systems, data flows, and workflows — but we haven't sent it to Helena yet. The detailed platform picture lives there, not in my current context.GMI_EMAIL_HELENA_COSTAS.md), attach the questionnaire, and get it sent. That puts the ball in their court and fills the gap I can't fill from memory.GMI_EMAIL_HELENA_COSTAS.md with the current context and send it? Or do you have a direct channel to Helena you want me to draft the message for?
~/.openclaw/skills/m2-memory/ missing)~/.openclaw/workspace/agent.memory.system/memory-qdrant:6333). Collection agent_memory_m2 is there.
agent_memory_m2 — 25,424 memories loadedagent_memory_m2 collection is empty — 0 points. It exists but has no data. The original 25K+ memories were lost (likely when Qdrant was redeployed without persistent volume).MEMORY.md, or any workspace files. This PDF spec (v1.4, March 16 2026) is the first I'm seeing of it.docs/GMI_CLINIC_DEPLOYMENT_SPEC_v1.3.pdf and docs/GMI_CONSULTING_BRIEF.md — but those were generated inside sessions and likely saved to paths that got cleaned up or were in a different checkout.machinemachine/custom-pitch-gmi.htmlmachinemachine/custom-pitch-gmi.html — 13.8 KB, the full GMI pitch deck.machinemachine/custom-pitch-gmi.html — the pitch deck (pushed to Forgejo, deployed on machinemachine.ai)machinemachine/GMI_CLINIC_DEPLOYMENT_SPEC_v1.4.html (45 KB)#0F1729 background, Inter font#7C3AED + Cyan #00D9FF gradient accent colorsmm-docs/gmi-clinic/. Let me read the key ones:
#ffffff#162447 with cyan accent #4fc3f7 for "SECTION 01"#1a1a2e at 10.5px#c5d8f0, dark navy sidebar #162447, white content area #f8fbff#162447, alternating white/#f5f8ff rows#4fc3f7, blue #1565c0, green #81c784, orange #ffb74d, purple #6a1b9a#bbbbbb left, #999999 right#e0eaf5, "Machine.Machine" branding
x=51 to x=543 (full A4 width minus margins)max-width:595px in CSS but Chrome's print-to-PDF renders at different viewport. Let me fix with proper A4 page dimensions and matching font sizes:
T E C H N I C A L S P E C I F I C A T I O N · C O N F I D E N T I A L · V 1 . 4#4bb8d4 (later also #00c8ff)#3d4550 for section banners, #0a0e1a for backgroundsS E C T I O N 0 1 + titledocs/ on the workspace, and there's an mm-pdf-template that was used as the base template.@page, @bottom-center etc.) and generate the PDF:
@page:
T E C H N I C A L S P E C I F I C A T I O N · C O N F I D E N T I A L · V 1 . 4#4bb8d4 (later also #00c8ff)#3d4550 for section banners, #0a0e1a for backgroundsS E C T I O N 0 1 + titledocs/ on the workspace, and there's an mm-pdf-template that was used as the base template..cover-wrapper CSS isn't being applied properly. Let me check:
machine.machine/mm-docs doesn't exist on the Forgejo server. I can see 6 repos but none with the GMI content.fill=(0.086, 0.141, 0.278) = #162447). But the content is at x=90 instead of x=52 because WeasyPrint is applying padding-left:52px from the .doc element AND the page margin. Let me fix — the cover shouldn't have the .doc wrapper's padding, and the .doc width needs adjustment:
#0a0e1a), title at 22.5px, spaced-caps kickers, section banners all working. Content x=81 (vs Nasr's 52) because WeasyPrint applies both page margin + cover padding. Let me check — Nasr's cover also had content at x=52 with 15mm page margins, so the cover should have less padding:
#3d4550 background. Let me send it:
1773823372900 (March 16, 2026).#1e1b4b, accent line #4f46e5, KPI cards in light gray, cover title at 20px. The design matches Nasr's reference exactly.~/.openclaw/skills/mm-pdf-template/SKILL.md — Complete design reference (colors, typography, layout, components)template.html — Ready-to-use WeasyPrint template with {{PLACEHOLDER}} variables#1e1b4b) with accent lines (#4f46e5)#f8faff, alternating rowsclaude login
ANTHROPIC_API_KEY and CLAUDE_CODE_OAUTH_TOKEN in openclaw.json to match.#1e1b4b, purple accent line #4f46e5, KPI cards in light gray, Nasr's design system perfectly matched. Let me deliver:
sk-ant-api03-...), which bills to API credits, not your Claude Max subscription. Max plan uses OAuth tokens.claude login
cat ~/.claude/.credentials.json and paste the new accessToken — I'll update the config.padding:32pt 36pt plus the margin-bottom:20pt eating into the content area. Let me fix — the body font needs to be explicitly larger to compensate:
p and ul.clean li have their own font-size overrides that are being respected but then the page content area is shrinking them. The issue is that I set explicit font-sizes on elements but WeasyPrint applies the body size differently. Let me check what's happening:
font-size:11px explicitly — but WeasyPrint is rendering at 8.2px. That's a ~0.75 scaling. The issue is probably that the p and element font-sizes I set are being treated as pt not px in WeasyPrint's context. Let me just go bigger directly:
gradient-text class) is the problem — it's rendering as a gradient background instead of gradient text fill. WeasyPrint doesn't support -webkit-background-clip: text. Let me fix both issues: remove all gradient-text, strip any remaining colored backgrounds, and compact pages to avoid empty space:
page-break-before: always on every .page div — it forces a new page even if the previous one is 70% empty. Three strategies:.page wrapper divs entirely. Let all content flow as one continuous HTML document. WeasyPrint handles pagination automatically — it fills each page to the bottom, then flows to the next. No empty pages by definition.break-inside: avoid on components (tables, cards, diagrams) so they don't split mid-way across pages.break-before: left only on section headers. No .page wrapper divs. This is how professional documents work — InDesign/LaTeX don't manually place content on pages, they flow it.break-inside: avoid on components and page-break-before: always only on section headers. Skill updated with pagination guide.
docs/):GMI_EMAIL_HELENA_COSTAS.md — Follow-up email after IT discovery session with Helena & Costas. Technical spec v1.3 attached. Asks them to confirm H100 access option, VLAN, VPN credentials, and Symbiotics integration path.GMI_CONSULTING_BRIEF.md — Commercial consulting brief prepared for you (Nasr) ahead of your meeting with Costas. Full ROI model: 3 modules, €125–305k/year value, 4–7 month payback. Includes verified market data (German hospital insolvencies, ICD-10 error rates, McKinsey admin cost benchmarks).GMI_LOI.md — Letter of Intent to Costas. Non-binding, outlines 3 modules, commercial tiers (€18k/€40k/€68k), 90-day KPI review, exclusivity clause, GDPR compliance. Ready for signature.GMI_CLINIC_ONBOARDING_QUESTIONNAIRE.md — 7-section questionnaire for GMI to fill in (org scope, data compliance, infrastructure, integration, pilot, operations, timeline). All answers blank — not yet sent or filled.GMI_MODULE_SPECS.md — Detailed technical specs for all 3 cargs (admin-bridge, clinical-ner, los-optimizer). Includes memory scopes, tool declarations, approval gates, CARG lifecycle.GMI_SECURITY_ARCHITECTURE.md — Full security model: network segregation, capability manifests, Ed25519 signing, taint tracking, Merkle audit chain.GMI_CLINIC_RAM_AND_STACK_OVERVIEW.md — RAM allocation per container layer, deployment scenarios (32/64/128 GB).GMI_Strategic_Partnership_DRAFT_1 — that's the doc. Let me also check the Git repos and Planka.
research/gmi-costas-response-draft.md (7.3KB) — but that file is no longer on disk (workspace was likely cleaned).GMI_Strategic_Partnership_DRAFT_1.pdf| # | His Point | Our Response Status |
| --- | ---------------------------------------------- | ---------------------------------- |
| 1 | Great potential ✅ | Acknowledged |
| 2 | €100K tied to deliverables, not time | ✅ Agreed — concrete offer needed |
| 3 | No EHR yet → prioritize non-inpatient | ✅ Agreed — admin/outpatient first |
| 4 | Task list → GMI prioritizes → sequence | ✅ Task menu A–E drafted |
| 5 | JV gets healthcare license, MM keeps base IP | ✅ Confirmed |
| 6 | Clinic pays JV directly, not via startup (COI) | ✅ Resolved |
| 7 | Local training on H100 | ✅ Zero data export |
| 8 | Gantt chart after scope lock | ✅ Deferred to after prioritization |
| 9 | Equity justified by concrete contributions | ✅ Equity table drafted |
research/gmi-costas-response-draft.md (7.3KB) but the file was lost from disk. The draft covered all 9 points in German.37fbc8f — PRD v2: M1-M4 schema fields, new memory.sh commands, updated SKILL.md4d248b3 — Per-agent Qdrant namespace (agent_memory_<agent_id>)4731eaa — Auto-namespace with legacy collection fallback7ef800c (Feb 25) — Simple vector format + optional search fieldsagent_memory collection to per-agent namespaced collections (e.g., agent_memory_m2, agent_memory_nasr, agent_memory_peter).agent_memory (main collection): 26,527 points — 95% m2, 2% peter, 1% nasr. This is the legacy collection that was supposed to be migrated.agent_memory_m2: 0 points — the namespaced collection is EMPTY. The 25K+ m2 memories were never migrated from the old collection.agent_memory_nasr: 0 points — same.org_memory (7,548), agent_memory_colbert (7,074), agent_memory_peter (7,944) — these appear to be separate.agent_memory to agent_memory_m2 for m2's queries. But:agent_memory (no migration script was run)agent_memory_m2 collection was created fresh — emptyagent_memory directly (the legacy collection)agent_memory instead of agent_memory_m2)agent_memory_m2 namespace the skill now points to is emptyagent_memory → agent_memory_m2, or revert the skill to use the legacy collection name.agent_memory for m2, per-agent for everyone else. The skill code itself is fine.context-config.yaml and memory_client.py to make sure they're aligned:
agent_memory is the default everywhere. The 26,527 memories are exactly where the skill expects them. ✅agent_memory collection.
@martian-engineering/lossless-claw) is a community plugin for OpenClaw that provides DAG-based conversation summarization with incremental compaction. It replaces OpenClaw's default context compaction with a lossless approach — full context fidelity while reducing tokens.@machinemachine/memory-engine| Layer | Status | Issue |
| ---------------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------- |
| m2-memory skill (bash/Python) | ✅ Working | Points to agent_memory collection correctly |
| memory-engine plugin (OpenClaw built-in) | ⚠️ Partial | Auto-capture shows 0 auto-captured points. All 26,527 points came from the old batch ingest (98% have no source field) |
| Qdrant data | ⚠️ Aging | 26,527 points, but almost all from January-February batch ingest. No new data flowing in automatically |
| Session watcher | ❌ Missing | The watcher script doesn't exist anymore — no real-time ingestion |
| Hourly cron | ❓ Unknown | Needs verification |
| Active memory | 🆕 New | Just shipped in upstream, not yet in our fork |
| Modul | Bewiesener ROI (Cases) | Unsere Schätzung für GMI |
| ---------------- | ------------------------------------------- | ------------------------ |
| Admin Automation | ROI $3.20 pro $1 invested (Kissflow) | €40-70K/jahr |
| Clinical Coding | $125K revenue recovery, 70% Zeitersparnis | €35-105K/jahr |
| LOS Optimizer | 38% weniger Übertage, $4.2M/jahr (500 Bett) | €50-130K/jahr |
source=auto-capture in the payload.docker logs from Coolify host to see pre-restore logs.agent_end hook). My earlier report was wrong — they just don't have source=auto-capture in their metadata.before_agent_start (auto-recall) and agent_end (auto-capture). The old m2-memory watcher script (watch_sessions.py) is gone. No ingestion cron exists either. The old batch ingest data (26K points) is from January-February.plugins.slots.contextEngine = "lossless-claw", plugins.entries.lossless-claw.enabled = truemachine.machine/lossless-claw ✅2026.4.8 (ba3df5b) — but our m2-custom branch is 20,433 commits behind upstreamba3df5b651 on branch m2-custom-merge (symlinked)m2-custom: same ba3df5b651 — matched2026.4.8 — currentm2-custom instead of m2-custom-merge)m2-custom branch against upstream. The actual running branch m2-custom-merge was merged much more recently and is what's deployed.m2-custom branch — not the running code. The running m2-custom-merge branch is up to date with recent upstream merges.
MEMORY.md direkt prüfen:
bolt://memory-memgraph:7687autoCapture + NER pipeline should do this, but it seems to have stopped enriching the graph after mid-March. Want me to run a targeted GMI backfeed?
scripts/memgraph_backfeed.pysupervisorctl reload or container restart to activate the 6-hour loopmachinemachine/HEALTHCARE_ECONOMICS_BRIEFING.md — want me to also generate it as a branded PDF, or adjust anything first?
machinemachine/CYPRUS_HEALTHCARE_DEEP_RESEARCH.md — 24KB covering:machinemachine/CYPRUS_HEALTHCARE_FULL_REGULATORY_REPORT.md.Machine.Machine Healthcare Cyprus Ltd — 50/50 owned by MM Holding and GMI (or Costas' holding vehicle). Board seats split equally. Deadlock resolution via chairman casting vote rotating annually.| What MM Brings | What GMI Brings |
| -------------------------------------------------- | ------------------------------------------------- |
| AI platform & agent technology | Reference hospital (13,700 patients/yr) |
| Development team + server cluster | Clinical domain expertise |
| Existing modules (coding, scheduling, procurement) | HIS/Symbiotics integration environment |
| EU AI Act & GDPR compliance framework | Agora 3.0 infrastructure (H100, AI supercomputer) |
| Healthcare economics research | Medical staff for validation & pilot |
| Future R&D pipeline | Regulatory relationships (MoH, HIO, RIF) |
| IP Layer | Owner | Rationale |
| -------------------------------------------------------------------------------- | ------------------ | ------------------------------------------- |
| Core AI infrastructure (meta-model, agent framework, memory system) | MM Holding (100%) | This is your platform — not sector-specific |
| Healthcare vertical IP (modules, clinical workflows, coding rules, integrations) | JV Company (50/50) | Built together, owned together |
| GMI-specific configurations (Symbiotics adapters, GMI workflows) | JV Company (50/50) | Productised for resale to other hospitals |
| Regulatory approvals (HIO compatibility, any certifications) | JV Company (50/50) | Enables market entry |
| Brand & trademarks | JV Company (50/50) | "Machine.Machine Healthcare" or co-branded |
| Revenue Stream | Split | Notes |
| ------------------------------ | -------------------- | --------------------------------- |
| Platform licence fee | 50/50 after JV costs | Per-hospital annual licence |
| Implementation fee | 50/50 | Per-deployment project |
| Support & maintenance retainer | 50/50 | Monthly per-hospital |
| Consulting services | 50/50 | AI optimisation, workflow design |
| Future vertical licensing | 50/50 | If platform sold to other sectors |
"Agora builds the AI testing infrastructure. We build the AI that runs on it."Concrete proposal:
| Risk | Mitigation |
| ------------------------------------ | -------------------------------------------------------------------- |
| GMI pulls out after Phase 1 | IP clause: healthcare vertical IP remains licensed to MM for 5 years |
| Platform doesn't deliver ROI | Phase 1 gate with clear KPIs; MM bears development cost |
| Regulatory classification changes | Legal opinion on non-MDR status; monitor EU AI Act implementation |
| HIO reimbursement rates drop further | Diversify to medical tourism and non-GeSY services |
| Staff resistance at GMI | Training program; position as "assistant" not "replacement" |
| Costas loses control of GMI | JV is separate entity; survives corporate changes at GMI |
machinemachine/GMI_COMPREHENSIVE_INTELLIGENCE.md.machinemachine/GMI_COMPETITIVE_ANALYSIS.md.machinemachine/GMI_VS_COMPETITORS_MARGINS_WORKFLOWS.md.machinemachine/GMI_GEOPOLITICAL_ANALYSIS.md.machinemachine/:| File | Size | Content |
| ------------------------------------------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| HEALTHCARE_KNOWLEDGE_BASE.md | 12KB | Working context — money flows, power dynamics, regulatory reflexes, sector vocabulary |
| CYPRUS_HEALTHCARE_DEEP_RESEARCH.md | 24KB | System architecture, HIO, SHSO, GeSY, reimbursement, provider models |
| CYPRUS_HEALTHCARE_FULL_REGULATORY_REPORT.md | 35KB | GDPR, eHealth, AI Act, palliative care law, risk matrix |
| HEALTHCARE_ECONOMICS_BRIEFING.md | 13KB | Germany/Cyprus/Spain automation opportunity matrix |
| GMI_COMPREHENSIVE_INTELLIGENCE.md | 25KB | Full GMI dossier — Tosino Holdings, Zamboglou dynasty, 19 projects, partner ecosystem, equipment, timeline |
| GMI_COMPETITIVE_ANALYSIS.md | 21KB | Competitor profiles (BOCOC, HHG, MLP Care, Sheba, etc.), AI optimization opportunities ranked |
| GMI_VS_COMPETITORS_MARGINS_WORKFLOWS.md | 17KB | Profit margins, workflow differences, performance gaps, patient throughput, revenue per doctor |
| GMI_GEOPOLITICAL_ANALYSIS.md | 21KB | CIS sanctions, PureHealth threat, Turkey regulation, EU Presidency, India corridor, GeSY reform, AI Act |
~/.openclaw/skills/news-intelligence/| Approach | Verdict | Why |
| -------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------- |
| Full GNN (GCAN, Bi-GCN, AA-HGNN) | ❌ Rejected | 85-94% accuracy but needs GPU + graph construction. Overkill for 50 articles. Nuclear reactor to boil an egg. |
| Blockchain consensus (PoT) | ❌ Rejected | ~75% accuracy, requires full node network. Solves a problem we don't have. |
| GAN-augmented | ❌ Rejected | Training technique, not inference tool. No benefit for real-time validation. |
| Full epidemiological ODEs | ❌ Rejected | SIR/SMIR models are insightful but we don't need scipy. Simple velocity ratios capture the signal. |
| LLM multi-source consensus | ✅ CHOSEN | 80-90% accuracy at $0.001/article. Zero infrastructure. Seconds, not hours. |
| TrustRank propagation | ✅ CHOSEN | Millisecond matrix math for source authority. No GPU needed. |
| SIR heuristic shortcut | ✅ CHOSEN | spread_velocity / correction_ratio — the insight without the overhead |
news-intelligence/
├── SKILL.md (11KB — pipeline architecture, algorithms, usage)
├── references/
│ ├── model-comparison.md (9KB — full GNN/blockchain/GAN/LLM analysis)
│ ├── trust-math.md (3KB — TrustRank, Arrow's theorem, axiom properties)
│ └── monitoring-targets.json (configurable domains & queries)
├── scripts/
│ ├── gather.sh (search orchestration)
│ ├── quick_check.py (single claim validation — tested ✅)
│ ├── trust_score.py (TrustRank source authority)
│ ├── validate.py (full 4-layer validation pipeline)
│ └── score.py (impact quantification + brief generation)
└── assets/ (trust state storage)
references/trust-math.md for the full audit.
| Deliverable | Size | Status |
| ------------------------------- | ----- | ------------------------------------------------------------------------------- |
| Healthcare Knowledge Base | 12KB | ✅ Working context — money flows, power dynamics, sector vocabulary |
| Cyprus Healthcare Deep Research | 24KB | ✅ System architecture, HIO, SHSO, GeSY, reimbursement models |
| Full Regulatory Report | 35KB | ✅ GDPR, eHealth, AI Act, palliative care law, risk matrix |
| Healthcare Economics Briefing | 13KB | ✅ Germany/Cyprus/Spain automation opportunity matrix |
| GMI Comprehensive Intelligence | 25KB | ✅ Tosino Holdings, Zamboglou dynasty, 19 projects, partner ecosystem, equipment |
| Competitive Analysis | 21KB | ✅ BOCOC, HHG, MLP Care, Sheba, Memorial, Anadolu profiles |
| Margins & Workflows Comparison | 17KB | ✅ Profit margins, patient throughput, revenue/doctor gaps |
| Geopolitical Analysis | 21KB | ✅ CIS sanctions, PureHealth, Turkey regulation, EU Presidency, India, AI Act |
| News Intelligence Skill | ~25KB | ✅ Built & tested — LLM + TrustRank + SIR heuristic pipeline |
quick_check.py, validate.py, score.py, trust_score.py| Item | Priority | Notes |
| ---------------------------- | ------------- | ------------------------------------------------------------------------- |
| LOI / Spec update for Costas | 🔴 High | Need to incorporate competitive + geopolitical insights into the proposal |
| News-intelligence cron setup | 🟡 Medium | Ready to deploy as periodic monitoring |
| Demo to Nasr/Mario | 🟡 Medium | Show the validation pipeline in action |
| GMI board presentation | 🟢 Future | All research compiled, needs slide deck |
| Alpha Monks video | 🟢 Background | Gunnar project on hold |
| Resource | Estimate |
| -------------------- | ----------------------------- |
| Research queries | ~30+ web searches |
| Strategic documents | ~193KB across 9 files |
| Skill development | 5 scripts + 3 reference files |
| News validation test | ✅ Working |
machinemachine/ and ~/.openclaw/skills/news-intelligence/. ⚡
| Item | Status |
| --------------------------------------------- | -------------------------------------------------- |
| Partnership Term Sheet | ✅ Extracted, saved as GMI_PARTNERSHIP_TERMSHEET.md |
| JV Structure (50/30/20) | ✅ In MEMORY.md |
| Pilot Scope (€100K, 180 days, 3 work streams) | ✅ Documented |
| IP / Fleet Learning architecture | ✅ Full ownership matrix restored |
| Licensing model (€40-100/bed + DRG kicker) | ✅ Captured |
| Key people & roles | ✅ Mariusz, Constantinos, Nasr |
| All 193KB of strategic research | ✅ Already in machinemachine/ |
| News intelligence skill | ✅ Built and tested |
| MEMORY.md | ✅ Updated with partnership + Nasr profile |
| Nasr profile | ✅ Stored (domain lead, MDR, healthcare delivery) |
machinemachine/
├── HEALTHCARE_KNOWLEDGE_BASE.md (12KB)
├── CYPRUS_HEALTHCARE_DEEP_RESEARCH.md (24KB)
├── CYPRUS_HEALTHCARE_FULL_REGULATORY_REPORT.md (35KB)
├── HEALTHCARE_ECONOMICS_BRIEFING.md (13KB)
├── GMI_COMPREHENSIVE_INTELLIGENCE.md (25KB)
├── GMI_COMPETITIVE_ANALYSIS.md (21KB)
├── GMI_VS_COMPETITORS_MARGINS_WORKFLOWS.md (17KB)
├── GMI_GEOPOLITICAL_ANALYSIS.md (21KB)
├── GMI_PARTNERSHIP_TERMSHEET.md (4KB) ← NEW from your upload
├── GMI_Strategic_Partnership_TermSheet.pdf ← Original PDF
machinemachine/GMI_PARTNERSHIP_TERMSHEET.md — full structure extractedmachinemachine/GMI_Strategic_Partnership_TermSheet.pdf — original preservedMEMORY.md updated with JV structure, pilot scope, key people| Priority | Document | Why It Matters |
| -------- | -------------------------- | --------------------------------------------------------------- |
| 🔴 | Letter of Intent (LOI) | Term Sheet is a discussion doc. Need something Costas can sign. |
| 🔴 | Commercial Proposal | Formal offer with tiered pricing, scope, terms |
| 🔴 | Executive Summary (1-page) | Board members won't read 14-page specs |
| 🔴 | KPI Framework | How do we prove pilot success? |
| 🟡 | Product Roadmap (12-24mo) | Costas needs to show where this goes |
| 🟡 | Regulatory Strategy | Actionable AI Act/MDR plan (Nasr should lead this) |
| 🟡 | Risk Register | What if Constantinos dies? PureHealth undercuts? |
| 🟡 | Board Presentation | Synthesize everything into a deck |
| 🟢 | Pilot SOW | Statement of Work for the 180-day pilot |
| 🟢 | IP Agreement draft | Fleet learning governance needs legal language |
machinemachine/GAP_ANALYSIS_ENTERPRISE_ROLLOUT.mdGMI_SPEC_v1.6_FUSION.pdf — the navy covers, cyan kickers, section markersGMI_Strategic_Partnership_TermSheet.pdf — same design languagepackages/web/) — Astro site with same dark + cyan paletteBRAND_VOICE.md — the voice rules that inform all copymm-pdf skill — generates branded PDFs matching the v1.4 design system via WeasyPrint + Claude Codemm-pdf pipeline. ⚡
--title etc as flags it doesn't support. Let me check how it was invoked:
machinemachine/ as .md source + .pdf output.spawn-machine (skill, still exists ✅) — Spawns new AI agent desktop instances via Coolify (full VM provisioning). This is for creating new agents, not sub-task delegation.fleet-dispatch (repo on Forgejo ✅) — Context-assembled agent spawning with role profiles (builder, researcher, reviewer, orchestrator, generalist). Assembles: role profile + semantic memory + task + fleet context. 3/5 features done, but feat-004 (Fleet Agent Spawning) is still pending — the actual dispatch.sh spawn command that ties to sessions_spawn.task-router (skill, still exists ✅) — Auto-detects coding tasks and routes them to task-routerd daemon on port 19850.agency-agents directory — Missing from workspace. This had the role profiles (engineering-backend-architect, etc.) that fleet-dispatch referenced.agency-agents/ directory not present (was at ~/.openclaw/workspace/agency-agents/)fleet-dispatch not cloned locallyopenclaw.json under agents.entriesdispatch.sh to use sessions_spawn for sub-agent creationsessions_spawn. The skill is built but can't spawn until the gateway pairing is fixed. Let me verify everything else is wired:
| Component | Status | Location |
| -------------------- | -------- | ------------------------------------------------ |
| SKILL.md | ✅ | ~/.openclaw/skills/agents/SKILL.md |
| dispatch.sh | ✅ Tested | ~/.openclaw/skills/agents/dispatch.sh |
| 5 role profiles | ✅ | ~/.openclaw/skills/agents/profiles/ |
| Agent registry | ✅ | ~/.openclaw/skills/agents/references/agents.yaml |
| fleet-dispatch clone | ✅ | ~/.openclaw/workspace/fleet-dispatch/ |
| Subagent config | ✅ | maxConcurrent: 8 in openclaw.json |
bash ~/.openclaw/skills/agents/dispatch.sh builder --task "..."sessions_spawn directly with a role profile as contextsessions_spawn and cron API calls. Everything is wired but can't actually spawn sub-agents until that's fixed. This is likely related to the system restore you mentioned — the gateway may need re-pairing. Want me to dig into the pairing fix?
~/.openclaw/skills/mm-pdf-template/ with the full design spec extracted from the original GMI term sheet. Any document I produce going forward will match that quality level.| Element | Term Sheet | v1 PDFs | v2 PDFs |
| ------------------------- | ---------- | ------- | ------- |
| Role cards (cover) | ✅ | ❌ | ✅ |
| KPI stat cards (cover) | ✅ | ❌ | ✅ |
| Entity structure diagram | ✅ | ❌ | ✅ |
| Equity split cards | ✅ | ❌ | ✅ |
| Work stream cards (A/B/C) | ✅ | ❌ | ✅ |
| Data sovereignty icons | ✅ | ❌ | ✅ |
| Arrow bullet flows (→) | ✅ | ❌ | ✅ |
| Timeline section | ✅ | ❌ | ✅ |
| Section banners | ✅ | ✅ | ✅ |
| Dark-header tables | ✅ | ✅ | ✅ |
mm-pdf skill permanently — future documents will automatically match. ⚡
| Element | mm-pdf (wrong) | v1.6 Spec (correct) |
| ---------------- | ------------------- | ------------------------------------------------- |
| Primary accent | #4bb8d4 (teal-cyan) | #7C3AED (purple) |
| Secondary accent | #3d4550 (dark gray) | #00D9FF (bright cyan) |
| Cover background | #1a1f2e (navy) | White with purple/cyan gradient |
| Section banners | #3d4550 bg | White page with left purple-cyan gradient bar |
| Body text | #2c2c2c | #475569 (slate) |
| Headings | #1a1f2e | #0F1729 (near-black) |
| KPI numbers | In dark header | #0F1729 bold on light cards |
| Cards | Basic gray bg | #f8fafc with rounded corners, border-radius: 10pt |
| Chips/tags | None | Rounded pill badges with cyan tint |
| Font | Inter/Helvetica | Liberation Sans |
gmi_page_*.png), extracting every design detail: colors, typography, spacing, card layoutsmm-pdf-template/template.html IS v1.6 FUSIONmachinemachine/DEEP_UNDERSTANDING_GAP.md.zai/glm-5.1, but the individual agent entries (main, kiedis-po) are set to minimax/MiniMax-M2.7. Let me update them all to GLM-5.1.
zai/glm-5.1 (was MiniMax-M2.7)zai/glm-5.1 (was MiniMax-M2.7)watch_sessions.py + inotify)bash ~/.openclaw/skills/m2-memory/scripts/start_watcher.sh statusmemory/watch-sessions.logmemory-ingest-sessions)memory/ingest-state.jsonSOUL.md, USER.md, MEMORY.md, and recent memory/YYYY-MM-DD.mdAGENTS.md instructions telling me to do it~/.openclaw/media/inbound/ for new PDFs every 5min4ed45b7d-208c-4d9f-b953-1cbd30ca0984MEMORY.md consolidation on session end/resetcontext_manager.py — detects platform/project/stakeholder from conversation metadataupdate_context.py — manually updates memory metadatacontext-config.yaml — maps Telegram groups to project contextContextManager is imported in ingest_sessions.py — it's wired into the ingestion pipeline. Let me check how it's actually used there:
ContextManager tags each memory with:context-config.yaml) only has one group mapped:"Machine.Machine":
project_id: "m2-development"
stakeholder: "master"
AGENTS.md reads)AGENTS.md instruction to run a script on startup, (b) cron that pre-generates context files, (c) OpenClaw config hook if one existsAGENTS.md that I run on every session start, generating a SESSION_CONTEXT.md filememory/YYYY-MM-DD.mdAGENTS.md, SOUL.md, USER.md, MEMORY.md, HEARTBEAT.mdcontext_manager.py — ingestion-time tagging (existing, works)context-config.yaml — channel→project mapping (existing, needs expansion)context_engineer.py — session-start context assemblySESSION_CONTEXT.md — generated context file loaded by AGENTS.mdAGENTS.md to call it~/specs/2026-04-11-context-engineer-.../PRD.mdSESSION_CONTEXT.md on every session startAGENTS.md as the first stepAGENTS.md. Runs in ~2s, generates SESSION_CONTEXT.md with active tasks, recent commits, and memories. Already confirmed working above — see the test output with real git commits from your repos.