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InvestorClaw — Hermes Skill (v4.0)

Powered by InvestorClaw (Apache 2.0). This skill file is MIT-0-licensed; the underlying service is Apache 2.0.

TL;DR for hermes operators

InvestorClaw v4.0 turns hermes into a first-class portfolio-analysis agent. The service runs as two local Docker containers and exposes its capabilities over MCP-HTTP. Hermes 0.12+ registers the MCP servers as native function-callable tool sources — the LLM sees investorclaw.portfolio_ask, mnemos.search_memories, and friends in the same tool catalog as browser_*, terminal, and skill_view.

Headline upgrade — HER-1 is gone. Read the next section if you remember v2.x.

What changed since v2.x — HER-1 elimination

If you ran InvestorClaw v2.x against hermes, you hit HER-1: the "skill-as-doc-hint" caveat. In v2.x, InvestorClaw shipped as a hermes skill bundle injected into the system prompt. The LLM had to use hermes meta-tools (skill_view, terminal) to read the skill documentation and then imitate the analyst commands by shelling out. That indirection layer was lossy and slow, and the Linux baseline empirical reliability landed around 8% (2.3/30) on the standard prompt barrage — vs 77% on zeroclaw, which had real tool registration.

v4.0 ends that. There is no skill bundle to inject. The deterministic engine runs as a containerized service and publishes its analytical surface over MCP-HTTP. Hermes 0.12+ registers MCP servers declaratively in ~/.hermes/config.yaml and exposes their tools to the LLM directly — same dispatch path as any other built-in tool.

What this means in practice for hermes users:

• No more meta-tool indirection. The LLM calls

investorclaw.portfolio_ask directly, not via skill_view → terminal → fragile shell parsing.

• Reliability now matches other agent runtimes. Expect the same

routing accuracy as zeroclaw / openclaw — roughly an order of magnitude better than v2.x on the same prompts.

• Memory is built in. The mnemos.* tool family gives hermes a

persistent memory layer it never had before, scoped to InvestorClaw observations and user preferences.

• No skill bundle to keep in sync. Bumping the service to a newer

ic-engine version is docker compose pull && docker compose up -d. The tool catalog hermes sees is whatever the running service publishes.

Architecture (hermes ⇄ InvestorClaw)

hermes (host)
  │
  │  config.yaml mcp_servers:
  │     investorclaw → http://localhost:18090/mcp
  │     mnemos       → http://localhost:5002/mcp
  ▼
Docker compose (~/.investorclaw/compose.yml)
  ├── argonautsystems/ic-engine:4.7.7-cpu       :8090   portfolio analysis MCP
  └── mnemos-os/mnemos-rs:4.2       :5002   memory + KG MCP
       (dashboard at :8092 for portfolio upload + key config)

The user runs docker compose up -d to install the service. Hermes discovers tools at startup by handshaking with each MCP server.

Tool surface

When InvestorClaw is running and hermes has reloaded its config, the tool catalog gains:

Portfolio analysis (investorclaw.*)

• investorclaw.portfolio_ask — natural-language portfolio question

routed through the deterministic engine

• investorclaw.portfolio_holdings — current snapshot of positions,

values, weights

• investorclaw.portfolio_performance — Sharpe, volatility, top /

bottom performers, max drawdown

• investorclaw.portfolio_bonds — bond analytics (YTM, duration,

FRED yield curve)

• investorclaw.portfolio_analyst — analyst ratings per holding
• investorclaw.portfolio_news — news correlation for held positions
• investorclaw.portfolio_lookup — ticker / account lookup
• investorclaw.portfolio_optimize — Sharpe / min-vol optimization
• investorclaw.portfolio_rebalance — current vs target with tax

impact

• investorclaw.portfolio_scenario — what-if scenarios on holdings
• investorclaw.portfolio_cashflow — projected cashflow from bonds
• investorclaw.portfolio_peer — peer comparison vs benchmark
• investorclaw.portfolio_setup — auto-discover portfolio files in

/data/portfolios/

• investorclaw.portfolio_refresh — refresh market data without

re-uploading files

• investorclaw.portfolio_guardrails — view / configure

educational-only guardrails

Memory (mnemos.*)

• mnemos.search_memories — full-text + semantic search across

remembered observations

• mnemos.create_memory — record an observation about user

preferences, prior questions, or current investing context

• mnemos.list_memories — browse by category / date

Usage idioms

Just ask portfolio questions in chat. Hermes' LLM picks the right MCP tool from the catalog automatically.

Cookbook — what to ask

The first call after a cold cache may take 30–60 seconds while the deterministic pipeline builds the signed envelope; subsequent calls reuse the cache.

hermes chat -q "What's in my portfolio?" \
  --provider together -m google/gemma-4-31B-it --yolo

hermes chat -q "What changed since last week?" \
  --provider together -m google/gemma-4-31B-it --yolo

hermes chat -q "Refresh my market data and show me the worst
                performer." \
  --provider together -m google/gemma-4-31B-it --yolo

Recommended narrative model

hermes routes its chat completions through whichever provider the user selects on the command line or in ~/.hermes/config.yaml. Anthropic on hermes — paid path only since 2026-04-04: routing OAuth- subscription tokens to a claws-agent violates Anthropic's ToS per their Apr 3 announcement. To use Anthropic models you need either (a) the discounted "extra usage bundle" add-on for your subscription, or (b) a direct Anthropic API key. Even with paid credits, Anthropic isn't cost-competitive with Together for InvestorClaw narrative work; we don't deploy Anthropic on our own fleet for hermes.

Recommended providers for the InvestorClaw narrative tier (set TOGETHER_API_KEY in the container's portfolios/keys.env or via portfolio_keys_set):

• Default narrative — Together AI google/gemma-4-31B-it — serverless

tier, ~100 tok/s, ~$0.0008 / 1 K tokens, fleet default. This is what the InvestorClaw container expects via INVESTORCLAW_NARRATIVE_MODEL.

• Higher-quality alternative — Together AI MiniMaxAI/MiniMax-M2 —

larger context, but moved off Together's serverless tier 2026-05; requires a paid dedicated endpoint.

• Local-only / offline — Ollama gemma4:e4b on host — zero cloud

cost, GPU-bound, no key required.

Recommended LLM behavior (the model already does this on its own, but worth knowing):

1. Portfolio questions → call investorclaw.portfolio_ask with

the user's natural-language question. The deterministic engine routes it to the correct analyzer and returns a structured ic_result envelope plus a narrative body.

1. Follow-up questions → call mnemos.search_memories first to

pull relevant prior observations (risk tolerance, prior holdings discussions). Then call the appropriate investorclaw.* tool with that context.

1. What-changed questions → combine mnemos.search_memories for

prior portfolio summaries with investorclaw.portfolio_holdings for the current snapshot; let the LLM diff them.

1. After delivering an analysis → call mnemos.create_memory to

record salient observations the user might want to remember (e.g., "User flagged BABA as a never-sell sentimental position"). Don't over-record.

Important behaviors

• Deterministic at the data layer. If a portfolio file format

isn't recognized, the tool returns a structured error with detected columns and supported formats. Surface that to the user — point them at the dashboard's column-mapping wizard at http://localhost:18092/portfolios/map.

• Educational only — never investment advice. All outputs include

the disclaimer envelope. Echo it when summarizing.

• MCP servers are local by default at http://localhost:18090/mcp

and http://localhost:5002/mcp. Remote deployments (Tailscale, cloud) just change the URLs — the tool surface is identical.

• No portfolio? No problem. The LLM can talk about generic

market questions via investorclaw.portfolio_ask even before a portfolio file is uploaded; it'll guide the user to the dashboard.

Install pointer

Hermes does not use ClawHub — ClawHub is the OpenClaw / ZeroClaw skill registry. Running clawhub install drops the skill into an openclaw path, not Hermes (and hermes skills install investorclaw won't find it — it isn't in a Hermes registry). Install into Hermes manually:

1. Copy this skill directory into ~/.hermes/skills/investorclaw/.
2. Bring up the InvestorClaw container (the engine):

cd ~/.investorclaw && docker compose up -d.

1. Paste the MCP block from config-snippet.yaml into

~/.hermes/config.yaml, then restart Hermes.

INSTALL.md next to this file has the full step-by-step (skill drop, compose up, config block, restart, verify).

The dashboard (http://localhost:18092) and the agent's MCP tools both come from the container in step 2 — the skill itself is only the agent-side pointer, so Docker must be running either way.

(Claude Code / Claude Desktop users instead install the marketplace plugin from argonautsystems/InvestorClaw; see docs/GETTING_STARTED.md.)

What this skill does NOT do

• Does not manage money or execute trades
• Does not give investment advice
• Does not access user accounts or move funds
• Educational outputs only

Reporting issues

This skill describes the InvestorClaw service from a hermes operator's perspective. If a tool returns an unexpected result, the issue is in the upstream service (Apache 2.0, mnemos-os/mnemos-ic-runtime + argonautsystems/InvestorClaw), not in this SKILL.md. If hermes can't see the tools at all, that's an install issue — work through INSTALL.md and the troubleshooting section there.