What does this skill do, and when should you use it?
Profile Agent is one of 14 skills bundled in the olaxbt/ai-market-maker (AIMM) repository, located at src/agents/profile_agent/SKILL.md. It reads a questionnaire covering risk tolerance, time horizon, preferred signals, leverage comfort, and asset scope, then runs a built-in rule matrix to produce a profile: weight deltas, the resulting effective weights, and an explanatory narrative. In rule-based mode it makes no external API calls at all; an optional LLM client (Hermes/OpenAI) enables an enhanced mode. It does not place trades or fetch market data — its only job is translating 'what kind of trader are you' into parameters the system can use.
Accepts a structured questionnaire (POST /profile-agent with fields risk_tolerance, time_horizon, preferred_signals, leverage_comfort, assets); runs the built-in rule matrix to compute weight deltas for each risk/horizon/signal combination; returns profile JSON containing deltas, effective weights, and a narrative; supports direct natural-language queries such as 'Generate a profile for a conservative swing trader'; optionally uses llm/openai_client.py for LLM-enhanced mode, with zero external calls in rule-based mode.
- A crypto trader onboarding into the AIMM system who wants initial weight configuration matching their risk tolerance and swing/intraday preferences
- A quant team onboarding new users or strategies, batch-generating profiles at different risk tiers via the questionnaire endpoint
- A trading educator or community operator demonstrating how 'aggressive scalper vs conservative swing trader' profiles differ in weights
- A developer wiring the /profile-agent endpoint into their own agent pipeline to convert user preferences into downstream-consumable JSON
- Users who prefer not to send questionnaire data to third-party APIs, choosing pure rule-based mode with no external calls
How do you install this skill?
- This is a static review only; no code was executed, and no committed test covers the profile agent's key paths.
- The rule matrix is not shown, so delta computation and narrative labeling cannot be independently verified.
- LLM-enhanced mode depends on overseas APIs (OpenAI/Atlas Cloud) that may be unreachable from mainland-China networks; the rule-based path is unaffected.
- Publisher is not verified by the FollowSkills registry; identity is unknown, verify repository maintenance activity yourself.
- profile_id hashes input + source + timestamp; exact input scope is undisclosed, so treat potential questionnaire data as privacy-relevant.
Optional: OpenAI-compatible LLM client for LLM-enhanced mode
The skill ships inside the AIMM repository: git clone https://github.com/olaxbt/ai-market-maker.git, then per the README run pip install uv && uv sync --extra dev (repo-level setup, Python 3.11+ required). The skill file itself lives at src/agents/profile_agent/SKILL.md. For OpenClaw: claw install https://github.com/olaxbt/ai-market-maker. Note: the README only documents installation of the whole collection, not standalone installation of this individual sub-skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/olaxbt/ai-market-maker.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/src/agents/profile_agent" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Generate a profile for a conservative swing trader
- What weights would an aggressive scalper get?
Two entry points: 1) HTTP query — POST /profile-agent with body {"risk_tolerance": "moderate", "time_horizon": "swing", "preferred_signals": "technical", "leverage_comfort": "1-3x", "assets": "majors_only"}; the response is profile JSON with deltas and narrative. 2) Direct prompt — ask the agent 'Generate a profile for a conservative swing trader' or 'What weights would an aggressive scalper get?'. The core call is process(questionnaire). For LLM-enhanced mode configure llm/openai_client.py; otherwise rule-based mode works out of the box.
What are this skill's strengths and limitations?
- Rule-based mode has zero external dependencies and zero API calls — fast and reproducible offline
- Structured output (deltas + effective weights + narrative) is directly consumable downstream yet human-readable
- Both structured questionnaire and natural-language entry points lower the barrier to use
- LLM enhancement is optional; the core function works fully without an OpenAI dependency
- The actual weighting logic of the rule matrix is not documented, so profile quality cannot be independently assessed
- It only generates profiles/parameters; there is no backtesting of the produced weights and no documented evidence linking them to performance
- As a sub-skill of the AIMM monorepo, standalone deployment outside its Python environment is not documented
- Behavior and failure modes of LLM-enhanced mode are not described
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Profile Agent — Trader Profiler this page | 47 · Use with care | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Risk Guard | 44 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
| AIMM Portfolio Management Skill | 40 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Lighter Agent Kit | 57 · Use with care | ★ 67 | 2mo ago | MIT |
| Bybit AI Trading Skill | 51 · Use with care | ★ 83 | 12d ago | MIT |
Neither SKILL.md nor the README compares this skill to any specific alternative. The README notes the broader system is orchestrated with LangGraph and that all agents follow a uniform Input → Process → Output → Feedback contract, but no direct competitor is named.
How did FollowSkills review this skill?
SKILL.md declares no external API calls in rule-based mode with optional LLM client, so data-flow disclosure is basic but present; however there is no least-privilege statement, no user confirmation mechanism, no rollback/recovery, and no detail on what inputs feed the SHA-256 profile_id. Deducted for incomplete permissions, confirmation, isolation and recovery.
persona.md specifies a deterministic rule path with delta bounds [-0.15,+0.15] and re-normalization, which is self-consistent; but no committed test covers the profile agent's key paths, and error handling or failure feedback for abnormal questionnaire inputs is absent. Static cap is 10; deducted for untested paths and thin failure modes.
The 5-question schema, POST input format and output JSON are clearly specified and the onboarding scenario is well defined; but non-fit boundaries are not declared, there is no Chinese-language support statement, and optional LLM enhancement depends on overseas providers (OpenAI/Atlas Cloud), a reachability concern for mainland-China users. Deducted for boundaries and environment fit.
SKILL.md is concise and readable with explicit optional dependency notes; but the skill has no version, changelog, FAQ, known-limitation disclosure, or stated maintenance/update responsibility at the skill level. Deducted for governance gaps.
Claims directly usable profile JSON (deltas + effective weights + narrative) from a deterministic rule path, which carries real marginal value for onboarding; but no representative output is verifiable from the source, and the underlying rule matrix is not shown, so output correctness cannot be checked. Static cap is 7; deducted for unverifiable output.
The repository has a CI workflow and test suite, but none targeting the profile agent's key paths; the rule matrix and delta computations are author claims with no independent reproduction evidence. Static cap is 5; deducted for thin coverage.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →