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Skill Evolution

Agents autonomously learn, test, and deploy new skills through a 42-module native evolution system — no external dependencies, no CLI wrappers, no AGPL risks.

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How It Works

The evolution engine follows a Retrieve-Before-Generate strategy: before creating new solutions, it searches existing high-confidence fixes. This alone eliminates redundant LLM calls.

1. Discovery — Agent identifies a recurring task pattern or detects a failure signal
2. Evidence Aggregation — Collects success and failure cases across multiple executions
3. Variant Generation — Creates 3 candidate variants in parallel (cost-efficient vs competitors’ 50-500 LLM calls)
4. Scoring — LLM-as-Judge evaluation with four dimensions (Function / Quality / Safety / Compatibility)
5. Approval — GUI-based human approval workflow with diff preview
6. Deployment — A/B tested with automatic rollback on regression

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Four Evolution Types

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5-Layer Safety

Every evolved skill passes through five independent safety gates before reaching production:

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Frustration Detection

The system detects user dissatisfaction through 5 categories and 38 bilingual patterns (Chinese + English), triggering DERIVED evolution without explicit user feedback:

• Verbosity — “just give me the answer”
• Style — “be more concise”
• Format — “use a table instead”
• Workflow — “stop doing X first”
• General — frustration expressions

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Evidence-Driven Evolution

Unlike competitors that evolve from single failure signals, Myrm aggregates evidence across multiple executions:

• Success cases — preserved to prevent regressions
• Failure cases — analyzed for root cause patterns
• Minimum evidence threshold — requires at least 3 executions and 1 failure before triggering evolution

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Quality Monitoring

3-dimensional degradation detection with sliding window statistics:

• Success rate monitoring with configurable threshold (default: 70%)
• P95 latency tracking with automatic alerting
• Server error rate (5xx) monitoring

Skills that degrade are automatically isolated via 1-Strike (critical failure) or 3-Strikes (gradual degradation) policies.

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Stop-Loss: Versions, Shadow A/B, and Batch Snapshots

Plain language: If an evolved skill makes your agent worse, you can roll back in Settings — no CLI. You can also approve changes in Shadow mode first (production behavior stays on the old version while we compare in the background), then promote when metrics look good. Batch-optimizing many skills saves a snapshot before it starts; you can cancel and roll back mid-run if results go sideways. vs competitors: Hermes/OpenClaw offer CLI or file-based rollback; none ship a full GUI loop for shadow validation plus batch mid-flight rollback.

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Curator: Lifecycle Governance

Automated skill lifecycle management through the Curator system:

• Cluster Detection — Identifies semantically similar skills via prefix + embedding analysis
• Umbrella Merge — GUI preview before merging, preventing accidental consolidation
• Automatic Pruning — Removes inactive and low-quality skills
• History Visualization — Timeline view of all evolution events in the GUI

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Skill Library

24+ prebuilt skills available out of the box, with multi-source marketplace integration.

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Toolset-Aware Skills

Skills automatically adapt to your deployment environment. Each skill can declare which tools or tool groups it requires:

• requires_tools / requires_tool_groups — skill only appears when specific tools are available
• fallback_for_tools / fallback_for_tool_groups — skill auto-activates as a tutorial/workaround when a native tool is missing

8 standardized tool groups (web, browser, file_ops, shell, computer_use, memory, kanban, wiki) ensure consistent behavior across Local WebUI, Tauri Desktop, and SaaS Cloud deployments.

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Zero-Roundtrip Skill Injection

When you explicitly invoke a skill (e.g., “use code-review”), Myrm injects the full SOP directly into context — no extra LLM tool call needed. This saves 2-5 seconds and 500-2000 tokens per invocation compared to the traditional “request → LLM calls select tool → load SOP” flow. The injected payload includes:

• Full SOP content with ${SKILL_DIR} template variables resolved
• [Skill directory: /path/to/skill] for file access
• Auxiliary file listing (scripts, references, templates)
• [IMPORTANT: The user has invoked...] strong-signal header for model compliance

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Three-Way Hash Protection

When Myrm upgrades bundled prebuilt skills, user customizations are never silently overwritten: Under the hood, origin_hash (SHA-256 of the bundled source at last sync) is compared against the current stored content hash. If they differ, the user has customized the skill and the upgrade is deferred — not forced. This solves a common problem with prebuilt/template systems: users who tweak defaults lose their changes on every update.

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Growth Dashboard

Track your agent’s learning progress visually:

• KPI summary (total skills, success rate, evolution count)
• Heat map of skill usage patterns
• Radar chart of capability coverage
• Weekly statistics and trend analysis
• Skill evolution timeline

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Daily Work Journal

The Daily Journal tab provides a consolidated view of everything your agent did on any given day:

• Overview metrics — sessions, tokens, cost, tool calls, approvals, cron runs, kanban events
• Source breakdown — sessions grouped by origin channel (Web UI, Telegram, API, etc.)
• Unified timeline — all events (sessions, approvals, cron runs, kanban events) sorted chronologically
• Date navigation — browse any past day with a date picker
• Agent filtering — filter by specific agent when running multiple agents

Zero new storage is required — the journal aggregates data from 6 existing sources (Chat, Message, ApprovalRecord, CronRunModel, KanbanTaskEventModel, EventLog) in real time.

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Multi-Agent Skill Scoping & Sharing

In a multi-agent sandbox, skills are perfectly isolated yet securely shareable.

• Scope Isolation: Skills natively belong to the agent that learned them. They don’t pollute a global pool like in OpenClaw or Hermes.
• Cross-Agent Mounting: Users can mount a skill from Agent A to Agent B with a single click in the GUI, complete with a visual origin badge.
• Copy-on-Write (CoW) Forking: If Agent B evolves a skill mounted from Agent A, Myrm automatically forks a localized variant for Agent B. Agent A’s original skill remains untouched and pristine.
• 1-Click Rollback: Undo an evolution on a CoW fork, and the system intelligently restores the cross-agent mount mapping, retaining complete semantic history.
• Robust Garbage Collection: Deleting an agent cascade-deletes its exclusive skills (both database records and physical rmtree wipe with path boundary protection), leaving zero orphan data or “ghost” skills.

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Compared to Alternatives

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vs Hermes Ecosystem Plugins

Hermes requires 5 separate third-party plugins to approximate what Myrm provides natively: Key advantages of Myrm’s native approach:

• No dependency fragmentation — a single product vs 5 separate repos with different maintainers, licenses, and update cycles
• Deeper integration — evolution system talks directly to security, context management, and GUI layers
• GUI-first experience — every feature has visual management vs CLI-only tooling
• Production-grade safety — 5-layer evolution safety + 6-layer platform security vs ad-hoc checks