Methods
A method is a learned workflow. When an agent finishes a multi-step task, it can save the sequence of tool calls as a YAML body that future tasks reuse. Methods sit alongside Skills and Tools in the same RAG index — the preflight search picks all three together.
This page is the developer reference: what's in a method, how scoring works, where they're stored, and how the auto-selection mechanics around methods/tools/skills are tuned.
What's in a method
| Property | Description |
|---|---|
name | Display name |
description | Brief description (used by RAG search) |
triggerText | Optional phrases that activate this method ("deploy to staging", "publish post") |
body | The YAML workflow — step-by-step tool calls with logic between them |
toolsUsed / skillsUsed | Auto-extracted from body so loading the method auto-loads its dependencies |
tokenCount | Estimated cost for context budgeting |
version | Bumped on every edit |
Loading a method automatically loads its referenced tools and skills. So an agent that starts with research skills can pull in a "deploy to staging" method (and the tools that method uses) without you doing anything.
Scoring
Methods are scored using a recency-weighted success rate:
score = successRate × recencyWeight
recencyWeight = 1.0 / (1.0 + daysSinceUsed / 30.0)
Each time a method is used, the system records a MethodEvent (loaded, succeeded, failed) and recalculates the score. High-quality, recently-used methods rank higher in search results — so the workflows that actually work float to the top automatically.
Auto-selection mechanics (preflight search)
Before every chat message, a preflight RAG search runs across all enabled skills, methods, and tools. It uses hybrid BM25 + vector matching to find items relevant to the user's query, then injects matching skill instructions and method bodies into the system prompt.
The search runs through the configured Core Model (Settings → General → Core Model), and falls back to the active chat model when Core Model is unset. A small fast Core Model (foundation on macOS 26+, or gemma-4-e2b-it-4bit) keeps preflight cheap.
Search width controls how aggressively it pulls candidates per index:
| Mode | Methods | Tools | Skills | Best for |
|---|---|---|---|---|
off | 0 | 0 | 0 | Disabling auto-selection entirely |
narrow | 1 | 2 | 1 | Fastest responses, minimal context |
balanced (default) | 3 | 5 | 2 | Most cases — good coverage at moderate cost |
wide | 5 | 8 | 4 | Maximum coverage, larger prompts |
Set the mode in Management → Settings → Capabilities.
Mid-conversation discovery
The agent can also expand its kit while a chat is in progress via two always-on tools:
| Tool | What it does |
|---|---|
capabilities_search | Search methods, tools, and skills across all indexes in parallel |
capabilities_load | Load a specific capability by ID into the active session |
Loading a method through capabilities_load automatically loads its referenced tools and skills.
Storage
Methods live in ~/.osaurus/methods/methods.sqlite (encrypted with SQLCipher since 0.17.7).
Browsing methods
There isn't a dedicated "Methods" tab in the app — methods live alongside skills in the same RAG index. To inspect what's been learned:
- Management → Insights shows when methods were loaded and whether they succeeded or failed
- The methods database is browsable via SQLite tools if you really need to dig in
Skills, Methods, Tools — the comparison
| Skills | Methods | Tools | |
|---|---|---|---|
| Source | You author them or import from a marketplace | Agents save them after successful runs | Built-in plugins, native plugins, MCP providers |
| Content | Markdown instructions + reference files | YAML sequences of tool calls | Code (Swift, Rust, Python via MCP) |
| What they do | Add domain knowledge / methodology | Replay a known-good workflow | Take action (read files, run commands, call APIs) |
| Loaded by | RAG search (preflight + on-demand) | RAG search (preflight + on-demand) | RAG search; loading a method auto-loads its tools |
| Token cost | The skill's instructions text | The method's YAML body | Just the tool's spec (description + parameters) |
Related:
- Skills — user-facing skills documentation
- Tools & Plugins — what tools exist and how they're built
- Agents — capabilities are scoped per agent