📦 deps(thirdparty): update snapshots
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# Skills Invocations
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How each tool activates and invokes skills.
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## Invocation Patterns Overview
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| Tool | Pattern | Description |
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| ------ | --------- | ------------- |
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| Claude Code | Model-invoked | Agent autonomously decides based on request + description |
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| Claude (claude.ai) | Auto + Model | Pre-built skills auto-activate; custom skills when relevant |
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| GitHub Copilot | Model-invoked | Based on prompt + skill description |
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| VS Code (Copilot) | Model-invoked | Auto-activates, follows progressive disclosure |
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| OpenAI Codex | Explicit + Implicit | `/skills` command or `$skill` mentions, or model decides |
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| Cursor | Model-invoked | Agent determines relevance automatically |
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| Amp | Lazy-loaded | On-demand loading when relevant |
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| Letta | Tool-based | Agent calls `Skill` tool to load into memory |
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| Goose | Model-invoked | Loads skills, accesses files via file tools |
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| OpenCode | Tool-based | Skills registered as dynamic tools via plugin |
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## Detailed Invocation Methods
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### Claude Code
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**Type:** Model-invoked (autonomous)
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Claude autonomously decides to use skills based on:
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- Current request context
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- Skill `name` and `description` from frontmatter
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**Contrast with slash commands:**
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- Skills = model-invoked (agent decides)
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- Slash commands = user-invoked (explicit)
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---
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### Claude (claude.ai)
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**Type:** Automatic + Model-invoked
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- **Pre-built skills** (document actions): Activate automatically
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- **Custom skills**: Load when model determines relevance
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---
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### GitHub Copilot
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**Type:** Model-invoked
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Copilot decides activation based on:
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- User's prompt content
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- Skill `description` field
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When activated:
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- `SKILL.md` content injected into agent context
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---
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### VS Code (Copilot)
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**Type:** Model-invoked (auto-activation)
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- No manual skill selection required
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- Follows progressive disclosure pattern
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- Model determines when skills are relevant
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---
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### OpenAI Codex
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**Type:** Explicit + Implicit
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**Explicit invocation:**
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- `/skills` slash command — Opens skill selector
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- `$<skill-name>` mention — Reference specific skill in prompt (e.g., `$plan`, `$skill-creator`)
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**Implicit invocation:**
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- Codex decides based on skill descriptions
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- Automatic activation when task matches skill description
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**Surface support:**
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- CLI and IDE extensions support explicit invocation
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- Web and iOS don't support explicit invocation yet (but can prompt Codex to use repo skills)
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**Built-in skills:**
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- `$plan` — Research and create implementation plans
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- `$skill-creator` — Bootstrap new skills
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- `$skill-installer` — Download skills from GitHub
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---
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### Cursor
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**Type:** Model-invoked ("agent-decided rules")
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- Agent determines relevance automatically
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- No manual intervention required
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- Skills applied without user selection
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**Constraint:** Skills cannot be configured as "always apply" or manually invoked — agent-decided only.
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---
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### Amp
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**Type:** Lazy-loaded
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- Skills loaded on-demand when relevant
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- Described as "lazy-loaded instructions"
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- No explicit invocation required
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---
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### Letta (Letta Code)
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**Type:** Tool-based
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**Model invocation:**
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- Agent calls the **`Skill` tool** to load skills into memory
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- Agent decides when to load based on context
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- Skill tool commands: `load`, `unload`, `refresh`
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**Explicit invocation:**
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- Prompt: "Use the testing skill..." to force specific skill
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- `/skill` command: Extract new skill from recent work
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**Memory integration (Two Blocks):**
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- **`skills` block**: Always visible — list of available skills (names + descriptions)
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- **`loaded_skills` block**: Session state — full content of currently loaded skills
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- Both blocks are read-only (modified only via Skill tool)
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**Alternative access:**
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- Can read `.skills/<name>/SKILL.md` directly for one-time preview (without loading)
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---
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### Goose
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**Type:** Model-invoked
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- Loads skills when relevant
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- Accesses supporting files via file tools
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- Treats skills as filesystem resources
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---
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### OpenCode
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**Type:** Tool-based
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- `opencode-skills` plugin registers skills as **dynamic tools**
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- Skills become tool-like affordances
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- Agent invokes skills as it would any other tool
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## Invocation Pattern Comparison
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### Model-Invoked (Autonomous)
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The agent decides when to use skills without explicit user action.
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**Pros:**
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- Seamless user experience
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- Agent can combine skills as needed
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- No learning curve for users
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**Cons:**
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- Less predictable
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- May miss relevant skills
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- User has less control
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**Tools:** Claude Code, Claude, GitHub Copilot, VS Code, Cursor, Amp, Goose
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### Explicit Invocation
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User directly requests skill usage via commands or mentions.
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**Pros:**
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- Predictable behavior
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- User maintains control
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- Clear audit trail
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**Cons:**
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- Requires user to know available skills
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- More friction
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- May miss opportunities
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**Tools:** OpenAI Codex (`$skill`, `/skills`)
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### Tool-Based
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Skills are exposed as tools the agent can call programmatically.
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**Pros:**
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- Fits existing tool-use patterns
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- Clear invocation semantics
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- Integrates with agent memory
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**Cons:**
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- Requires tool infrastructure
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- More complex implementation
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**Tools:** Letta, OpenCode
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## Progressive Disclosure in Invocation
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Most tools follow a staged loading pattern:
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```text
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┌─────────────────────────────────────────────────────────────┐
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│ Stage 1: Index │
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│ Load: name, description │
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│ When: Startup / cache refresh │
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├─────────────────────────────────────────────────────────────┤
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│ Stage 2: Activate │
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│ Load: Full SKILL.md body │
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│ When: Agent decides skill is relevant │
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├─────────────────────────────────────────────────────────────┤
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│ Stage 3: Execute │
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│ Load: scripts/, references/, assets/ │
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│ When: Skill instructions reference them │
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└─────────────────────────────────────────────────────────────┘
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```
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This minimizes context usage while maintaining full capability access.
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