Python: Support archive-type MCP skills (source, toolbox, sample)

python/packages/core/AGENTS.md

@@ -91,6 +91,7 @@ agent_framework/
 - **`SkillScriptRunner`** - Protocol for file-based script execution. Any callable matching `(skill, script, args) -> Any` satisfies it. Code-defined scripts do not use a runner.
 - **`SkillScriptArgumentParser`** - Public type alias for an optional callable `(raw args: dict | list[str] | str | None) -> dict | None` that converts the raw `args` value before an `InlineSkillScript` runs (applied before the inline list-args guard). It is an opt-in customization hook (port of .NET PR #6498) that lets callers support backends sending tool-call arguments in a non-conforming shape (e.g. vLLM JSON strings). The output is constrained to a `dict` (named keyword arguments) or `None`, because inline scripts bind arguments by keyword name. Supply it via the `argument_parser=` constructor arg on `InlineSkillScript`, `InlineSkill` (default for scripts added via `@skill.script`), or `ClassSkill` (default for scripts discovered via `@ClassSkill.script`). When `None` (the default), the raw value is used unchanged. File-based scripts are unaffected (their runner owns arg handling).
 - **`SkillsProvider`** - Context provider (extends `ContextProvider`) that discovers file-based skills from `SKILL.md` files and/or accepts code-defined `Skill` instances. Follows progressive disclosure: advertise → load → read resources / run scripts. By default all three tools it exposes (`load_skill`, `read_skill_resource`, `run_skill_script`) are registered with `approval_mode="always_require"`, so every skill operation needs approval. To run unattended, pass one of the static auto-approval rules to `ToolApprovalMiddleware` (via `auto_approval_rules`): `SkillsProvider.read_only_tools_auto_approval_rule` approves only the read-only tools (`load_skill`, `read_skill_resource`) while still prompting for `run_skill_script`, and `SkillsProvider.all_tools_auto_approval_rule` approves every skill tool including script execution. Both rules reject any call carrying a `server_label` so they stay scoped to this provider's local tools and never auto-approve a same-named hosted tool. Alternatively, for trusted skills, the constructor / `from_paths` kwargs `disable_load_skill_approval`, `disable_read_skill_resource_approval`, and `disable_run_skill_script_approval` (all default `False`) opt individual tools out of approval entirely by registering them with `approval_mode="never_require"` (the auto-approval rules only apply to tools that still require approval). The tool names are also exposed as class constants (`LOAD_SKILL_TOOL_NAME`, `READ_SKILL_RESOURCE_TOOL_NAME`, `RUN_SKILL_SCRIPT_TOOL_NAME`).
+- **`MCPSkillsSource`** - `SkillsSource` that discovers Agent Skills served over MCP by reading the well-known `skill://index.json` (SEP-2640). Index entries are dispatched by their `type` (case-insensitive): `skill-md` entries become one `MCPSkill` each (its `SKILL.md` body and sibling resources are fetched on demand via `resources/read`), and `archive` entries are downloaded as a single ZIP / TAR / gzip-TAR blob and unpacked **entirely in memory** (via the private `_ArchiveEntryLoader`) into a `FileSkill` whose `SKILL.md` body drives it and whose sibling files (matching the resource extensions, within the search depth) become in-memory `InlineSkillResource` resources. **Nothing is written to disk** — there are no temporary directories to create, own, or prune (this is a deliberate divergence from .NET, which extracts archives to disk; it removes the temp-dir leak and the dangerous prune-of-unowned-subdirs footgun). Entries whose type has no handler (e.g. `mcp-resource-template`) are skipped. **MCP-delivered scripts are never runnable**: the loader emits no `SkillScript`s, so a bundled script can at most surface as a readable resource (and only if it matches the resource extensions — `.py` is not a default resource extension). The archive `SKILL.md` frontmatter `name` must match the advertised index-entry `name` or the skill is skipped. Extraction is hardened: `_normalize_archive_member_name` rejects path-traversal ("zip-slip") member names, non-regular TAR members (links/devices) are skipped, and file-count / uncompressed-size (`_read_member_with_limit`) / download-size limits are enforced. Archive behavior is configured with `archive_*` constructor kwargs (`archive_resource_extensions`, `archive_resource_search_depth`, `archive_max_file_count`, `archive_max_size_bytes`, `archive_max_uncompressed_size_bytes`) — Python uses plain kwargs, not a `*Options` object as in .NET. A non-"resource not found" error while downloading an archive propagates (so a failed `CachingSkillsSource` refresh does not overwrite a cached list with a partial result). Unlike .NET's `AgentMcpSkillsSourceOptions.RefreshInterval`, this source has no built-in refresh interval; wrap it in `CachingSkillsSource(..., refresh_interval=...)` for caching/refresh. This is a port of .NET PR #6631; the `FileSkillsSource` `script_extensions`/`resource_extensions` kwargs default to the built-in tuples and treat `None` as "use defaults" and an empty tuple as "discover none" (an empty tuple previously fell back to defaults). `FoundryToolbox.as_skills_provider()` forwards matching `archive_*` kwargs to this source.
 - **`SkillsSource` decorators** - Skill sources are composable: `SkillsSource` is the abstract base, with concrete sources (`InMemorySkillsSource`, `FileSkillsSource`, `MCPSkillsSource`) and decorators that wrap an inner source — `AggregatingSkillsSource` (concatenate several sources), `FilteringSkillsSource` (predicate filter), `DeduplicatingSkillsSource` (first-wins by name), and `CachingSkillsSource` (cache the inner source's skills list). `DelegatingSkillsSource` is the abstract base for decorators. **`get_skills` takes a `SkillsSourceContext`**: every source/decorator implements `async def get_skills(self, context: SkillsSourceContext) -> list[Skill]` and forwards `context` to inner sources. `SkillsSourceContext` (frozen) carries the invoking `agent` (`SupportsAgentRun`) and optional `session` (`AgentSession | None`); `SkillsProvider` builds it from `before_run`'s `agent`/`session` and passes it into the pipeline. `FilteringSkillsSource`'s predicate is context-aware: `Callable[[Skill, SkillsSourceContext], bool]` (port of .NET #6797). **Default caching is applied only to the built-in, context-independent leaf sources**: for the `Skill` / sequence-of-skills / `from_paths` constructors, `SkillsProvider` builds `DeduplicatingSkillsSource(CachingSkillsSource(<file|in-memory leaf>))` so expensive filesystem/network discovery runs once. A **caller-supplied `SkillsSource` is used as-is — never auto-wrapped in caching or deduplication** — because auto-caching a context-aware caller source in a single shared bucket would replay the first invocation's skills for later `SkillsSourceContext`s and leak skills across agents/tenants (matches .NET, whose custom-source constructor also adds no caching/dedup). Callers who want caching on a custom pipeline compose `CachingSkillsSource(inner, cache_isolation_key_selector=...)` themselves. `disable_caching=True` only affects the built-in leaf caching (it has no effect on a caller-supplied source, which is never cached). `CachingSkillsSource` shares a single in-flight fetch across concurrent callers (per cache key) and does not update its cache on a failed fetch, so the next call retries (an initial failure leaves the cache empty; a refresh failure keeps the previously cached list). By default all callers share one cache bucket; pass `cache_isolation_key_selector=Callable[[SkillsSourceContext], str | None]` to cache separately per key (e.g. per agent name) for context-aware inner sources — the key should be low-cardinality and stable, and returning `None` (or leaving the selector `None`) uses the shared bucket. By default a cached list never expires; pass `refresh_interval=timedelta(...)` (port of .NET `CachingAgentSkillsSourceOptions.RefreshInterval`) to treat a cached list as stale once it is older than the interval so the next call re-queries the inner source (useful when an inner source such as `MCPSkillsSource` changes over the process lifetime; a zero/negative interval makes every result immediately stale, and a failed refresh keeps the prior list and retries). Freshness is measured with a monotonic clock (`time.monotonic()`). `SkillsProvider.__init__` / `from_paths` expose a `cache_refresh_interval` kwarg that is threaded into the built-in `CachingSkillsSource` (it has no effect on a caller-supplied source or when `disable_caching=True`). **`MCPSkillsSource` and `MCPSkill` accept exactly one of `client` (a fixed `ClientSession`) or `session_provider` (`Callable[[], ClientSession]`, resolved on every fetch); providing both/neither raises `ValueError`.** Use `session_provider` when the underlying session may be swapped over time — e.g. a reconnecting `MCPTool`/`FoundryToolbox` whose `session` is replaced on reconnect — so cached `MCPSkill`s keep fetching against the live session instead of a closed one (`MCPSkillsSource` forwards its provider to every `MCPSkill` it creates). A fixed `client` is safe only when the session outlives the skills.
 
 ### Model Context Protocol (`_mcp.py`)