Track LLM token usage in reports

[HexSleeves]

Motivation

• Capture per-call token telemetry so report metadata can surface LLM token consumption and detect cost/usage trends.
• Preserve existing structured-output parsing while retaining raw provider metadata needed for usage (LangChain raw payload).

Description

• Add token counters to LLMCallRecord and llm_call_record() defaults: input_tokens, output_tokens, and total_tokens.
• Use with_structured_output(..., include_raw=True) and unwrap {"raw","parsed","parsing_error"} responses, extracting usage from raw.usage_metadata (supports input_tokens/output_tokens and prompt_tokens/completion_tokens variants) and preserving parsing-error behavior.
• Record token usage in both sync (run_batches) and async (arun_batches) paths (including non-structured raw responses), and propagate analyzer.llm_usage into node-level llm_call_log entries (success and failure).
• Aggregate llm_call_log token counters into report JSON metadata under metadata.llm_usage while keeping existing metadata intact.
• Add support in the CLI structured-output adapter for include_raw=True so local/CLI providers expose raw usage without changing parser expectations.
• Add unit tests covering the new fields, sync/async usage capture, missing usage metadata handling, structured-output include_raw=True parsing, and metadata aggregation; closes feat: expose LLM token usage in JSON report output #242.

Testing

• Ran ruff format . and ruff check . with no issues.
• Ran the full test suite with pytest -q: all tests passed (1261 passed, 12 skipped, 34 deselected, 6 xfailed).
• mypy src was executed and reported pre-existing typing issues in unrelated modules; no new type regressions introduced by these changes (mypy warnings are repository pre-existing and not caused by this PR).

[rng1995]

[Automated SkillSpector Review]

Requesting changes. Usage capture works for LLMAnalyzerBase, but the report still undercounts the real TP4 graph LLM call. Instrument the direct chat_completion() path and add a regression asserting provider usage is included in metadata.llm_usage.

[rng1995]

Blocking: this expectation bakes in zero tokens for TP4 even though production mcp_tool_poisoning.py calls chat_completion() directly and records a successful LLM call. That path bypasses the new raw-response usage extraction, so report totals are incomplete. Instrument chat_completion/TP4 and assert the provider's nonzero usage metadata here.

[HexSleeves]

Fixed in 8b45e81. chat_completion_with_usage() now preserves normalized provider token metadata, TP4 forwards it into llm_call_log (including parse failures), and the regression verifies the nonzero TP4 usage reaches metadata.llm_usage. Verification: Ruff clean; 1262 tests passed.