/**
 * Token metric contract shared by analytics_summary, analytics_models and analytics_timeseries.
 *
 * observed_tokens = input + cache_write + output: every token the model processed that was not a
 * cache hit. Adapters store input without cache reads (Codex subtracts cached_input_tokens from the
 * OpenAI input); Anthropic reports newly cached prompt tokens as cache_creation_input_tokens instead
 * of input_tokens, so cache writes are counted to keep Claude comparable with Codex, whose uncached
 * input already contains the tokens it caches. Cache reads and reasoning stay separate.
 *
 * Token metrics measure consumption, so they cover main sessions, subagent sessions (with or
 * without user messages) and `task` containers (usage imported without a local conversation, e.g.
 * unmatched Cursor dashboard export events); session and message KPIs keep the main-session scope. Sessions of
 * unknown kind and main rows without user messages (legacy telemetry-only or token-only groups,
 * possibly ghost projections of tokens already counted elsewhere) stay technical for tokens too.
 */
export declare function observedTokensSql(alias?: string): string;
/** SQL predicate of the non-main sessions whose tokens are counted. */
export declare function tokenOnlySessionSql(alias?: string): string;
/** SQL predicate of the sessions whose tokens are counted: KPI main sessions, subagents, task containers. */
export declare function tokenSessionSql(alias?: string): string;
/** SQL predicate of the main-session KPI scope used by session and message counts. */
export declare function mainSessionSql(alias?: string): string;
export declare const OBSERVED_TOKENS_NOTE = "observed_tokens = input_tokens + cache_write_tokens + output_tokens (tokens not served from cache); cache reads and reasoning are reported separately, never estimated";
export declare const TOKEN_SCOPE_NOTE = "token metrics cover main, subagent and task (imported usage) sessions; session and message counts cover main sessions only";