[docs] Update glossary - weekly full scan

docs/src/content/docs/reference/glossary.md

@@ -388,6 +388,16 @@ Optional workflow metadata for categorization and organization. Enables filterin
 
 A short human-friendly name (such as `sonnet` or `mini`) that gh-aw resolves to the best available concrete model at compile time. Aliases are defined as ordered lists of provider-scoped glob patterns; the first pattern that matches an available model wins. Meta-aliases reference other aliases and are resolved recursively. Built-in vendor aliases and meta-aliases are listed in the [Model Aliases & Multipliers Reference](/gh-aw/reference/model-tables/). Custom aliases can be defined in workflow frontmatter using the [Model Alias Format Specification](/gh-aw/reference/model-alias-specification/).
 
+### Max Runs (`max-runs`)
+
+A top-level frontmatter field that caps the number of times the AWF proxy will invoke the AI engine within a single workflow run. Applies to all engines and maps to `apiProxy.maxRuns` in the compiled lock file. Replaces the deprecated `engine.max-runs` field. Example:
+
+```aw wrap
+max-runs: 10
+```
+
+See [Engines Reference](/gh-aw/reference/engines/).
+
 ### Network Permissions
 
 Controls over external domains and services a workflow can access. Configured via `network:` section with options: `defaults` (common infrastructure), custom allow-lists, or `{}` (no access).
@@ -428,6 +438,19 @@ A plain GitHub Actions workflow (`.yml`) that separates trigger definitions from
 
 Trigger files can live in the **same repository** as the orchestrator or in a **different repository** (cross-repo `workflow_call`). Cross-repo usage requires the callee repository to be public, internal, or to have explicitly granted Actions access. When using `secrets: inherit`, the caller's secrets are passed through — including `COPILOT_GITHUB_TOKEN`, which must be configured in the caller's repository. See [CentralRepoOps](/gh-aw/patterns/central-repo-ops/).
 
+### User Rate Limit (`user-rate-limit`)
+
+A frontmatter field that prevents individual users from triggering a workflow too frequently. Configured with `max-runs-per-window` (maximum runs per time window, 1–10), an optional `window` in minutes (default 60, max 180), an optional `events` list to restrict which trigger types count, and an optional `ignored-roles` list of exempt roles (default: `[admin, maintain, write]`). The pre-activation job checks recent runs and cancels the current run if the limit is exceeded. Example:
+
+```aw wrap
+user-rate-limit:
+  max-runs-per-window: 5
+  window: 60
+  ignored-roles: []
+```
+
+See [Rate Limiting Controls](/gh-aw/reference/rate-limiting-controls/).
+
 ### Weekday Schedules
 
 Scheduled workflows configured to run only Monday through Friday using `daily on weekdays` syntax. Recommended for daily workflows to avoid the "Monday wall of work" where tasks accumulate over weekends and create a backlog on Monday morning. The compiler converts this to cron expressions with `1-5` in the day-of-week field. Example: `schedule: daily on weekdays` generates a cron like `43 5 * * 1-5`.
@@ -512,6 +535,10 @@ A feature of `gh aw logs` that aggregates firewall, MCP, and metrics data across
 
 A weighted token count that normalizes raw API token usage into a single comparable value for cost estimation and monitoring. Computed by applying cache and output multipliers to each token category (input, output, cache read, cache write) and summing the results. Appears in audit reports, `gh aw logs` output, and safe-output message footers (as `{effective_tokens}` and `{effective_tokens_formatted}`). For episode-level aggregation, `total_estimated_cost` uses effective tokens as its basis. See [Effective Tokens Specification](/gh-aw/reference/effective-tokens-specification/).
 
+### Forecast (`gh aw forecast`)
+
+An experimental CLI command that projects future Effective Token consumption using a Monte Carlo simulation. It samples historical workflow runs, applies a Poisson-bootstrap algorithm to model run frequency, and returns P10/P50/P90 percentile estimates over a configurable time horizon. Supports both local (`.github/workflows/`) and remote (`--repo`) discovery modes. Output is available as a console table or machine-readable JSON (`--json`). Useful for capacity planning, budget governance, and detecting cost regressions before they occur. See [Forecast Specification](/gh-aw/reference/forecast-specification/).
+
 ### Time Between Turns (TBT)
 
 The elapsed time between consecutive LLM API calls in an agentic workflow run. A "turn" is one complete LLM inference request; TBT measures the gap from when the model finishes one response (and tool calls are dispatched) to when the next request is sent (after all tool results are collected). TBT is an important performance and cost metric because LLM inference providers implement prompt caching with a fixed TTL: