# Algorithm Watch

Algorithm Watch monitors known and observed platform signals across X, Instagram, and Facebook. When platforms shift what they reward, your strategy needs to shift with them; Algorithm Watch shortens the gap without turning any one signal into a mandate.

### Tabs

Algorithm Watch has three tabs:

* **Signals** - source list, review reminders, and the status of algorithm context flowing into Content Forge.
* **Algorithm Updates** - logged platform changes with summaries, actionable notes, suggested tests, confidence, and source URLs.
* **Current Events** - read-only RSS items that the ForgeSays pipeline uses as current-event context.

### Signals

Signal sources can be global or org-specific. Org-specific sources can be added for web pages, RSS feeds, GitHub repos, or APIs, then assigned to X, Instagram, Facebook, TikTok, LinkedIn, or all platforms.

Sources are reviewed manually on a two-week cadence. Mark a source as reviewed after you check it. Sources and logged updates are used as advisory context in Content Forge, but Brand Voice, factual accuracy, and your org identity take priority.

### Algorithm Updates

Use Algorithm Updates to record meaningful distribution changes you want the team to remember. Each update can include:

* Platform and surface
* Summary of the change
* Actionable note
* Suggested test
* Confidence level
* Source URL

Updates help connect observed platform changes to content decisions and [Experiments](/thecontentforge-docs/feature-guides/experiments.md).

### Current Events

The Current Events tab is a read-only view of the rolling RSS store. It lets you browse items that are available as grounding context for ForgeSays, filter by source or time range, and copy a current-events context packet when useful.

Items appear there when matching RSS content is ingested by the ForgeSays pipeline.

### Workflow

1. Check **Signals** weekly, or when something feels off in reach
2. Review due sources and mark them reviewed when checked
3. Add an **Algorithm Update** when you spot a meaningful platform change
4. Cross-reference [Patterns](/thecontentforge-docs/feature-guides/patterns.md) to see whether your own performance shifted
5. Test the response with an [Experiment](/thecontentforge-docs/feature-guides/experiments.md) so you have evidence, not assumption

### Tips

* Algorithm changes are noisy. Do not overhaul your strategy on a single signal
* Treat algorithm context as advisory input, not an instruction to override Brand Voice or accuracy
* Cross-reference Algorithm Watch with Patterns; your own data is the final word on whether a change is hitting you


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