Where scores appear

Open Agentic Foundation in the sidebar to see every connected repo in a list. The workspace header leads with N of M repos measured. The overall % is the platform average and includes unscanned repositories as 0%. Use Show unscanned on the table when you want only repos that have not been scanned yet.

Each row shows the overall score (0–100) and a color band after a real scan. Unscanned rows stay muted and show Not scanned instead of 0%. Click a repo name to open its detail view, where the same number sits at the top with a per-dimension breakdown underneath.

The overall score

Each repository has one overall readiness score from 0 to 100. It rolls up the nine dimension scores with extra weight on pillars that matter most for day-to-day agent work (especially Agentic Readiness and Test Coverage). It is not a simple average of the nine percentages.

Band	Range	What it usually means
Excellent	85–100	Strong docs, automation, and agent context.
Good	65–84	Solid base; a handful of targeted fixes left.
Fair	40–64	Meaningful gaps; check the Resolution Hub.
Needs attention	0–39	Structural friction; prioritize top items.

Example: payments-api might show 58 (Fair) while handbook shows 82 (Excellent). The API repo has fixable gaps; the docs repo simply has a different shape of criteria (many delivery rows are N/A).

Dimension scores

Below the overall score in the detail view, you see all nine dimensions. Each row includes:

Field	What it tells you
Score %	How well this repo meets that pillar’s criteria.
Confidence	How much evidence the scan found. Low confidence means thin or missing signals, not necessarily a bad codebase.
Status badge	Same band colors as the overall score (Excellent / Good / Fair / Needs attention).

Example: On web-checkout, Agentic Readiness might be 45% with low confidence because there is no .cursorignore and no agent instructions. Test Coverage might be 72% with high confidence because Jest files and a vitest.config.ts are easy to detect.

Reading the detail view

Click a repository to open three main areas.

Dimension breakdown

Each dimension expands to show individual criteria with a status:

Status	Meaning	Quick example
Met	Fully satisfied.	AGENTS.md lists npm test and npm run build.
Partial	Present but incomplete.	README says “run tests” without an exact command.
Gap	Missing expected signal.	No .github/workflows/ in a service repo.
N/A	Doesn’t apply here. Excluded from the dimension score.	No Dockerfile expected in handbook.
Blocked	Couldn’t verify at scan time.	K8s manifests live in acme-infra, not this repo.

Example: In payments-api → Agentic Readiness, you might see Gap on “Agent instructions” and Met on “Scriptable build & test” because the README has commands but there is no AGENTS.md.

In the Dimension Breakdown header you can also select Export remediations to download outstanding gaps (open and PR-pending) as Markdown (.md) or PDF (.pdf), grouped by dimension. Each item includes rubric metadata, agent-oriented fix instructions, and remediation hints. Export is complementary to in-platform Fix all and the AI Resolution Hub. It does not replace them.

How to use the exported remediation file

Use the download when you prefer to fix locally in your IDE (or with an AI coding assistant) instead of queueing automated fixes in the platform.

1. 1
   Download

   On the repo detail view, open Dimension Breakdown and click Export remediations. Choose Markdown (.md) or PDF (.pdf). Files are named like readiness-remediations-<repo>-<date>.md or .pdf.

2. 2
   Open the repo in your IDE

   Clone or open the same repository the scan scored (default branch unless you know you are fixing elsewhere).

3. 3
   Attach or paste the Markdown

   Drop the file into your agent chat, attach it as context, or paste the sections you care about. Markdown is best for coding agents; PDF is for sharing or printing. Each issue includes rubric metadata, agent instructions, and remediation hints. Ask the assistant to apply the remediations, or work through them yourself.

4. 4
   Apply what the file can change in code

   Most remediations are file or CI changes (docs, ignore files, workflows, specs). Those belong in a normal pull request you open from your IDE.

5. 5
   Handle settings outside the PR

   Some Continuous Integration governance items (G1–G5) need GitHub org/repo settings, Sonar linkage, or similar. A PR cannot fix those. Follow the in-app instructions from Fix it, or see Automated fixes and GitHub write access and the Contracts & governance patterns.

6. 6
   Merge and re-scan

   Merge code changes to the default branch, finish any settings steps, then Re-scan. Scores and hub rows update only after a completed scan. See Closing the loop.

Issues list

A flat list of every Gap, Partial, and Blocked item across all dimensions, sorted High → Medium → Low severity. Each line has a short description and a remediation hint. This is the same content the AI Resolution Hub pulls from, scoped to one repo.

Score history

After two or more completed scans, a trend chart shows how the overall score moved over time. Use it to confirm that a merged PR actually improved readiness.

Example: payments-api goes from 54 → 71 after adding AGENTS.md and a CI test job. The chart should show a step up on the scan date following that merge.

Example: reading one repo end to end

Walk through payments-api (Node/TypeScript API) as a new engineering lead would:

1. 1
   List view

   Overall 58 (Fair), last scanned yesterday. Agentic Readiness and Governance look like the weak pillars from the mini breakdown on the row.

2. 2
   Detail header

   Nine dimension tiles: Agentic Readiness 41% (low confidence), Test Coverage 68%, Delivery Readiness 75%, and so on.

3. 3
   Drill into Agentic Readiness

   Gap: no AGENTS.md. Partial: .cursor/rules/ exists but descriptions are empty. Met: README has npm test and npm run build.

4. 4
   Issues list

   Top item: “Add agent instructions” (High). Second: “CI does not run tests” (High). Jump to AI Resolution Hub to see the same items ranked against other repos.

When scores update

Scores refresh on every completed scan. Pushing a fix to GitHub does not change numbers until the next run finishes.

Trigger	What happens
Initial onboarding	First scan queues automatically for each visible repo.
Scheduled scan	About every 24 hours per connected repo.
Manual Re-scan	Click Re-scan on the repo row or Analyze from the list.
Failed scan	Previous score stays; the row shows failed status until you retry.

Typical wait: 5–15 minutes after you trigger a scan. Large monorepos can take longer.

Common questions

Why did only one dimension move?
Fixes are pillar-specific. Adding openapi.yaml lifts API & Contract Design; it won’t move Test Coverage unless you also wire tests.

Why did the overall score drop?
New Gap or Blocked rows appeared: a file was removed, GitHub governance checks failed, or the rubric now detects something that was invisible before. Check the issues list for what changed.

Why is overall “Good” but agents still struggle?
A single Gap in Agentic Readiness (no AGENTS.md, no ignore file) can hurt agents more than the headline number suggests. Read dimension scores, not just the total.

Next

• The 9 dimensions
• AI Resolution Hub
• Agentic Foundation overview