---
schema_version: "newruntime-agent-readable-v0.2"
type: "project_build_brief"
stable_id: "project_build_brief:github-projects-for-ai-engineers"
project_slug: "github-projects-for-ai-engineers"
project_title: "GitHub Projects for AI Engineers"
buildability_class: "B2"
verification_status: "locally-reproduced"
proof_artifact_count: 6
buildability_axis_count: 7
replay_evidence_count: 2
project_status: "candidate"
last_verified: "2026-08-15"
record_date: "2026-08-15"
date_kind: "last_verified"
routes: {"html":"https://newruntime.com/projects/github-projects-for-ai-engineers/","project_markdown":"https://newruntime.com/projects/github-projects-for-ai-engineers.md","project_json":"https://newruntime.com/projects/github-projects-for-ai-engineers.json","acceptance":"https://newruntime.com/projects/github-projects-for-ai-engineers/acceptance.md"}
---

# Build brief: GitHub Projects for AI Engineers

## Desired outcome

Turn the Telegram export and two public ecosystem references into a ranked repository radar and a navigable map of the wider production AI stack.

## Wow moment

One HTML or JSON route now exposes 140 ranked repositories plus 98 official tool links across 13 capability lanes and nine production-depth levels.

## Known inputs

- Local Telegram export repository-link roots
- Public GitHub repository URLs
- Current GitHub stars, forks, archived state, and canonical repository names
- New Runtime topic taxonomy and project theme lanes

## Available tools

- Local data extraction
- GitHub public web/API reads
- Repository normalization script
- Astro content validation

## Proof status

- Verification status: locally-reproduced.
- Last verified: 2026-08-15.
- Expected artifacts: 4.
- Provider scopes: 3.

## Fixture outputs

- Structured radar dataset: dist/projects/github-projects-for-ai-engineers.json (https://newruntime.com/projects/github-projects-for-ai-engineers.json). Check: The JSON route exposes the ranked repository dataset, proof contract, handoff routes, and public GitHub source URLs.
- Human radar page: dist/projects/github-projects-for-ai-engineers/index.html (https://newruntime.com/projects/github-projects-for-ai-engineers/). Check: The HTML radar renders the same project count, theme lanes, and machine-readable handoff links as the dataset.
- Agent build brief: dist/projects/github-projects-for-ai-engineers/build-brief.md (https://newruntime.com/projects/github-projects-for-ai-engineers/build-brief.md). Check: The build brief names the data source, GitHub metric refresh boundary, privacy constraints, and acceptance checks.
- Clean-room replay fixture: scripts/project-proof-depth-v2-fixture-test.mjs. Check: The fixture replays the bounded deterministic-ranking task in a temporary directory, proves the initial failure of the wrong sort order, proves the final pass of the stars-then-forks rule, and verifies that an unrelated file is unchanged.
- Independent replay kit: scripts/project-proof-independent-replay-kit.mjs. Check: The kit starts with a ranking function that violates the published sort rule, exposes no ready solution, protects every out-of-scope file by checksum, and refuses to emit a receipt without a non-author attestation and a passing final check.
- Independent agent replay receipt: src/data/project-proof-receipts/github-projects-for-ai-engineers-independent-agent-v1.json (https://newruntime.com/projects/github-projects-for-ai-engineers.json). Check: A separately spawned agent received only the isolated kit, observed the failing deterministic-ranking check, changed only src/rank.mjs, passed the final check, and produced an attestation that the primary verifier accepted.

## Reproduction commands

1. npm run project-proof:replay:test
2. npm run project-proof:replay:prepare -- --project=github-projects-for-ai-engineers
3. npm run validate:content
4. npm run build
5. npm run validate:agent-indexes

## Expected artifacts

- A normalized repository dataset with canonical repo name, URL, stars, forks, theme, area, export hits, and reason.
- A deterministic ranking pass sorted by stars descending, then forks as the secondary signal.
- A normalized ecosystem dataset with official links, capability categories, production-depth levels, and explicit source notes.
- A private dropped-repository audit when refreshing from a local export snapshot.

## Buildability breakdown

Assessment level: non-author-replayed. Assessed at: 2026-08-15.

| Axis | Level | Evidence | Bottleneck |
| --- | --- | --- | --- |
| time | high | The clean-room ranking task reaches a checked result in one bounded local run, while the public blueprint targets one local content pass plus a GitHub metric refresh. | A full radar refresh still depends on fetching current GitHub metrics for all 140 repositories. |
| code burden | medium | The replay needs one focused comparator edit plus the existing test, and the public blueprint needs normalization scripts and content validation rather than a bespoke application. | Curating theme lanes and relevance still needs an editor who knows the AI-engineering landscape. |
| integration burden | medium | The replay uses only repository files, Node, and a test runner, while the wider blueprint touches public GitHub reads and Astro content routes that already exist in this repository. | GitHub metric drift forces every refresh through an approved external read before the ledger is current. |
| operational burden | high | The first proof is local and temporary, requires no server, and touches no shared or production state. | Keeping the published radar current requires a recurring refresh protocol rather than one-off runs. |
| permission clarity | high | GitHub read, local Telegram-export read, and public-site publish scopes are named separately with explicit approval gates in the build brief and acceptance contract. | A live metric refresh and a public deploy still need two separate owner approvals. |
| reproducibility | high | A separately spawned agent received only the isolated public kit, reproduced the failing deterministic-ranking check, changed the one allowed comparator file, and passed the same acceptance check without inspecting the author solution. | The bounded fixture proves the published sort rule, not a full 140-repository refresh against live GitHub data. |
| failure recovery | high | The replay records the initial failing check, preserves an unrelated file byte-for-byte, performs no remote action, and leaves explicit residual-risk and approval sections. | Rolling back a bad public radar release is outside this local proof. |

## Replay evidence

### clean-room-bounded-edit-v1

- Date: 2026-08-15.
- Kind: automated-clean-room.
- Instructions: public-build-brief-and-acceptance.
- Environment: Temporary local directory with a bounded Node fixture and no network or remote writes.
- Outcome: passed.

Artifacts:

- Task brief: task-brief.json. Check: Names the allowed comparator file, acceptance command, non-goals, and approval-gated actions.
- Focused implementation: src/rank.mjs. Check: Changes only the named comparator file while the unrelated note retains its original checksum.
- Real test: test/rank.test.mjs. Check: Fails while the initial comparator sorts ascending without a forks tiebreaker, and passes after the focused edit applies stars descending with forks as the secondary signal.
- Structured handoff: handoff.json. Check: Separates implemented files, verification evidence, residual risk, and approval-gated actions.

Limitations:

- This replay proves the deterministic ranking contract on a bounded fixture dataset, not a real external repository integration or a live GitHub metric refresh.
- No human or separately operated agent independently interpreted the brief in this clean-room replay.

### independent-claude-replay-v1

- Date: 2026-08-15.
- Kind: independent-agent.
- Instructions: public-build-brief-and-acceptance.
- Environment: Isolated temporary replay kit; the executor was a separately spawned agent session that received only the kit path, no parent conversation or main-repository context, and used no network or remote writes.
- Outcome: passed.

Artifacts:

- Independent attestation: reviewer-attestation.json. Check: Names an independent agent, records initial failure and final pass, lists only src/rank.mjs as changed, and attests that the author solution was not inspected.
- Verified replay receipt: src/data/project-proof-receipts/github-projects-for-ai-engineers-independent-agent-v1.json (https://newruntime.com/projects/github-projects-for-ai-engineers.json). Check: The primary verifier checked protected-file hashes, rejected unexpected files, reran the acceptance test, and recorded zero unexpected files.

Limitations:

- This proves a bounded non-author replay of the public workflow contract, not integration into a production repository.
- Reviewer identity is a local agent attestation and is not cryptographically verified.

## Provider scopes

| Provider | Access | Minimum scope | Approval gate |
| --- | --- | --- | --- |
| GitHub | read | Public repository metadata and repository pages only. | Human approves a live GitHub refresh before network calls. |
| Local Telegram export | read | Repository-link roots and duplicate counts from an approved local export snapshot. | Human names the export snapshot or existing dataset before extraction. |
| Public site | publish | Project content, project dataset, and generated static routes for this radar only. | Owner approves commit, push, and deploy separately from the metric refresh. |

## Privacy constraints

- Data touched: Public GitHub repository pages and local Telegram export link roots.
- Use only publishable public source URLs in public output.
- Do not expose private discovery feeds, raw exports, credentials, drafts, or internal handoff artifacts.

## Required approvals

- Public site content only after owner-approved deployment
- read: Human approves a current metric refresh before network calls.
- publish: Human approves publication/deploy separately from the local refresh.

## Build steps

1. Extract GitHub repository roots from the approved local export or existing project dataset.
2. Resolve redirects and drop placeholders, private repositories, duplicates, and non-AI-engineering projects.
3. Refresh public GitHub metrics and canonical full names for the surviving repositories.
4. Assign each repository to one theme lane and explain why it matters to New Runtime.
5. Rebuild the project JSON/Markdown/HTML routes and verify the ranked ledger still matches the refreshed dataset.

## Acceptance checks

- Every retained repository has a public GitHub URL, canonical full name, stars, forks, theme, engineering area, and reason.
- The ranking is deterministic: stars descending, forks as the secondary signal.
- Private Telegram provenance is not exposed; public citations point to GitHub repository URLs only.
- The HTML radar and JSON dataset agree on project count, theme counts, and top-ranked repositories.
- The ecosystem section and JSON dataset agree on tool count, capability lanes, production-depth levels, link kinds, and source attribution.
- Astro build and agent index contract tests pass after the refresh.

## Explicit non-goals

- Do not publish the raw Telegram export or private discovery provenance.
- Do not clone or execute the listed repositories during the first refresh pass.
- Do not add repositories only because they are popular if they do not support AI engineering work.

## Questions before external systems

- Which export snapshot or dataset is the source of truth for this refresh?
- Is a live GitHub metric refresh approved for this run?
- Should archived, redirected, or sponsor-only repositories be kept, dropped, or marked?

## Related routes

- html: https://newruntime.com/projects/github-projects-for-ai-engineers/
- project_markdown: https://newruntime.com/projects/github-projects-for-ai-engineers.md
- project_json: https://newruntime.com/projects/github-projects-for-ai-engineers.json
- acceptance: https://newruntime.com/projects/github-projects-for-ai-engineers/acceptance.md