railiance-fabric/docs/repo-reality-scanner.md

Repo Reality Scanner

The repo reality scanner discovers Fabric entities from repository evidence and turns them into candidate graph facts. It is a discovery layer, not a new authoring surface. Repo-owned declarations remain the highest-trust source for accepted Fabric graph data.

Contract

A scanner run emits a FabricDiscoverySnapshot. The snapshot is scoped to one repository, one commit, and one scan profile. It contains:

• replacement scopes, which define the evidence sets that may be replaced on a rescan
• candidate nodes, edges, and attributes
• source anchors for every candidate
• extractor provenance for every candidate
• tombstones for candidates that vanished inside a replacement scope
• reconciliation policy metadata

The JSON schema lives at schemas/discovery-snapshot.schema.yaml.

Deterministic Scanner CLI

The first implementation slice adds an offline deterministic scan command:

railiance-fabric scan . \
  --repo-slug railiance-fabric \
  --commit "$(git rev-parse HEAD)" \
  --dry-run \
  --output discovery-snapshot.json

Use --json to print the full FabricDiscoverySnapshot to stdout. Without --json, the command prints a concise summary of node, edge, attribute, and replacement-scope counts. The scanner does not call registries, catalogs, or LLMs in this mode; --output is the only write side effect.

The deterministic extractor framework currently covers:

• repository metadata from local git/path evidence
• README, INTENT, and SCOPE document presence and headings
• repo-owned Fabric declarations under fabric/
• Python pyproject.toml package metadata and dependencies
• Node package.json package metadata and dependencies
• common lockfiles such as package-lock.json, poetry.lock, and uv.lock
• Dockerfiles and Docker Compose services
• OpenAPI and AsyncAPI contract files
• Score workload files
• Kubernetes-style deployment manifests
• common service config files such as application.yaml and appsettings.json

Each extractor emits candidates through the same accumulator so stable-key duplicates merge inside a scan before the snapshot is returned.

LLM-Assisted Extraction

LLM extraction is optional and explicit:

railiance-fabric scan . \
  --repo-slug railiance-fabric \
  --llm \
  --llm-provider openai \
  --llm-model gpt-4.1-mini \
  --dry-run \
  --output discovery-with-llm.json

The implementation integrates through llm-connect with create_adapter and RunConfig. Tests use a MockLLMAdapter-compatible boundary so CI stays offline. If llm-connect is unavailable, the provider call fails, or the model returns malformed JSON, the scanner records a review_artifacts entry and keeps the discovery snapshot schema-valid.

The LLM never receives the whole repository. The scanner first builds a compact evidence bundle from deterministic candidates, prioritizing repo-owned Fabric declarations, services, capabilities, interfaces, libraries, deployments, and small README/INTENT/SCOPE signals. The prompt asks for strict JSON:

{"nodes": [], "edges": [], "attributes": []}

Projected LLM candidates are always origin: llm and review_state: needs_review. Candidates below the configured confidence threshold become llm_low_confidence review artifacts instead of graph candidates. Unresolved edge endpoints or attribute targets also become review artifacts. Accepted graph data still requires deterministic evidence, repo-owned declarations, or a later human review/acceptance path.

Identity

Identity is the main safety boundary. The scanner must not append guesses on every run. It needs to produce stable keys that are repeatable for the same observed entity.

Candidate node keys use this shape:

discovery:{repo_slug}:{entity_kind}:{normalized_name}[:source_fingerprint]

Use the optional source fingerprint when a name is too generic or when multiple entities of the same kind can share a display name. Examples include HTTP routes, generated clients, deployment manifests, and catalog records.

Candidate edge keys use a relationship fingerprint over:

• source stable key
• edge type
• target stable key
• optional evidence scope

Candidate attribute keys use the entity stable key plus the normalized attribute name and, where needed, a source fingerprint.

Stable-key parts are lowercased and normalized to ASCII-like identity segments. The helper functions in railiance_fabric.discovery define the initial rules.

Source Anchors

Every candidate must carry one or more source anchors. A source anchor identifies why the scanner believes the fact exists. Anchors can point to files, package manifests, lockfiles, API contracts, deployment manifests, service catalogs, registries, LLM evidence bundles, or manual review notes.

Source anchors include a fingerprint. The fingerprint should cover stable location fields such as path, URL, ref, line range, or JSON pointer. Snippets are useful for review but should not be the only identity anchor because formatting noise can churn snippets.

Replacement Scopes

A replacement scope says which extractor owns which set of candidates. Rescans may retire missing candidates only inside the same scope.

Examples:

• scope:repo-scoping:python-package:package_manifest:<hash>
• scope:state-hub:fabric-declarations:declaration
• scope:llm-connect:readme-summary:file:<hash>
• scope:railiance-fabric:local-registry:fabric_registry

Scopes have a mode:

• replacement: candidates missing from the next run in the same scope become tombstones.
• additive: candidates are added or updated, but absence does not retire old candidates.

LLM extractors should usually use replacement mode only for tightly bounded evidence bundles. Broad repo summaries are safer as additive or review-only until the extraction prompts are proven stable.

Merge Precedence

When multiple sources describe the same entity, reconciliation uses this precedence:

1. repo_declaration
2. deterministic
3. catalog
4. registry
5. llm
6. manual

Manual review can override local candidate state, but it should not silently rewrite repo-owned declarations. If accepted discoveries should become authoritative, the safer next step is to generate a repo-owned declaration patch for human review.

Duplicate Handling

The reconciler should merge candidates with the same stable key automatically. It should also look for possible duplicates using:

• alias overlap
• identical source anchors
• identical evidence fingerprints
• normalized label similarity within the same entity kind
• relationship fingerprints with the same endpoints and edge type
• declaration ids that match discovery aliases

Exact stable-key matches can be merged automatically. Alias-only or similarity-only matches should become needs_review conflicts unless an extractor has a source-specific rule that makes the match deterministic.

Rescan And Tombstones

On a rescan, the scanner compares the previous accepted discovery snapshot with the newly produced snapshot for the same repo/profile.

• Same stable key: update in place.
• Same source anchor but changed attributes: update with changed evidence.
• Missing from same replacement scope: create a tombstone.
• Missing from a different scope: leave untouched.
• Reappears after tombstone: reactivate if the stable key and scope match.
• Reappears with a new key but same alias/source anchor: flag as possible duplicate resurrection.

Tombstones explain graph drift and prevent immediate re-creation loops. They should be retained long enough to compare several scan cycles and can later be compacted by repo, extractor, or entity kind.

Mapping To Fabric Graphs

Discovery candidates can project into the existing graph model when accepted:

• candidate service nodes map to ServiceDeclaration-like graph nodes
• candidate capabilities and interfaces map to provider surface nodes
• candidate dependencies map to dependency nodes and consumes edges
• candidate deployment/runtime entities map to graph explorer infrastructure nodes until declarations gain first-class runtime support
• candidate libraries map to library inventory records and graph explorer nodes

If a repo-owned declaration already exists for the same entity, discovery output should attach as supporting evidence instead of creating another node.

LLM Boundary

LLM extraction through llm-connect is optional and schema-gated. The scanner should use deterministic preselection to build small evidence bundles, ask for structured JSON, validate the JSON against the discovery schema, and record:

• extractor id and version
• prompt version
• provider and model
• usage metadata
• confidence and uncertainty
• rationale

Malformed, low-confidence, or conflicting LLM output becomes review material, not accepted graph data.