skills/graph-research/SKILL.md
Uses Graphify as ADLC's graph-backed research layer and Beads as an optional dependency-aware task memory layer. Produces evidence for compatibility, reuse, accuracy, dark-code hotspots, and long-horizon handoff.
npx skillsauth add bigeasyfreeman/adlc graph-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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ADLC research must be grounded in a persistent map of the codebase, not a fresh grep pass every time. This skill makes Graphify the default repo-understanding substrate and treats Beads as the optional work graph for task state, dependency holds, and durable agent memory.
Use Graphify for:
Use Beads for:
Do not use Beads as an architecture oracle. Do not use Graphify as the work-item source of truth. They solve different problems.
Run during research before raw repository search whenever a repo path is available.
Run again during code_review or pr_prep when the change touches any of:
If graphify-out/wiki/index.md exists, navigate that first.
If graphify-out/GRAPH_REPORT.md exists:
Built from commit value against git rev-parse HEAD.graphify update . and record that the refresh is AST-only unless semantic extraction is also run.If graphify-out/ does not exist:
graphify update . for an AST-only graph when Graphify is installed.graph_status = unavailable and fall back to normal research with an explicit gap.If .beads/ exists or bd is available:
bd prime for workflow context.bd ready --json when choosing execution slices.bd show <id> --json before treating an issue description as authoritative.Ask graph questions before raw grep when the graph is available:
graphify query "What modules and interfaces are most relevant to this PRD or change?"
graphify query "What constructs, config, schemas, persistence paths, and tests are related to this PRD or change?"
graphify query "What existing implementation should be reused or extended for this request?"
graphify query "What backward compatibility or forward compatibility paths could this change affect?"
graphify query "What validation surfaces prove behavior for the constructs this change touches?"
graphify query "What implementation interfaces should this change reuse, consume, emit, validate, or preserve?"
graphify query "What production_ready claims would be overclaimed without validation evidence, rollback, observability, security/privacy, reliability, or no-overclaim data?"
graphify query "What cross-module paths create dark-code risk for this request?"
graphify path "<changed module>" "<dependent module>"
graphify explain "<core concept or module>"
Use the specific module, service, schema, or public interface names from the PRD or diff. Do not ask vague graph questions and present the answer as evidence.
For every interface, schema, storage, or integration change, produce an explicit compatibility finding:
Graph evidence is a starting point, not proof by itself. Confirm critical compatibility claims against source files, schemas, tests, or docs before the Build Brief turns them into tasks.
For every code-backed ADLC run, produce a construct map scoped to the requested work:
Do not present "the repo" as the construct. Name the actual code constructs and relationships an agent must preserve.
For every integration, schema, emitter, workflow-state, CLI, provider, or reusable framework change, produce:
implementation_interface_candidates: reuse paths, consumes, emits, minimum fields, invariants, integration points, validation gates, failure semantics, and evidence refsblocked_production_claims: unsupported, evidence-only, monitor-only, not-yet-GA, governed, or overclaimed production-ready statements with the missing proof namedGraph-derived implementation-interface and productionization claims require direct verification before they become Build Brief task fields.
{
"graph_research_evidence": {
"graph_status": "fresh | stale_refreshed_ast_only | stale_not_refreshed | unavailable",
"graph_report": "graphify-out/GRAPH_REPORT.md | null",
"wiki_index": "graphify-out/wiki/index.md | null",
"head_commit": "string | null",
"graph_commit": "string | null",
"queries_run": [
{
"query": "string",
"result_summary": "string",
"source": "graphify query | graphify path | graphify explain"
}
],
"direct_verification": [
{
"claim": "string",
"evidence": "path:line | command output | doc quote",
"confidence": "high | medium | low"
}
]
},
"construct_map": {
"constructs": [
{
"name": "string",
"kind": "module | service | api | schema | config | env | persistence | event | queue | test | cli | generated_artifact | other",
"evidence": "graph query + path:line | command output",
"confidence": "high | medium | low"
}
],
"relationships": [
{
"from": "string",
"to": "string",
"relationship": "imports | calls | consumes | produces | stores | validates | configures | depends_on | other",
"evidence": "graph query + direct verification"
}
],
"validation_surfaces": [],
"blast_radius": [],
"accuracy_gaps": []
},
"compatibility_evidence": {
"backward_compatibility": [],
"forward_compatibility": [],
"rollback_or_downgrade": [],
"reuse_paths": [],
"paved_road_refs": [],
"accuracy_gaps": []
},
"implementation_interface_evidence": {
"implementation_interface_candidates": [],
"integration_points": [],
"validation_gates": [],
"blocked_production_claims": []
},
"task_memory_context": {
"beads_status": "available | not_configured | unavailable",
"ready_items": [],
"blocking_dependencies": [],
"persistent_memories": []
},
"dark_code_hotspots": [
{
"component": "string",
"risk": "string",
"evidence": "graph query + direct verification",
"needs_context_artifact": true
}
]
}
development
Discovers and records repo-local approved build paths so agents reuse proven patterns instead of inventing parallel architectures.
development
Scoped maintenance for docs/solutions entries when stale signals, refactors, or explicit user scope require refresh.
documentation
Conditionally captures verified reusable ADLC learnings into docs/solutions after successful closeout.
tools
Assesses structural and velocity dark-code risk from architecture, AI tool usage, ownership, and deployment practices. Produces a direct risk assessment without inventing missing facts.