From aidoc-flow
Scans a project's SDD artifacts to build a context model (inventory, traceability graph, workflow position, upstream candidates) before authoring a new document.
How this skill is triggered — by the user, by Claude, or both
Slash command
/aidoc-flow:context-analyzerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Scan a project's documentation and assemble a context model so a new artifact is
Scan a project's documentation and assemble a context model so a new artifact is authored with full awareness of what already exists. It surfaces the artifact inventory, parsed metadata, the traceability graph, current workflow position, and the most relevant upstream documents — preventing missing references and duplicate content.
Use context-analyzer when:
doc-* skill invocation.Do not use it on a project with no existing documentation, for a single
isolated document, or for deep traceability validation (use
../trace-check/SKILL.md).
Given a project_root (and optionally a target_artifact_type and a scan
depth of quick / standard / deep), the skill:
docs/<NN>_<X>/ (01_BRD …
08_IPLAN) by type, ID, path, title, and status.The model is consumed by ../skill-recommender/SKILL.md,
../workflow-optimizer/SKILL.md, ../quality-advisor/SKILL.md, and the
doc-* authoring skills. For deep traceability validation it defers to
../trace-check/SKILL.md.
can_reference):
framework/registry/LAYER_REGISTRY.yamlframework/governance/TRACEABILITY.mdframework/governance/ID_NAMING_STANDARDS.mdframework/layers/02_PRD/README.md ·
framework/layers/05_ADR/README.md../skill-recommender/SKILL.md ·
../workflow-optimizer/SKILL.md · ../quality-advisor/SKILL.md ·
../trace-check/SKILL.mdnpx claudepluginhub vladm3105/aidoc-flow-framework --plugin aidoc-flowDetermines position in the 8-layer SDD workflow from completed artifacts and recommends prioritized next steps, parallel-work opportunities, and progress metrics.
Analyzes existing codebases from a given directory (or project root) and generates context documents (architecture, requirements, test plan) for downstream skills.
Reverse-engineers existing codebases into V-Model artifacts (plan-context, ADRs, arc42, FEATURE inventory, backlog). Produces evidence-based documentation sourced from code and docs.