dev-context-multi-repo

Solid

Builds multi-repo context hubs and compiled markdown knowledge maps. Use when profiling repo portfolios or assembling LLM-ready cross-repo knowledge bases.

AI & Automation 80 stars 17 forks Updated 1 weeks ago MIT

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Quality Score: 86/100

Stars 20%
64
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Multi-Repo Context Use this skill to inventory, normalize, and publish one coherent knowledge hub for many repositories. The strongest default is a two-layer system: raw source captures plus an LLM-maintained compiled markdown hub with profiles, concept pages, backlinks, and derived reports. It owns repo discovery, high-signal extraction, cross-repo inference, artifact-set generation, and knowledge-graph assembly. It does not replace single-repo context or code-graph work. ## Quick Reference | Task | Use | |------|-----| | Discover repos and classify portfolio shape | `scripts/discover_repos.py`, [references/repo-discovery-patterns.md](references/repo-discovery-patterns.md) | | Scan repos into normalized profiles | `scripts/scan_repo.py`, `scripts/scan_portfolio.py`, [references/repo-classification-rules.md](references/repo-classification-rules.md) | | Build standard artifact set | `scripts/build_artifact_set.py`, [references/output-model-and-gap-analysis.md](references/output-model-and-gap-analysis.md) | | Build and validate graph outputs | `scripts/build_knowledge_graph.py`, `scripts/validate_graph.py`, `scripts/check_graph_consistency.py`, [references/knowledge-graph-patterns.md](references/knowledge-graph-patterns.md) | | Calibrate graph edge weights from evidence | `scripts/calibrate_weights.py`, [references/knowledge-graph-patterns.md](references/knowledge-graph-patterns.md) | | Query graph neighborhoods, paths, impact, rank, PPR, and communities | `scripts/query_g...

Details

Author
vasilyu1983
Repository
vasilyu1983/AI-Agents-public
Created
9 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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