Analyzing architecture
My personal directory of AI Agent skills
npx -y skills add tkolleh/skills --skill analyzing-architectureAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Detects repository context, maps dependencies (enterprise-specific or general), and generates/persists architectural documentation.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
2.8 KB, as published. Nobody here has run it
What I do
I analyze the current repository to determine its origin and architecture. If it is hosted on a private enterprise Sourcegraph instance, I execute a deep internal discovery protocol using the /sourcegraph-search skill. Otherwise, I perform a comprehensive local audit of language, frameworks, and public dependencies.
When to use me
Trigger this skill immediately upon entering a new repository or when the user asks "How does this system work?", "What is the architecture", "What are the system dependencies?", or "What should I know to work on this codebase?".
Operational Workflow
Phase 1: Context Detection
Run gh repo view --json url or inspect the local .git/config to check the remote host.
- IF host is a private enterprise Sourcegraph instance: Proceed to Phase 2 (Enterprise Protocol).
- OTHERWISE: Proceed to Phase 3 (General Protocol).
Phase 2: Enterprise Sourcegraph Discovery Protocol
Use src search to map the ecosystem. Adapt the repo patterns to your organization's Sourcegraph instance:
- Contracts: Search for the organization's interface definition contracts repository for Thrift IDL, Proto schema, GraphQL, etc...
- Message Queues: Search for message broker (e.g., Kafka) infrastructure-as-code repositories.
- Cloud/IAM: Search for cloud infrastructure bootstrap or service provisioning repositories.
- Data Pipelines: Search for ETL job or data pipeline deployer repositories.
- Data Stores: Search for database schema or storage infrastructure repositories.
Phase 3: General Discovery Protocol
- Identify Project Type: Detect primary language (e.g.,
package.json,build.sbt,go.mod). - Scan Local Entry Points: Use
serena.search_codeto find Main classes, server initializations, or API routes. - Map Local Dependencies: Parse dependency files to identify external services (e.g., AWS SDKs, database drivers, third-party APIs).
Phase 4: Documentation & Persistence
- Generate
AGENT.md: Create or update the rootAGENT.md. - Visualize: Call the
diagramming-d2skill to generate a system map. - Mandatory Memory Save: You MUST save the summarized project architecture (business goal, technical stack, and key dependencies) to your long-term memory. This ensures you do not need to re-run the full discovery in future sessions.
Constraints
- Evidence-Based: Every dependency listed must be cited with its
srcsearch result or local file reference. - Privacy: Never save sensitive strings, keys, or secrets to memory.