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Skillsscan-project-structure

scan-project-structure

Writing & research

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Updated 10-15 21:35

About this skill

Codex compatibility note:

  • Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
  • Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
  • User-question prompts mean to ask the user directly in Codex.
  • Ignore Claude-specific mode-switch instructions when they appear.
  • Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
  • Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
  • Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
  • For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
  • If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

  • docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
  • docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
  • docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Situation-based docs:

  • Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
  • Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
  • Spec/test-case planning or TC mapping: feature-docs-reference.md
  • Integration test implementation/review: integration-test-reference.md
  • E2E test implementation/review: e2e-test-reference.md
  • Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Scan project codebase → populate docs/project-reference/project-structure-reference.md with accurate service architecture, API ports, directory structure, tech stack, and module codes.

Workflow:

  1. Classify — Detect architecture type and scan mode
  2. Scan — Parallel sub-agents discover services, ports, tech stack
  3. Report — Write findings incrementally to report file
  4. Generate — Build/update reference doc from report
  5. Fresh-Eyes — Round 2 verification validates all paths and ports

Key Rules:

  • Generic — discover everything dynamically, never hardcode project-specific values MUST ATTENTION detect architecture type FIRST — scan depth and sub-agent focus depend on it
  • ALL port numbers must be read from actual config files — never infer from memory
Scan Project Structure

Phase 0: Detect Architecture Type & Mode

[BLOCKING] Before any other step, run in parallel:

  1. Read docs/project-reference/project-structure-reference.md

    • Detect mode: Init (placeholder) or Sync (populated)
    • In Sync mode: note which sections have content; check for stale ports/paths

  2. Detect architecture type:

Signal Architecture Sub-Agent Focus
Multiple src/Services/ directories, each with own Dockerfile Microservices Enumerate ALL services; map each to port + Dockerfile
Single src/ with one Program.cs / Startup.cs Monolith Single service deep-scan; module/feature breakdown
Single git repo, multiple deployable apps Monorepo (non-microservices) App boundaries; shared library mapping
Nx workspace / multiple project.json Nx monorepo Library graph; app/lib/buildable distinction
Multiple repos detected (git submodules) Polyrepo Per-repo breakdown; cross-repo contracts
  1. Detect orchestration approach:
Signal Orchestration What to Document
src/Aspire/ directory .NET Aspire Aspire project name, dashboard URL, resource names
docker-compose*.yml Docker Compose Service definitions, port mappings, volume mounts
k8s/ or charts/ Kubernetes / Helm Deployment targets, ingress config
No orchestration files Direct run Launch commands per service
  1. Load module list from docs/project-config.json modules[] if available — use as expected service catalog.

Evidence gate: Confidence <60% on architecture type → report uncertainty, DO NOT proceed with architecture-specific scan assumptions.

Phase 1: Plan

Create task tracking entries for each sub-agent and each verification step. Do not start Phase 2 without tasks created.

Phase 2: Execute Scan (Parallel Sub-Agents)

Launch 3 general-purpose sub-agents in parallel. Each MUST:

  • Write findings incrementally after each service/file — NEVER batch at end
  • Cite file:line for every port number and Dockerfile path
  • Confidence: >80% document; 60-80% note as "observed (unverified)"; <60% omit

All findings → plans/reports/scan-project-structure-{YYMMDD}-{HHMM}-report.md

Agent 1: Backend Services

Think (Completeness dimension): How many services exist? Is there a service in the codebase with no Dockerfile (worker? library? shared?)? Which services expose HTTP APIs vs are background workers?

Think (Port accuracy dimension): Where are ports defined — launchSettings.json, appsettings*.json, docker-compose.yml, Program.cs? Some services may have port in multiple places that must agree.

Think (Pattern dimension): Is there a consistent folder structure per service (e.g., Service/, Domain/, Application/, Infrastructure/)? What deviates from the pattern?

  • Glob for **/*.csproj and **/Dockerfile to find all services — reconcile against project-config.json modules
  • Read launchSettings.json and appsettings*.json for each service to extract ports
  • Grep for [ApiController], MapControllers, app.MapGet to classify API vs worker vs library
  • Find service entry points (Program.cs, Startup.cs) and identify service type
  • Note services that appear in config but have no Dockerfile (or vice versa) — flag as gap

Agent 2: Frontend Apps

Think (App inventory dimension): How many frontend apps exist? Which are active (have dev-start commands) vs legacy vs deprecated?

Think (Port/config dimension): Where is the dev server port defined — angular.json serve config, vite.config.ts, next.config.js, proxy config? Read the actual config — do not infer.

Think (Dependency dimension): Which apps consume which shared libraries? Is there a design system library? A domain library? What's the dependency graph direction?

  • Glob for **/angular.json, **/nx.json, **/vite.config.*, **/next.config.* (exclude node_modules)
  • Read app serve configurations for dev server ports
  • Find app entry points (main.ts, index.tsx, App.vue)
  • Identify framework versions from package.json (exact versions, not ranges)
  • Map app-to-library dependencies from Nx project graph or import analysis

Agent 3: Infrastructure & Tech Stack

Think (Infrastructure dimension): What external services must be running for the app to function? Which are optional? What are the default credentials (from docker-compose or defaults)?

Think (CI/CD dimension): What pipeline system is used? What are the build/test/deploy stages? What environments exist?

Think (Version accuracy dimension): Framework/library versions must come from actual config files — not assumed from project type.

  • Read docker-compose*.yml — extract infrastructure service definitions, port mappings, credentials
  • Find CI/CD configs (.github/workflows/*.yml, azure-pipelines.yml, Jenkinsfile) — extract stages
  • Parse primary package managers for key dependencies with versions (package.json, *.csproj)
  • Identify databases per service (MongoDB, SQL Server, PostgreSQL, Redis) from connection strings
  • Find message broker config (RabbitMQ, Kafka, Azure Service Bus) from appsettings

Phase 3: Analyze & Generate

Read full report. Apply fresh-eyes protocol:

Round 1 (main agent): Build section drafts from report findings.

Round 2 (fresh sub-agent, zero memory):

  • Do ALL Dockerfile paths in the service table exist on filesystem? (Glob verify — ALL, not 3)
  • Do port numbers match the actual launchSettings.json or docker-compose.yml entries? (Grep verify)
  • Are there any services found by Agent 1 that are missing from the service table?
  • Are framework versions from actual config files (not inferred)?

Target Sections

Section Content
Architecture Overview Architecture type, orchestration approach, deployment model
Service Architecture Table: Service Name, Type (API/Worker/App), Port, Dockerfile path
Infrastructure Ports Table: Service (DB/MQ/Cache), Port, Credentials (from docker-compose)
Frontend Apps Table: App name, Framework, Dev port, Build command
Tech Stack Table: Category (Backend/Frontend/Infra), Technology, Version
Module Codes Table: Module code abbreviation, Full name, Service path
Key Directories Top 2-3 levels of src/ with one-line purpose per top-level dir

Phase 4: Write & Verify

  1. Write updated doc with <!-- Last scanned: YYYY-MM-DD --> at top
  2. Surgical update only — preserve unchanged sections
  3. Verify (Glob): ALL Dockerfile paths in service table exist — not just 3
  4. Verify (Grep): Port numbers match actual config files
  5. Report: sections updated vs unchanged, services catalogued, gaps found

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks per file read. Prevents context loss from long files. Simple tasks: ask user whether to skip.

Prerequisites: MUST ATTENTION READ before executing:

Critical Thinking Mindset — Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources, admit uncertainty, self-check output, cross-reference independently. Certainty without evidence = root of all hallucination.

Scan & Update Reference Doc — Surgical updates only, NEVER full rewrite.

  1. Read existing doc first — understand structure and manual annotations
  2. Detect mode: Placeholder (headings only) → Init. Has content → Sync.
  3. Scan codebase (grep/glob) for current state
  4. Diff findings vs doc — identify stale sections only
  5. Update ONLY diverged sections. Preserve manual annotations.
  6. Update metadata (date, version) in frontmatter/header
  7. NEVER rewrite entire doc. NEVER remove sections without evidence obsolete.

Output Quality — Token efficiency without sacrificing quality.

  1. No inventories/counts — stale instantly
  2. No directory trees — use 1-line path conventions
  3. No TOCs — AI reads linearly
  4. One example per pattern — only if non-obvious
  5. Lead with answer, not reasoning
  6. Sacrifice grammar for concision in reports
  7. Unresolved questions at end

AI Mistake Prevention — Failure modes to avoid:

Verify AI-generated content against actual code. AI hallucinates service names, ports, and file paths. Grep/Glob to confirm before documenting. Trace full dependency chain after edits. Always trace full chain. Surface ambiguity before coding. NEVER pick silently. NEVER hardcode port numbers without config file evidence. Read launchSettings.json or docker-compose.yml — never infer from memory.

IMPORTANT MUST ATTENTION read existing doc first, scan codebase, diff, surgical update only. Never rewrite entire doc.

IMPORTANT MUST ATTENTION output quality: no counts/trees/TOCs, 1 example per pattern, lead with answer.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

IMPORTANT MUST ATTENTION break work into small task tracking tasks BEFORE starting IMPORTANT MUST ATTENTION detect architecture type in Phase 0 — sub-agent focus depends on it IMPORTANT MUST ATTENTION cite file:line for every port number and path — NEVER infer from memory IMPORTANT MUST ATTENTION sub-agents write findings incrementally after each service — NEVER batch at end IMPORTANT MUST ATTENTION verify ALL Dockerfile paths — spot-check of 3 is insufficient IMPORTANT MUST ATTENTION when Round 1 finds issues, Round 2 fresh-eyes after fixing validates ports and paths. Clean Round 1 ENDS the scan.

Anti-Rationalization:

Evasion Rebuttal
"Architecture type obvious from directory names" Verify from actual project files — names are not evidence
"Port numbers are standard (5000, 8080, etc.)" Read config files — NEVER infer ports from framework conventions
"Checked 3 Dockerfile paths, that's enough" Glob-verify ALL paths — partial verification hides missing services
"Framework versions obvious from project type" Read package.json/.csproj for exact versions — never assume
"Skip Round 2 even when Round 1 found issues" Clean Round 1 ends the scan. When issues exist, fresh-eyes mandatory after fixing — port numbers and paths are the most hallucination-prone data.
"project-config.json not needed if repo looks clear" Config file provides expected service catalog — use it to detect missing services

[TASK-PLANNING] Before acting, analyze task scope and break into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. Any supported AI tool may execute when this shared context and local docs are available.

  1. DETECT: Match prompt against workflow catalog
  2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
  3. ASK (REQUIRED FORMAT): Use a direct user question with this structure unless the user explicitly invoked a workflow/skill and the local protocol treats explicit invocation as confirmation:
    • Question: "Which workflow do you want to activate?"
    • Option 1: "Activate [BestMatch Workflow] (Recommended)"
    • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
  4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
  5. CREATE TASKS: task tracking for ALL workflow steps
  6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

  1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
  2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
  3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
  4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
  5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
  6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
  7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.