/infra-audit -- Infrastructure Auditor
SkillDatabases & dataReads docker-compose, env files, ORM configs, and connection strings to map current infrastructure. Flags missing layers (cache, queue, analytics) based on observed access patterns. Outputs a structured infrastructure manifest.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the /infra-audit -- Infrastructure Auditor skill
What this skill tells your AI
The instructions your AI receives, as published by sethgammon/citadel in skills/infra-audit/SKILL.md and read by ahel’s review.
When to Use
- Before adding a new database, cache, or queue to a project
- When onboarding to an unfamiliar codebase and need to understand its infra
- Before planning a workspace campaign that spans multiple services
- When someone asks "what systems does this project talk to?"
Do not use when:
- The user already knows the infra and just wants to wire something up (use
/architect) - The question is about code architecture, not infrastructure (use
/research)
Protocol
Step 1: DISCOVER
Scan the project for infrastructure configuration files. Check each category:
Container orchestration:
docker-compose.yml,docker-compose.*.ymlDockerfile,*.dockerfilek8s/,kubernetes/,helm/,charts/
Environment and secrets:
.env,.env.*,.env.example,.env.local*.envfiles in config directories
Database and ORM:
- Prisma:
prisma/schema.prisma - Drizzle:
drizzle.config.ts,drizzle/ - TypeORM:
ormconfig.*,data-source.ts - Sequelize:
.sequelizerc,config/database.* - Knex:
knexfile.* - SQLAlchemy:
alembic.ini,alembic/ - Django:
settings.py(DATABASES section) - Rails:
config/database.yml - Go: look for
pgx,gorm,sqlxin go.mod
Message queues and event streaming:
- Redis: connection strings,
ioredis,redisin package.json/requirements.txt/go.mod - RabbitMQ:
amqplib,pika,amqpimports - Kafka:
kafkajs,confluent-kafka,saramaimports - NATS:
nats,nats.goimports - SQS/SNS:
@aws-sdk/client-sqs,boto3sqs references
Cache:
- Redis (dual-use -- note if used as cache vs. pub/sub vs. primary store)
- Memcached:
memcached,pylibmcimports
Search:
- Elasticsearch:
@elastic/elasticsearch,elasticsearch-py - Meilisearch, Typesense, Algolia client libraries
Object storage:
- S3:
@aws-sdk/client-s3,boto3s3 references - MinIO, GCS, Azure Blob client libraries
External APIs:
- Stripe, Twilio, SendGrid, Auth0, Firebase, Supabase client libraries
- Any
NEXT_PUBLIC_*orVITE_*env vars pointing to external services
CI/CD:
.github/workflows/,.gitlab-ci.yml,Jenkinsfile,bitbucket-pipelines.yml
For each discovered item, record:
- What: the system (e.g., "PostgreSQL 15")
- Where: config file path and line
- How: connection method (direct, pooled, ORM, SDK)
- Role: primary store, cache, queue, search, auth, etc.
Step 2: TRACE CONNECTIONS
For each discovered system, trace how the application connects:
- Find connection strings in env files or config
- Find the client initialization code (imports,
new Client(),createPool()) - Identify which modules/services use this connection
- Note connection pooling, retry logic, health checks if present
Build a connection graph:
App --> [pool: 10] --> PostgreSQL (primary store)
App --> [ioredis] --> Redis (cache + pub/sub)
App --> [SDK] --> Stripe (payments)
Step 3: ANALYZE PATTERNS
Based on what's connected and how it's used, identify:
Access patterns:
- Read-heavy vs. write-heavy (look at query patterns in ORM usage)
- Real-time vs. batch (WebSocket/SSE presence, cron jobs)
- Request/response vs. event-driven (queue usage, webhook handlers)
Missing layers (flag only when evidence supports the need):
| Signal | Likely Missing | Evidence Required |
|---|---|---|
| Repeated identical DB queries in hot paths | Cache layer (Redis/Memcached) | Same query in 3+ request handlers |
setTimeout/setInterval for deferred work | Job queue (Bull/BullMQ/Celery) | Processing that doesn't need to block the response |
Full-text search via LIKE '%term%' | Search engine (Elasticsearch/Meilisearch) | Text search on >10K rows |
| Large file uploads stored in DB or local disk | Object storage (S3/MinIO) | Binary columns or fs.writeFile for user content |
| Analytics queries on production tables | Analytics DB (Snowflake/BigQuery/ClickHouse) | Aggregation queries mixed with OLTP |
| Multiple services sharing one DB | Event bus or API gateway | 2+ repos writing to same schema |
| No connection pooling | Connection pooler (PgBouncer) | Direct connections in serverless/high-concurrency |
Do not flag something as missing unless the evidence is in the code.
Step 4: WRITE MANIFEST
Output the infrastructure manifest to .planning/infra-manifest.md:
# Infrastructure Manifest
> Generated: {ISO date}
> Project: {project name from package.json or repo name}
## Current Systems
### {System Name} -- {Role}
- **Type**: {database|cache|queue|search|storage|auth|payments|...}
- **Product**: {PostgreSQL 15|Redis 7|Stripe SDK|...}
- **Config**: `{file path}`
- **Connection**: {method -- pooled, direct, SDK, ORM}
- **Used by**: {modules/services that import the client}
(repeat for each system)
## Connection Graph
{ASCII diagram of connections -- use /ascii-diagram conventions}
## Access Patterns
- {Pattern 1}: {evidence}
- {Pattern 2}: {evidence}
## Opportunities
### {Opportunity Title}
- **Signal**: {what in the code suggests this}
- **System**: {what would address it -- e.g., "Redis as cache layer"}
- **Impact**: {what improves -- latency, scalability, separation of concerns}
- **Effort**: low | medium | high
(repeat for each opportunity)
## Multi-Repo Considerations
{If the project references other repos, APIs, or shared databases, note them here.
This section feeds directly into /workspace if the user wants to act on opportunities
that span repos.}
Step 5: RETURN
Present a summary to the user:
- How many systems found
- The connection graph (inline, not just in the file)
- Top opportunities ranked by signal strength
- Whether any opportunities would require multi-repo coordination (suggest
/workspace)
Fringe Cases
- No docker-compose or env files: Scan for hardcoded connection strings in source code.
Many projects connect without formal config files. Check
src/,lib/,config/for connection patterns. Note the absence of externalized config as a finding. - Monorepo with multiple services: Treat each service directory as a separate scan target. Produce one manifest with sections per service. Note shared databases across services.
.planning/does not exist: Create it before writing the manifest.- No infrastructure found: Report that the project appears to be client-only or has no external dependencies. This is a valid finding, not an error.
- Secrets in env files: Never include actual secret values in the manifest. Record the variable name and which system it connects to, not the value.
Contextual Gates
Disclosure: "Auditing infrastructure configuration. No files modified."
Reversibility: green — read-only audit; only writes .planning/infra-manifest.md; undo with rm .planning/infra-manifest.md.
Trust gates:
- Any: full audit, manifest generation, opportunity analysis.
Quality Gates
- Every discovered system has: type, product, config path, connection method
- Connection graph covers all discovered systems
- Opportunities cite specific code evidence (file:line), not speculation
- No secret values appear in the manifest
- Manifest written to
.planning/infra-manifest.md - Multi-repo considerations section populated if cross-repo signals exist
Exit Protocol
---HANDOFF---
- Scanned {N} config files, found {M} external systems
- Key systems: {list top 3-4}
- Top opportunity: {highest-signal opportunity}
- Multi-repo scope: {yes/no -- if yes, suggest /workspace}
- Reversibility: green — delete .planning/infra-manifest.md to undo
---
Signals
- GitHub stars
- 921
- Forks
- 82
- Last commit
- Sep 2026
Advanced
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infra-audit- Source
- github.com/sethgammon/citadel