Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mattmre/EVOKORE-MCP-PUBLIC --skill architecture-decision-recordsgit clone --depth 1 https://github.com/mattmre/EVOKORE-MCP-PUBLICWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mattmre/evokore-mcp-public/architecture-decision-records)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/architecture-decision-records/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/architecture-decision-records.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02941 |
| Opus 5 | $0.00020 | $0.01470 |
| Sonnet 5 | $0.00008 | $0.00588 |
| Haiku 4.5 | $0.00004 | $0.00294 |
Grade A, and why
architecture-decision-records scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to architecture-decision-records — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Decision Records
Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.
When to Use This Skill
- Making significant architectural decisions
- Documenting technology choices
- Recording design trade-offs
- Onboarding new team members
- Reviewing historical decisions
- Establishing decision-making processes
Core Concepts
1. What is an ADR?
An Architecture Decision Record captures:
- Context: Why we needed to make a decision
- Decision: What we decided
- Consequences: What happens as a result
2. When to Write an ADR
| Write ADR | Skip ADR |
|---|---|
| New framework adoption | Minor version upgrades |
| Database technology choice | Bug fixes |
| API design patterns | Implementation details |
| Security architecture | Routine maintenance |
| Integration patterns | Configuration changes |
3. ADR Lifecycle
Proposed → Accepted → Deprecated → Superseded
↓
Rejected
Templates
Template 1: Standard ADR (MADR Format)
# ADR-0001: Use PostgreSQL as Primary Database
## Status
Accepted
## Context
We need to select a primary database for our new e-commerce platform. The system
will handle:
- ~10,000 concurrent users
- Complex product catalog with hierarchical categories
- Transaction processing for orders and payments
- Full-text search for products
- Geospatial queries for store locator
The team has experience with MySQL, PostgreSQL, and MongoDB. We need ACID
compliance for financial transactions.
## Decision Drivers
- **Must have ACID compliance** for payment processing
- **Must support complex queries** for reporting
- **Should support full-text search** to reduce infrastructure complexity
- **Should have good JSON support** for flexible product attributes
- **Team familiarity** reduces onboarding time
## Considered Options
### Option 1: PostgreSQL
- **Pros**: ACID compliant, excellent JSON support (JSONB), built-in full-text
search, PostGIS for geospatial, team has experience
- **Cons**: Slightly more complex replication setup than MySQL
### Option 2: MySQL
- **Pros**: Very familiar to team, simple replication, large community
- **Cons**: Weaker JSON support, no built-in full-text search (need
Elasticsearch), no geospatial without extensions
### Option 3: MongoDB
- **Pros**: Flexible schema, native JSON, horizontal scaling
- **Cons**: No ACID for multi-document transactions (at decision time),
team has limited experience, requires schema design discipline
## Decision
We will use **PostgreSQL 15** as our primary database.
## Rationale
PostgreSQL provides the best balance of:
1. **ACID compliance** essential for e-commerce transactions
2. **Built-in capabilities** (full-text search, JSONB, PostGIS) reduce
infrastructure complexity
3. **Team familiarity** with SQL databases reduces learning curve
4. **Mature ecosystem** with excellent tooling and community support
The slight complexity in replication is outweighed by the reduction in
additional services (no separate Elasticsearch needed).
## Consequences
### Positive
- Single database handles transactions, search, and geospatial queries
- Reduced operational complexity (fewer services to manage)
- Strong consistency guarantees for financial data
- Team can leverage existing SQL expertise
### Negative
- Need to learn PostgreSQL-specific features (JSONB, full-text search syntax)
- Vertical scaling limits may require read replicas sooner
- Some team members need PostgreSQL-specific training
### Risks
- Full-text search may not scale as well as dedicated search engines
- Mitigation: Design for potential Elasticsearch addition if needed
## Implementation Notes
- Use JSONB for flexible product attributes
- Implement connection pooling with PgBouncer
- Set up streaming replication for read replicas
- Use pg_trgm extension for fuzzy search
## Related Decisions
- ADR-0002: Caching Strategy (Redis) - complements database choice
- ADR-0005: Search Architecture - may supersede if Elasticsearch needed
## References
- [PostgreSQL JSON Documentation](https://www.postgresql.org/docs/current/datatype-json.html)
- [PostgreSQL Full Text Search](https://www.postgresql.org/docs/current/textsearch.html)
- Internal: Performance benchmarks in `/docs/benchmarks/database-comparison.md`
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 452 lines · 41 tokens per session scan A c19bfddfc0d8
architecture-decision-records is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 2,941 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to architecture-decision-records, differing in 12 lines, and is treated as a copy.
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law-of-common-region
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version-control-strategy
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presentation-deck
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accessibility-test-plan
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diary-study-plan
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