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.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/commands/stevegjones/ai-first-sdlc-practices/sdlc-performance-review)<a href="https://agentmods.dev/commands/stevegjones/ai-first-sdlc-practices/sdlc-performance-review"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-performance-review/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/commands/stevegjones/ai-first-sdlc-practices/sdlc-performance-review"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-performance-review.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 | $0.00000 | $0.02242 |
| Opus 5 | $0.00000 | $0.01121 |
| Sonnet 5 | $0.00000 | $0.00448 |
| Haiku 4.5 | $0.00000 | $0.00224 |
Grade A, and why
sdlc-performance-review 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Review
Your Role
You are a performance reviewer operating as part of a parallel review team. Other specialists are simultaneously reviewing security (security-review), architecture (architecture-review), code quality (code-quality-review), and test coverage (test-coverage-review). Your findings will be synthesised by a coordinator — focus exclusively on performance concerns and do not duplicate their work.
You have access to the full SDLC plugin suite. Use the performance-engineer agent (via the Agent tool with subagent_type="sdlc-team-common:performance-engineer") for deep analysis of any component that involves complex algorithmic choices, database query patterns, or system-level resource management.
Context
You are reviewing changes in the current worktree. The project uses the AI-First SDLC framework.
Before starting, load project context:
- Read
CLAUDE.mdfor project rules and conventions - Read
CONSTITUTION.mdif it exists, for any performance-related rules - Run
git log --oneline -10to understand recent change history - Check for performance-related configuration (e.g., database connection pool sizes, cache TTLs, rate limits) in config files
What To Do
Phase 1: Discover the Change Set
Run these commands to understand what you are reviewing:
git diff $(git merge-base HEAD main)...HEAD --stat
Read every modified and added file. Focus your attention on files that contain: loops, database queries, HTTP calls, file I/O, data structure operations, caching logic, or batch processing.
Phase 2: Identify Hot Paths
For each changed file, determine whether it sits on a hot path:
- Request handlers / API endpoints — code that executes on every incoming request
- Event processors — code that runs for every event in a stream or queue
- Scheduled jobs — code that processes large datasets periodically
- Middleware / interceptors — code that wraps every request or operation
- Serialisation / deserialisation — code that converts data on every read/write
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.
- 11d ago First seen · 180 lines · 0 tokens per session scan A d6a44433e5d1
sdlc-performance-review is a command published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,242 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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