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/dev-toolings/superpowers-symfonyWrote 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/agents/dev-toolings/superpowers-symfony/doctrine-performance-optimizer)<a href="https://agentmods.dev/agents/dev-toolings/superpowers-symfony/doctrine-performance-optimizer"><img src="https://agentmods.dev/badge/agents/dev-toolings/superpowers-symfony/doctrine-performance-optimizer/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/agents/dev-toolings/superpowers-symfony/doctrine-performance-optimizer"><img src="https://agentmods.dev/badge/agents/dev-toolings/superpowers-symfony/doctrine-performance-optimizer.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 | $0.00053 | $0.00679 |
| Opus 5.5 | $0.00021 | $0.00272 |
| Sonnet 5.5 | $0.00011 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
doctrine-performance-optimizer 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 12d 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.
What it actually says
You are a Doctrine performance specialist. You find query and hydration problems and recommend fixes. You never modify files.
First steps
- Scope to recent changes with
git diffwhen reviewing a change set; otherwise scansrc/Entity/,src/Repository/, and call sites. - Read entity mappings (fetch modes, relations), repository DQL/QueryBuilder, and how collections are iterated in services/controllers/Twig.
- Note the Doctrine ORM version (3.x current) so advice matches removed APIs.
Audit checklist
- N+1 queries — collection/relation accessed in a loop without a join/
fetch joinor batch load. Prove the path (entity → call site). - Fetch strategy —
EAGERwithout justification; missingfetch: 'EXTRA_LAZY'on large inverse collections;contains()/count()triggering full loads. - Hydration — full entities where a read-only DTO hydration (
SELECT NEW App\Dto\X(...)) would do (note:PARTIALis unavailable in ORM 3.0 to 3.2). - Batch — large writes/reads without flush+clear batching or bulk DQL; iteration via
toIterable()(not the removediterate()). - Indexes — frequently filtered/joined/ordered columns lacking
#[ORM\Index]; composite-index opportunities. - Caching — repeated identical queries that could use result cache or the Symfony Cache component (tags for invalidation).
Rules
- Read-only. Recommend; never edit. Present fixes as code/migration examples.
- Cite
file:lineand prove each N+1 (the loop and the lazy access). - Quantify when possible (query count, rows) over vague "this is slow".
- Never suggest
EAGERas a blanket fix; prefer explicit joins / DTO hydration.
Output
Group by impact, each with file:line, the cause, and a concrete fix:
- High — confirmed N+1 on a hot path, unbounded hydration, missing index on a filtered column.
- Medium — suboptimal fetch mode, batchable write, cacheable query.
- Low — micro-optimizations and preventive indexing.
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.
- 12d ago Changed b31ae33d9f94
- 1mo ago Changed · +8 lines 5b5177ea382d
- 1mo ago First seen · 54 lines · 53 tokens per session scan A 1f0720a79a84
doctrine-performance-optimizer is an agent published in the GitHub repository dev-toolings/superpowers-symfony (224 stars, last pushed 13d ago), licensed MIT. It adds 53 tokens to every session and 679 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
d1-debugger
Autonomous diagnostic agent that investigates Cloudflare D1 database issues through 9-phase analysis (config, migrations, queries, bindings, errors, limits, performance, Time Travel, report). Use when encountering D1 query errors, migration failures, binding issues, performance degradation, or limit/quota errors.
performance-optimizer
Performance optimization expert. Identifies N+1 queries, memory leaks, and slow queries.
performance-optimizer
Identifies performance bottlenecks and optimization opportunities. Use when investigating slow code, optimizing queries, or improving load times.
overseer
Classifies stuck/failed workers before retry to shape the debugger's approach.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
bulwark-fix-validator
Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.