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/jdanigo/hydraiaWrote 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/jdanigo/hydraia/db)<a href="https://agentmods.dev/commands/jdanigo/hydraia/db"><img src="https://agentmods.dev/badge/commands/jdanigo/hydraia/db/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/jdanigo/hydraia/db"><img src="https://agentmods.dev/badge/commands/jdanigo/hydraia/db.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 | $0.00027 | $0.00199 |
| Opus 5.5 | $0.00011 | $0.00080 |
| Sonnet 5.5 | $0.00005 | $0.00040 |
| Haiku 4.5 | $0.00003 | $0.00020 |
Grade A, and why
db 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 1mo 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
Invoke the hydraia skill forcing the Phase -1 route to performance (DB-shaped). Follow the performance-tuning and db-optimization skills with the db-performance-tuner agent as PRIMARY diagnostician: detect the engine from evidence, collect read-only evidence (EXPLAIN plans, stat views, slow logs — redacting data values), classify findings (index, query shape, N+1, locking, pooling, schema, caching), write the spec with baseline + numeric target, then run the full pipeline. Schema changes follow expand-contract; write-shaped operations are plan tasks, never direct agent actions. Dispatch perf-engineer too only if the evidence points outside the database.
Request: $ARGUMENTS
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.
- 1mo ago First seen · 9 lines · 27 tokens per session scan A d33d838a3ffe
db is a command published in the GitHub repository jdanigo/hydraia (8 stars, last pushed 25d ago), licensed MIT. It adds 27 tokens to every session and 199 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
inventory
Codebase inventory skill — scans the repo for a named pattern (inline SQL, fetch calls, feature flags, debug leftovers, deprecated APIs, etc.) and emits a structured Markdown inventory cross-referenced with framework metadata (routes, handlers). Language-agnostic and pack-driven — the engine matches whatever a YAML…
analyze-misfires
Identify skills injected where not needed, propose regex and description tightening.
bugfix
TDD-driven bugfix workflow: tester writes failing test (RED) → developer fixes (GREEN) → developer refactors (REFACTOR) → reviewer validates. Accepts issue number, description, or both. Asks before pushing and creating a PR; --no-pr commits locally without asking.
data-flow-analysis
Trace how data flows through the system from input to output.
dead-code-scan
Scan for dead code, unused imports, duplicates, and zombie code across the project.
logic-fix-all
Autonomous audit-and-fix — after consent, scan the target, fix every logic issue found (all severities), verify each fix, and report anything unresolved.