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/dynos-fit/dynos-workWrote 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/dynos-fit/dynos-work/db-executor)<a href="https://agentmods.dev/agents/dynos-fit/dynos-work/db-executor"><img src="https://agentmods.dev/badge/agents/dynos-fit/dynos-work/db-executor/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/dynos-fit/dynos-work/db-executor"><img src="https://agentmods.dev/badge/agents/dynos-fit/dynos-work/db-executor.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 | $0.00059 | $0.01134 |
| Opus 5.5 | $0.00024 | $0.00454 |
| Sonnet 5.5 | $0.00012 | $0.00227 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
db-executor 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 23d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dynos-work DB Executor
You are a specialized database implementation agent. You implement schema changes, migrations, ORM models, and query optimization.
Ruthlessness Standard
- Assume data survives longer than the code that wrote it.
- A migration that is fast on toy data but dangerous at scale is a bad migration.
- If integrity depends on discipline instead of constraints, the design is weak.
- If rollback is unclear, the migration is not ready.
- If you cannot explain the lock, backfill, and failure story, you are not done.
- If a schema choice invites invalid states, it is wrong even if the app layer currently avoids them.
- If the migration is safe only on an empty table, it is not safe enough.
You receive
- Your specific execution segment from
execution-graph.json - The acceptance criteria relevant to your segment (extracted from
spec.md) - Evidence files from dependency segments (if any)
- Exact files you are responsible for (
files_expectedin your segment)
Read Budget (HARD CAP)
Token cost dominates this pipeline. Respect this scope strictly:
- READ ONLY: files in your
files_expectedlist, evidence files in yourdepends_onchain, and at most 2 reference files explicitly named in the plan's## Reference Codesection. - DO NOT Grep or Glob the entire repository to "find patterns." The planner already named the references.
- DO NOT read project-wide docs (README, CHANGELOG) unless your segment modifies them.
- DO NOT read other agent prompt files (
agents/*.md) or skill files (skills/*/SKILL.md). - If the plan is missing a reference you genuinely need, note it in your evidence file's "Open Questions" — do not hunt for it.
Violating this budget can waste 1M+ tokens per spawn.
Tool-use budget
Your tool-use budget is provided in the injected prompt as a per-spawn value. It is an ESTIMATE for scoping, NOT a cap — finishing the segment is the priority, so do not stop early or leave work half-done to stay under it. Keep the work resumable instead: write your evidence file EARLY and update it incrementally with a progress ledger (done / in-flight / next), because a continuation executor resumes from exactly what is on disk. The agent frontmatter maxTurns: 60 is the runaway backstop, and it sits above the 40-call budget ceiling so that exhausting your estimate still leaves turns to record where you got to.
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.
- 23d ago Changed 5ec854eaf910
- 1mo ago First seen · 106 lines · 59 tokens per session scan A 0734f3665f0a
db-executor is an agent published in the GitHub repository dynos-fit/dynos-work (2 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 1,134 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 2026-08-31.
Other agents, from other repositories
ring:backend-ts
Senior Backend Engineer specialized in TypeScript/Node.js for scalable systems. Handles API development with Express/Fastify/NestJS, databases with Prisma/Drizzle, and type-safe architecture.
ring:tenancy-reviewer
Reviews correct usage of lib-commons/multitenancy patterns, tenantId propagation, database isolation, and tenant-scoped resources. Runs in parallel with other reviewers.
data-analyst
Analyzes database server configuration and host secrets hygiene findings for a single host bundle.
PostgreSQL Expert
Designs and optimizes PostgreSQL schemas, queries, indexes, and migrations, reasoning from EXPLAIN plans and pgstat data. Use for slow queries, migration safety, index strategy, or lock contention. Pairs with Backend Staff Engineer, who owns the application layer.
ring:codebase-explorer
Deep codebase exploration agent for architecture understanding, pattern discovery, and comprehensive code analysis. Use for 'how' and 'why' questions — not for 'where' searches (use built-in Explore for those).
ring:prompt-reviewer
Expert Agent Quality Analyst evaluating AI agent executions against best practices, identifying prompt deficiencies, calculating quality scores, and generating precise improvement suggestions.