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 wan-huiyan/agent-traffic-control --skill db-access-review-subagent-needs-explicit-probe-budgetgit clone --depth 1 https://github.com/wan-huiyan/agent-traffic-controlWrote 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/wan-huiyan/agent-traffic-control/db-access-review-subagent-needs-explicit-probe-budget)<a href="https://agentmods.dev/skills/wan-huiyan/agent-traffic-control/db-access-review-subagent-needs-explicit-probe-budget"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/db-access-review-subagent-needs-explicit-probe-budget/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/wan-huiyan/agent-traffic-control/db-access-review-subagent-needs-explicit-probe-budget"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/db-access-review-subagent-needs-explicit-probe-budget.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 | $0.00077 | $0.01562 |
| Opus 5 | $0.00039 | $0.00781 |
| Sonnet 5 | $0.00015 | $0.00312 |
| Haiku 4.5 | $0.00008 | $0.00156 |
Grade A, and why
db-access-review-subagent-needs-explicit-probe-budget 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 6d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A review subagent with live-DB access needs an explicit probe budget
Problem
You dispatch a general-purpose subagent to review a PR or verify findings, and it has live database /
cloud / Bash access. Unprompted, it does the thorough thing: re-derives every claim from scratch,
running expensive exploratory queries (full-window joins, multi-date sweeps, dry-runs). Two failure
modes follow:
- It runs very long (20–40+ min of wall time on heavy probes), far past what the review needs.
- It can socket-close mid-run. The Agent tool returns
API Error: The socket connection was closed unexpectedlywith a non-zerotool_usescount but no final assistant message — so the agent did real work (11, 20 tool calls) but its verdict was never delivered. The output is lost, and it is not resumable from the orchestrator unless aSendMessage/continue tool is available (often it is not). You've burned the tokens and the wall-clock for nothing.
The root cause is that an unbounded reviewer treats "review this change" as "independently re-establish the entire ground truth," which is exactly the slow, fragile thing — when the orchestrator has usually ALREADY established that ground truth and just needs a second pair of eyes on the diff.
Context / Trigger Conditions
- About to dispatch a code-review / verification / research agent with BigQuery / gcloud / psql / Bash.
- An
Agentresult showsAPI Error: The socket connection was closed unexpectedlyplustool_uses: N (>0),subagent_tokens: 0, and no Strengths/Issues/Assessment in the output. - A dispatched reviewer has no return after ~20–30 min.
- You (orchestrator) already ran the expensive live probes and confirmed the numbers in the PR body.
Solution
Bound review/verify subagents explicitly in the dispatch prompt:
- Hard budget: "Target under ~6 minutes, ≤8 tool calls." A reviewer with a budget self-limits to the high-value checks.
- Forbid the expensive re-derivation: "Do NOT run BigQuery probes / multi-table joins over date
ranges. I already verified — your job is the STATIC diff review + run only the
fast unit test (
pytest …)." Hand it the ground truth as given facts to sanity-check cheaply, not to reproduce. - Prefer several short bounded agents over one open-ended one — diversity of lens at low per-agent risk; if one socket-closes you still have the others.
- On a socket-close: capture the returned
agentId. If aSendMessage/continue tool exists, try resuming for just the verdict (no new probing). If not, re-dispatch a fresh bounded agent — do not assume the lost agent "basically finished."
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.
- 6d ago Changed · +1 lines e170bd288055
- 11d ago First seen · 95 lines · 77 tokens per session scan A c4ded6b1af45
db-access-review-subagent-needs-explicit-probe-budget is a skill published in the GitHub repository wan-huiyan/agent-traffic-control (3 stars, last pushed 6d ago), licensed MIT. It adds 77 tokens to every session and 1,562 once invoked, about $0.0004 per session on Opus 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.
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