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 inherited-scope-doc-names-may-not-existgit 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/inherited-scope-doc-names-may-not-exist)<a href="https://agentmods.dev/skills/wan-huiyan/agent-traffic-control/inherited-scope-doc-names-may-not-exist"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/inherited-scope-doc-names-may-not-exist/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/inherited-scope-doc-names-may-not-exist"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/inherited-scope-doc-names-may-not-exist.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 | $0.00072 | $0.03142 |
| Opus 5 | $0.00036 | $0.01571 |
| Sonnet 5 | $0.00014 | $0.00628 |
| Haiku 4.5 | $0.00007 | $0.00314 |
Grade A, and why
inherited-scope-doc-names-may-not-exist 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.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Dispatch Schema Probe
Problem
Scope docs and plan docs authored by predecessor sessions routinely cite specific BigQuery paths, table names, and column names as if they were verified facts. Often they're not — authors write from memory, from stale schema snapshots, or from hypothetical schemas that never landed. When you mechanically copy those references into 7 parallel agent prompts and fire them against production data, you burn the entire wallclock window discovering the errors one at a time, mid-run.
The failure mode is costly because:
- Multi-agent dispatches amplify the blast radius. 1 wrong table path × 7 agents × 10 min before each tap-out = 70 agent-minutes of wasted compute before anyone notices.
- Predecessor scope docs carry authority. A scope doc that went through multiple review rounds, PR approvals, and handoff sessions LOOKS authoritative. No one re-checks the dataset paths because "surely someone already did."
- Plausible-wrong > obviously-wrong.
target_2025looks like exactly the kind of column name a training-features table would have.analytics.predictions_daily.predictions_dailylooks like a sensible namespaced path. Both are wrong. Obviously-wrong paths (typos) fail loudly; plausible-wrong paths fail slowly inside agents. - Column-name prefixes are assumed. Predecessor sessions often describe engagement
columns as "the
evt_*family" or "theengagement_*columns" when the actual warehouse naming has no prefix at all (e.g.,page_visit_8_30d,login_1d). - The fix is cheap but the miss is expensive. A 5-minute
bq ls+INFORMATION_SCHEMA.COLUMNSprobe costs <$1 and surfaces 100% of path/name errors before firing. Skipping it costs a full overnight window.
Context / Trigger Conditions
Use this skill when ALL of:
- You're about to dispatch ≥2 parallel agents, Cloud Run jobs, scheduled triggers, or long-running Python scripts that query a data warehouse.
- The prompts / SQL / Python are being generated from a scope doc, plan doc, or handoff document authored in a prior session (not freshly inspected by you).
- The dispatch wallclock is measured in hours (not minutes) — i.e., the cost of mid-run failure is not trivial.
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 First seen · 278 lines · 72 tokens per session scan A 2d08317440ea
inherited-scope-doc-names-may-not-exist is a skill published in the GitHub repository wan-huiyan/agent-traffic-control (3 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 3,142 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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