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 ivegamsft/basecoat --skill data-tier-auditgit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/data-tier-audit)<a href="https://agentmods.dev/skills/ivegamsft/basecoat/data-tier-audit"><img src="https://agentmods.dev/badge/skills/ivegamsft/basecoat/data-tier-audit/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/ivegamsft/basecoat/data-tier-audit"><img src="https://agentmods.dev/badge/skills/ivegamsft/basecoat/data-tier-audit.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 | $0.00341 |
| Opus 5.5 | $0.00024 | $0.00136 |
| Sonnet 5.5 | $0.00012 | $0.00068 |
| Haiku 4.5 | $0.00006 | $0.00034 |
Grade A, and why
data-tier-audit 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
Data Tier Audit Skill
Review data-layer changes for schema drift, migration safety, query risk, and index health.
USE FOR
- Auditing schema changes and data contract drift
- Reviewing migrations for reversibility and rollout safety
- Checking indexes, pagination, and query plans for performance risk
- Verifying repository and data-access patterns remain consistent
DO NOT USE FOR
- UI copywriting or visual design
- Infrastructure-only changes with no data-layer impact
- Generic code review without a data concern
- Fixing runtime bugs outside the data layer
Workflow
- Compare the proposed schema or migration against existing contracts.
- Verify rollback support, nullability changes, and backfill risk.
- Inspect queries for N+1 patterns, scans, and missing indexes.
- Check data integrity, ownership boundaries, and repository patterns.
- Summarize findings with severity, evidence, and remediation.
Output
Return:
- Verdict: pass, pass with notes, or request changes
- Findings with severity and evidence
- Rollback and backfill risks
- Suggested remediation steps
Related Agent
Use with data-tier agent.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 51 lines · 59 tokens per session scan A e168c3d7ddf5
data-tier-audit is a skill published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 13d ago), licensed MIT. It adds 59 tokens to every session and 341 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 2026-09-03.
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