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 michellemayes/meta-doctor --skill scaffold-doctorgit clone --depth 1 https://github.com/michellemayes/meta-doctorWrote 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/michellemayes/meta-doctor/scaffold-doctor)<a href="https://agentmods.dev/skills/michellemayes/meta-doctor/scaffold-doctor"><img src="https://agentmods.dev/badge/skills/michellemayes/meta-doctor/scaffold-doctor/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/michellemayes/meta-doctor/scaffold-doctor"><img src="https://agentmods.dev/badge/skills/michellemayes/meta-doctor/scaffold-doctor.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 | $0.00116 | $0.03985 |
| Opus 5 | $0.00058 | $0.01992 |
| Sonnet 5 | $0.00023 | $0.00797 |
| Haiku 4.5 | $0.00012 | $0.00398 |
Grade A, and why
scaffold-doctor 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 — 463 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scaffold-doctor
Generates a production-ready "doctor" agent inside the target repo by following
the pattern described in references/architecture.md.
Read that file first — it defines the module table, data flow diagram,
three run modes, and the error-isolation invariant that all generated code must
preserve.
Interactive Flow
Ask questions one at a time, in the order listed below. Use multiple choice wherever possible. Stack questions come first because they determine which modules get generated and which swap-table rows apply.
Q1 — Stack
Ask each sub-question on its own turn.
1a. Language / runtime
1) TypeScript (default)
2) Python
3) Other — describe
1b. LLM provider
1) Amazon Bedrock (default)
2) Anthropic API direct
1c. Scheduler
1) AWS EventBridge + Lambda (default)
2) GitHub Actions
3) POSIX cron / systemd timer
4) Kubernetes CronJob
1d. VCS
1) GitHub (default)
2) GitLab
1e. Tracker
1) Linear (default)
2) Jira
3) GitHub Issues
1f. Chat
1) Slack (default)
2) Microsoft Teams
3) Discord
For any non-default choice, note which swap-table row applies (see
references/swap-table.md) — generation will
follow that row exactly.
Q2 — Product name
What is your application's name? (Used as the doctor's identity in the system prompt, e.g. "Acme Doctor".)
Free text. Store as PRODUCT_NAME.
Q3 — Target repo
What is the owner/name of the GitHub (or GitLab) repo the doctor will read from and open PRs against? (e.g.
acme-corp/backend)
Free text. Store as REPO_SLUG.
Q4 — Log source
Which log groups should the doctor scan? List ARNs or names, one per line. Press enter twice when done. (Default: CloudWatch Logs; describe a different source if applicable.)
Collect as LOG_GROUPS[]. Default lookback: 60 minutes.
Q5 — Data source for audits
Which query tool should runbooks use?
1) AWS Athena (default)
2) Generic SQL (Postgres, MySQL, BigQuery…) — specify driver
3) Prometheus
4) None — runbooks use only log/metric/VCS tools
What ships with it
7 files 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.
- 12d ago First seen · 463 lines · 116 tokens per session scan A fd151499f758
scaffold-doctor is a skill published in the GitHub repository michellemayes/meta-doctor (29 stars, last pushed 2mo ago), licensed MIT. It adds 116 tokens to every session and 3,985 once invoked, about $0.0006 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 2026-08-30.
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