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 oborchers/fractional-cto --skill secrets-and-configuration-managementgit clone --depth 1 https://github.com/oborchers/fractional-ctoWrote 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/oborchers/fractional-cto/secrets-and-configuration-management)<a href="https://agentmods.dev/skills/oborchers/fractional-cto/secrets-and-configuration-management"><img src="https://agentmods.dev/badge/skills/oborchers/fractional-cto/secrets-and-configuration-management/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/oborchers/fractional-cto/secrets-and-configuration-management"><img src="https://agentmods.dev/badge/skills/oborchers/fractional-cto/secrets-and-configuration-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 3 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00083 | $0.02264 |
| Opus 5 | $0.00042 | $0.01132 |
| Sonnet 5 | $0.00017 | $0.00453 |
| Haiku 4.5 | $0.00008 | $0.00226 |
Grade A, and why
secrets-and-configuration-management 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
One Secret Per Service, One Path Everywhere
The most common infrastructure mistake with credentials is over-engineering the separation. Splitting database passwords into a secrets manager, endpoints into a parameter store, and feature flags into environment variables creates three access patterns, three IAM policies, and a Terraform apply every time you change a connection string.
Put everything in one secret per service. One JSON blob containing every environment variable the service needs -- credentials, endpoints, flags, all of it. Store it in a secrets manager with customer-managed encryption. The application reads and parses the secret at startup. Done.
Core Principles
-
One secret per service. Each service has exactly one secret:
/{service}/env. It contains a JSON object with every env var the service needs -- database passwords next to database hosts, API keys next to feature flags. No separation between "secrets" and "configuration." -
Account = environment. The secret path is
/{service}/envin every account. Dev account, prod account, staging account -- same path. The AWS account provides the isolation (seemulti-account-from-day-oneskill). Application code never needs to know which environment it runs in. This mirrors the internal DNS pattern (seenetwork-architectureskill) wheredb.internalresolves to the right database per VPC -- same hostname everywhere, different values per environment. -
Change without Terraform. Update the secret value in the console or CLI, force a container redeploy. No plan/apply cycle for config changes. Terraform creates the secret resource and sets the initial value; the team manages the value thereafter.
-
Customer-managed encryption. Every secret is encrypted with a KMS key you control, not the provider default. This enables key rotation, cross-account access policies, and decryption audit trails.
-
Rotation is a spectrum. Database credentials can auto-rotate if you invest in the rotation Lambda. Third-party API keys (Stripe, SendGrid) rarely rotate in practice. Don't let perfect rotation block shipping. Start with KMS encryption and proper access scoping; add rotation when it matters.
What ships with it
2 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 · 205 lines · 83 tokens per session scan A 0b8d27f44ba8
secrets-and-configuration-management is a skill published in the GitHub repository oborchers/fractional-cto (30 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 2,264 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 2026-08-30.
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