The Tool Desk
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Three scorecards, three kinds of technical input
The tools are aimed at quick triage, not at replacing a full review. Each accepts text a reader supplies, then applies heuristics to surface potential issues. The author describes their checks in a September 30, 2026 article on DEV Community.
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| Scorecard | Input described by the author | Examples of issues it may surface |
|---|---|---|
| Agent Health Checker | JSON, YAML, or plain text describing an AI agent configuration | Retries and backoff, timeouts, tracing hooks, secrets in configuration, tool-auth notes, injection-prone tool sinks, evaluations or tests, and reliance on a single LLM without fallback |
| Cloud Bill Smell Detector | A cost CSV, invoice export, or billing JSON exported from your account | Idle-looking line items, missing tags, and transfer-heavy patterns |
| Observability Gap Finder | OpenTelemetry, Prometheus, or Splunk-style snippets, or a service-inventory YAML | Missing service-level indicators, services without tracing, and absent cardinality guards |
Agent Health Checker
This scorecard is for a configuration-level pass over an AI agent. Its checks span reliability controls such as retries, backoff, and timeouts; visibility through tracing hooks; and risk areas such as exposed secrets or tool sinks that may be vulnerable to injection. It also looks for evaluation or test coverage and whether the setup relies on one model without a fallback.
A flagged item is a prompt to inspect the relevant configuration and code, not proof that the system is exploitable or unsafe. A text-based check also cannot establish how the agent behaves at runtime.
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Cloud Bill Smell Detector
The cloud scorecard takes an exported billing file or pasted billing data. Its role is to point toward lines that may warrant investigation, including idle-looking resources, missing ownership tags, or costs dominated by data transfer. It does not connect to a cloud account, according to the author; the reader supplies an export.
That distinction matters: the scorecard can only reason about the information present in the pasted data. A suspicious-looking line needs to be checked against actual resource use, billing context, and the account’s configuration before anyone treats it as waste.
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Observability Gap Finder
This tool is intended for telemetry snippets or a service inventory. The examples the author gives include services lacking tracing, missing service-level indicators, and no cardinality guard. Those are useful prompts for asking whether important services can be followed through a request path, whether reliability is measured in terms users care about, and whether metric labels could create uncontrolled series growth.
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What “client-side” means for pasted data
Banerjee says the tools process pasted input locally in the browser and that the text should not leave the page. He also says the tools are hosted on GitHub Pages and links a public source repository. Those facts are consistent with a client-side design, but static hosting and public source code do not, by themselves, prove what a deployed page sends at runtime. The available account does not independently establish the behavior of the live pages.
Check requests in your browser
- Open the scorecard page, then open your browser’s developer tools and select the Network tab.
- Clear the existing request list so activity from loading the page is easier to distinguish from later activity.
- Paste a non-sensitive sample and run the scorecard.
- Inspect requests generated while processing the sample. Look for requests that include the pasted content, and review destinations and payloads where the browser exposes them.
This is a practical check, not a formal security audit. A clean observation during one test does not prove that every version, browser state, or future deployment behaves identically. Do not paste secrets, credentials, customer data, or other sensitive material unless you have established that the tool and your own environment are appropriate for it.
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Use scores as triage, not certification
The author describes the scorecards as “prioritization aids, not audit stamps.” Their checks are heuristic: they may flag benign patterns, and they may miss real problems. A score is best treated as a way to choose what to examine first before production, not as evidence that a configuration has passed a security, cost, or reliability review.
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- For billing findings, compare the flagged export lines with actual resource activity and the relevant account context.
- For observability findings, verify telemetry in the running system and assess whether it supports the service’s operational needs.
The author says the tools are free browser utilities, with no account connection described as necessary for the cloud scorecard. The linked article also points to a public repository, but the tool pages and repository were not independently verified here. Treat the descriptions and privacy boundary as claims from the author, and make your own checks before relying on them.
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