Borrowing it
Nothing to install: this file belongs to DanWahlin/ai-agent-board. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DanWahlin/ai-agent-board/main/.squad/skills/otel-validation/SKILL.mdgit clone --depth 1 https://github.com/DanWahlin/ai-agent-boardWrote 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/danwahlin/ai-agent-board/otel-validation)<a href="https://agentmods.dev/skills/danwahlin/ai-agent-board/otel-validation"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/otel-validation/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/danwahlin/ai-agent-board/otel-validation"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/otel-validation.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 Anti-Refusal · line 26 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00015 | $0.00191 |
| Opus 5 | $0.00008 | $0.00096 |
| Sonnet 5 | $0.00003 | $0.00038 |
| Haiku 4.5 | $0.00002 | $0.00019 |
Grade A, and why
otel-validation 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 9d 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
Context
Use when auditing Squad usage, delegation quality, model mix, tool-call behavior, or session bottlenecks.
Patterns
- Prefer telemetry/session-store evidence when available.
- Significant work-unit routing accuracy is the primary KPI; raw tool/task counts are supporting evidence.
- Pair telemetry with
.squad/orchestration-log/entries and git history.
Examples
- A drift audit checks whether substantial code changes had corresponding agent delegation and orchestration logs.
- A model-policy audit checks whether routine work stayed on standard/fast models instead of premium reviewers.
Anti-Patterns
- Do not judge delegation quality only by raw tool counts.
- Do not expose private telemetry contents in user-facing summaries.
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.
- 9d ago First seen · 28 lines · 15 tokens per session scan A 7de8d274c262
otel-validation is a skill published in the GitHub repository DanWahlin/ai-agent-board (57 stars, last pushed 15d ago), licensed MIT. It adds 15 tokens to every session and 191 once invoked, about $0.0001 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.
Other skills, from other repositories
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…
haiku
When writing a haiku for this bot, follow these conventions.
fastapi-router-py
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.