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 ashish7802/awesome-api-skills --skill jaegergit clone --depth 1 https://github.com/ashish7802/awesome-api-skillsWrote 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/ashish7802/awesome-api-skills/jaeger)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/jaeger"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/jaeger/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/ashish7802/awesome-api-skills/jaeger"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/jaeger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium MCP Rug Pull · line 18 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00000 | $0.00539 |
| Opus 5 | $0.00000 | $0.00269 |
| Sonnet 5 | $0.00000 | $0.00108 |
| Haiku 4.5 | $0.00000 | $0.00054 |
Grade A, and why
jaeger 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jaeger Skill
Open source, end-to-end distributed tracing.
Ecosystem Graph
graph LR
jaeger["Jaeger"]
jaeger -- "depends on" --> opentelemetry
jaeger -- "integrates with" --> grafana
Quick Start
Jaeger receives distributed traces (usually from OpenTelemetry), stores them, and provides a UI to visualize the exact lifecycle of a request as it hops across multiple microservices.
docker run -d -p 16686:16686 -p 4317:4317 jaegertracing/all-in-one:latest
Production Patterns
Trace Sampling
Do not trace 100% of your requests in production. Use probabilistic sampling (e.g., 1%) or tail-based sampling (recording 100% of errors but only 1% of successful requests) to prevent Jaeger's storage backend from imploding.
Architecture & Scaling
Storage Backends
The all-in-one Docker image uses in-memory storage and will lose data upon restart. For production, you must configure Jaeger to use a durable storage backend like Elasticsearch or Cassandra.
Error Recovery
If the Jaeger UI is incredibly slow, it is likely due to the underlying Elasticsearch database struggling to aggregate massive trace volumes. Optimize your ES cluster and ensure you are aggressively rotating old indices.
Security Notes
Jaeger's UI has no built-in authentication mechanism. When deploying to Kubernetes, place it behind an OAuth2 Proxy or an Ingress controller configured with strict IP whitelisting.
Relationships
Prerequisites: opentelemetry
Works Well With: grafana
References
Why use this skill
Use this when your agent works with jaeger — structured patterns beat pasted docs and prevent common hallucinations.
AI pitfalls
- Using outdated SDK or API versions from training data
- Inventing environment variable names
- Omitting error handling and retry logic
Production checklist
- Secrets in environment variables, not source code
- Error handling and logging in place
- Rate limits and timeouts configured
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.
- 9d ago First seen · 62 lines · 0 tokens per session scan A 75046fd2520c
jaeger is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 539 tokens. 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
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
manage-skills
A maintenance workflow for checking whether project verification skills still cover the code and rules that changed during a session.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
systematic-debugging
Structured debugging methodology — use before proposing fixes for any error or failure. Covers: code bugs, build errors, deploy failures, config conflicts, dependency issues, infra problems. Also use when previous fix attempts failed or root cause is unclear.
iii-error-handling
Handle iii engine and SDK errors across Node, Python, Rust, and browser workers. Use when interpreting error codes, retryability, RBAC denial, timeouts, handler failures, or SDK-specific exception surfaces.
debug
Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden.