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 aspectrr/deer --skill observability-logs-searchgit clone --depth 1 https://github.com/aspectrr/deerWrote 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/aspectrr/deer/observability-logs-search)<a href="https://agentmods.dev/skills/aspectrr/deer/observability-logs-search"><img src="https://agentmods.dev/badge/skills/aspectrr/deer/observability-logs-search.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.01333 |
| Opus 5 | $0.00021 | $0.00666 |
| Sonnet 5 | $0.00008 | $0.00267 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
observability-logs-search 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 8d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logs Search
Search and filter logs to support incident investigation. The workflow mirrors Kibana Discover: apply a time range and
scope filter, then iteratively add exclusion filters (NOT) until a small, interesting subset of logs remains. Use
ES|QL only (POST /_query); do not use Query DSL.
Parameter conventions
| Parameter | Type | Description |
|---|---|---|
start |
string | Start of time range (Elasticsearch date math, e.g. now-1h) |
end |
string | End of time range (e.g. now) |
kqlFilter |
string | KQL query string to narrow results |
limit |
number | Maximum log samples to return (e.g. 10–100) |
groupBy |
string | Optional field to group the histogram by (e.g. log.level, service.name) |
Context minimization
Keep the context window small. In the sample branch of the query, KEEP only a subset of fields; do not return full documents by default.
Recommended KEEP list for sample logs:
message, error.message, service.name, container.name, host.name, container.id, agent.name,
kubernetes.container.name, kubernetes.node.name, kubernetes.namespace, kubernetes.pod.name
The funnel workflow
You must iterate. Do not stop after one query. Keep excluding noise with NOT until fewer than 20 log patterns
remain.
- Round 1 — broad: Run a query with only the scope filter and time range.
- Inspect: Look at the histogram, sample messages, and categorized patterns.
- Round 2 — exclude noise: Add
NOTclauses to the KQL filter for dominant noise patterns. - Repeat: Keep adding NOTs until fewer than 20 log patterns remain.
- Pivot (optional): Once the funnel isolates a specific entity, run one more query focused on that entity.
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.
- 8d ago First seen · 101 lines · 42 tokens per session scan A d55c32912224
observability-logs-search is a skill published in the GitHub repository aspectrr/deer (404 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 1,333 once invoked, about $0.0002 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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