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Observability Agent - Intent-based Router · first-fluke | DecimalAI

devopsby first-fluke

Observability Agent - Intent-based Router

first-fluke/oma-observability

Source first-fluke/oh-my-agent ↗License MIT

Intent-based observability + traceability router across layers, boundaries, and signals. Routes to vendor-specific skills via category taxonomy; owns transport tuning, meta-observability, incident forensics.

SkillScore68Maintained

Automatic

DevOps & SRE pack

Last verified 8/17/2026

What it changes

Passed 15 of 22 test cases with the skill, and 12 of 22 without it.

Without skill55%

With skill68%

Checks it passed that the model alone missed

Declares LLM ops and generative AI evaluation as out of scope for this skill.
Identifies data pipeline lineage tracking as out of scope for this observability skill.
States that chaos engineering orchestration and fault injection execution are out of scope.

and 1 more in the full report

4 fixed11 passed either way6 still failing1 broke

Pass-rate lift: +14 pp
Tokens: +149%
Agent turns: 0%
Response time: -7%

Model gemini-3.6-flashMeasured 8/17/2026v315 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/first-fluke-oma-observability/SKILL.md https://app.decimal.ai/s/first-fluke-oma-observability/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add first-fluke-oma-observability, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/first-fluke-oma-observability/ and pins it in .decimal/skills.lock

decimalai skills add first-fluke-oma-observability

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/first-fluke-oma-observability/SKILL.md https://app.decimal.ai/s/first-fluke-oma-observability/SKILL.md

Raw file: /s/first-fluke-oma-observability/SKILL.md ↗ · machine-readable summary at /s/first-fluke-oma-observability.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 4

Prompt

We are building a Retrieval-Augmented Generation (RAG) feature using OpenAI models and vector search. We need to capture prompt token counts, LLM generation latency, prompt versioning, and individual span evaluation scores across our pipeline. Please recommend the appropriate telemetry setup and configuration for this application.

What was checked, without and with the skill
Checked	Without skill	With skill
Declares LLM ops and generative AI evaluation as out of scope for this skill.	Missed	Passed
Directs the user to specialized LLM evaluation platforms such as Langfuse, Arize Phoenix, LangSmith, or Braintrust.	Passed	Passed

case-04 · gemini-3.6-flash · 8/17/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md319 linessingle file

Observability Agent - Intent-based Router

Scheduling

Goal

Route, design, tune, and review observability work across MELT+P signals, layers, boundaries, vendor categories, transport choices, meta-observability, and incident forensics.

Intent signature

User asks for observability, telemetry, OTel, metrics, logs, traces, profiles, SLOs, RUM, APM, incident forensics, trace propagation, transport tuning, or observability-as-code.
User needs vendor/category routing or observability architecture instead of a single vendor's already-covered setup.

When to use

Setting up an observability pipeline (OTel SDK + Collector + vendor backend)
Designing traceability across service and domain boundaries (W3C propagators, baggage, multi-tenant, multi-cloud)
Tuning transport layer (UDP/MTU, OTLP gRPC vs HTTP, Collector DaemonSet vs sidecar topology)
Running incident forensics (6-dimension localization: code / service / layer / host / region / infra)
Selecting a vendor category (OSS full-stack vs commercial SaaS vs high-cardinality specialist vs profiling specialist)
Implementing observability-as-code (Grafana Jsonnet dashboards, PrometheusRule CRD, OpenSLO YAML, SLO burn-rate alerts)
Meta-observability (pipeline self-health, clock skew detection, cardinality guardrails, retention matrix)
Covering the MELT+P signal set: metrics, logs, traces, profiles (OTEP 0239), cost (OpenCost), audit (SOC2/ISO), privacy (GDPR/PIPA)
Migrating off deprecated tools (Fluentd → Fluent Bit or OTel Collector, per CNCF 2025-10 guide)

When NOT to use

LLM ops (prompt versioning, evals, gen_ai span deep dive); use Langfuse, Arize Phoenix, LangSmith, or Braintrust directly
Data pipeline lineage: use OpenLineage + Marquez, dbt test, or Airflow lineage backends
IoT / hardware / datacenter physical-layer telemetry (IPMI, BMC, SNMP); use vendor DCIM tooling (Nlyte, Sunbird, Device42)
Chaos engineering orchestration: use Chaos Mesh, Litmus, Gremlin, or ChaosToolkit (this skill consumes their telemetry; it does not orchestrate chaos)
GPU / TPU infrastructure observability: use NVIDIA DCGM Exporter + Prometheus
Software supply chain (SBOM, attestation): use sigstore (cosign / rekor), in-toto framework, SLSA level attestations
Incident response workflow (on-call rotation, paging, escalation); use PagerDuty, OpsGenie, or Grafana OnCall
Single-vendor setup already fully covered by that vendor's own published skill; invoke the vendor skill directly

Expected inputs

Observability intent, target system, architecture boundary, signals, vendor context, and incident symptoms if any
Existing OTel/collector/vendor configs, dashboards, SLOs, trace/log/metric examples, or deployment topology

Expected outputs

Routed observability guidance, setup/migration/tuning plan, incident-forensics path, alerting/SLO guidance, or observability-as-code recommendations
Transport, meta-observability, privacy, audit, and retention checks
Vendor delegation target when appropriate

Track record

Benchmark

15 / 22

test cases passed, vs 12 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

first-fluke / oh-my-agent

.agents/skills/oma-observability/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v3

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Observability Agent - Intent-based Router

Scheduling

Goal

Intent signature

When to use

When NOT to use

Expected inputs

Expected outputs

Track record

Benchmark

Adoption

SkillSafety

Source

Related skills