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Fluentbit Generator

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akin-ozer
fluentbit-generator

Generate/create Fluent Bit configs — INPUT, FILTER, OUTPUT, parsers, log pipeline.

Overview

Publisherakin-ozer
Repositorycc-devops-skills
Skill namefluentbit-generator
Stars
326
Forks
39
Bundled files
18
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by akin-ozer on GitHub. Read the source before you install it.

Installation

Install the Fluentbit Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/fluentbit-generator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/akin-ozer/cc-devops-skills.git /tmp/cc-devops-skills
mkdir -p .claude/skills
cp -r /tmp/cc-devops-skills/devops-skills-plugin/skills/fluentbit-generator .claude/skills/fluentbit-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fluentbit Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fluentbit Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fluentbit Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Fluent Bit Config Generator

Trigger Guidance

Use this skill when the user asks for any of the following:

  • Create or update a Fluent Bit config (fluent-bit.conf, parsers.conf)
  • Build a log pipeline (INPUT -> FILTER -> OUTPUT)
  • Configure Kubernetes logging with metadata enrichment
  • Send logs/metrics to Elasticsearch, Loki, CloudWatch, S3, Kafka, OTLP, or Prometheus remote write
  • Implement parser, multiline, lua, or stream processing behavior

Do not use this skill for pure validation-only requests; use fluentbit-validator in that case.

Execution Flow

Follow this sequence exactly. Do not skip stages.

Stage 1: Set Working Context and Preflight

Use one of these deterministic command patterns.

bash
# Option A (recommended): run from this skill directory
cd /Users/akinozer/GolandProjects/cc-devops-skills/devops-skills-plugin/skills/fluentbit-generator
python3 scripts/generate_config.py --help
bash
# Option B: run from any cwd with absolute paths
python3 /Users/akinozer/GolandProjects/cc-devops-skills/devops-skills-plugin/skills/fluentbit-generator/scripts/generate_config.py --help

Preflight checks:

  • Confirm python3 is available.
  • Confirm script help loads without errors.
  • If using relative output paths, run from the intended target cwd.

Fallback:

  • If python3 or script execution is unavailable, switch to manual generation flow (Stage 4) using examples/ templates.

Stage 2: Clarification Questionnaire (Explicit Template)

Collect these fields before generation.

Required questions:

  1. Primary use case: kubernetes, application logs, syslog, http ingest, metrics, or custom?
  2. Inputs: which sources and paths/ports (for example tail /var/log/containers/*.log)?
  3. Outputs: destination plugin(s) and endpoint(s) (host, port, uri/topic/index/bucket)?
  4. Reliability/security requirements: TLS on/off, retry expectations, buffering limits?
  5. Environment context: cluster name, environment name, cloud region (if applicable)?

Optional but important:

  1. Expected log format: json, regex, cri, docker, multiline stack traces?
  2. Throughput profile: low/medium/high and acceptable latency?
  3. Constraints: offline environment, missing binaries, read-only filesystem?

If any required answer is missing, ask focused follow-up questions before generating.

Stage 3: Decide Script vs Manual Generation

Use this decision table.

ConditionPath
Request matches a built-in use case and only needs supported flagsScript path (Stage 5)
Request needs uncommon plugin options not represented by script flagsManual path (Stage 4)
Complex multi-filter chain, custom parser/lua logic, or specialized plugin tuningManual path (Stage 4)
Script cannot run in environment (missing Python/permissions)Manual path (Stage 4)
User explicitly requests hand-crafted configManual path (Stage 4)

Supported script use cases:

  • kubernetes-elasticsearch
  • kubernetes-loki
  • kubernetes-cloudwatch
  • kubernetes-opentelemetry
  • application-multiline
  • syslog-forward
  • file-tail-s3
  • http-kafka
  • multi-destination
  • prometheus-metrics
  • lua-filtering
  • stream-processor
  • custom

Always state the decision explicitly, including why the other path was not used.

Stage 4: Manual Generation (When Script Is Not the Best Fit)

  1. Read the closest template in examples/ first.
  2. Read examples/parsers.conf before defining new parsers.
  3. Assemble config in this order:
  • [SERVICE]
  • [INPUT]
  • [FILTER]
  • [OUTPUT]
  • parser definitions (if needed)
  1. Reuse known-good sections from examples and only customize required parameters.
  2. Keep tags and parser references consistent across sections.

Required local template selection:

  • Kubernetes + Elasticsearch: examples/kubernetes-elasticsearch.conf
  • Kubernetes + Loki: examples/kubernetes-loki.conf
  • Kubernetes + CloudWatch: examples/cloudwatch.conf
  • Kubernetes + OpenTelemetry: examples/kubernetes-opentelemetry.conf
  • App multiline: examples/application-multiline.conf
  • Syslog forward: examples/syslog-forward.conf
  • File to S3: examples/file-tail-s3.conf
  • HTTP to Kafka: examples/http-input-kafka.conf
  • Multi destination: examples/multi-destination.conf
  • Metrics: examples/prometheus-metrics.conf
  • Lua filtering: examples/lua-filtering.conf
  • Stream processor: examples/stream-processor.conf
  • Parsers: examples/parsers.conf

Fallback:

  • If no matching example exists, start from scripts/generate_config.py --use-case custom output shape and extend manually.

Stage 5: Script Generation Commands (Deterministic Examples)

Run from skill directory unless output path is absolute.

bash
cd /Users/akinozer/GolandProjects/cc-devops-skills/devops-skills-plugin/skills/fluentbit-generator

# Kubernetes -> Elasticsearch
python3 scripts/generate_config.py \
  --use-case kubernetes-elasticsearch \
  --cluster-name prod-cluster \
  --environment production \
  --es-host elasticsearch.logging.svc \
  --es-port 9200 \
  --output output/fluent-bit.conf

# Kubernetes -> OpenTelemetry
python3 scripts/generate_config.py \
  --use-case kubernetes-opentelemetry \
  --cluster-name prod-cluster \
  --environment production \
  --otlp-endpoint otel-collector.observability.svc:4318 \
  --output output/fluent-bit-otlp.conf

# File tail -> S3
python3 scripts/generate_config.py \
  --use-case file-tail-s3 \
  --log-path /var/log/app/*.log \
  --s3-bucket my-logs-bucket \
  --s3-region us-east-1 \
  --output output/fluent-bit-s3.conf

Fallback:

  • If a required option is unsupported by the script, document that gap and switch to Stage 4.

Stage 6: Plugin Documentation Lookup Fallback Chain

Use this strict order when plugin behavior or parameters are uncertain.

  1. Context7 (first choice)
  • Resolve library id for Fluent Bit docs.
  • Query plugin-specific configuration details.
  1. Official Fluent Bit docs (second choice)
  • Use https://docs.fluentbit.io/manual and plugin-specific pages.
  • Prefer official plugin reference sections over blogs.
  1. Web search (last choice)
  • Use only when Context7 and official docs are unavailable/incomplete.
  • Prioritize sources that quote or link official docs.

When to stop escalating:

  • Stop as soon as required parameters and one validated example are found.
  • If all sources are unavailable, proceed with local examples/ and clearly mark assumptions.

Stage 7: Validation and Fallback Behavior

Primary validation path:

  • Invoke fluentbit-validator after generation.

If validator is unavailable, run local fallback checks:

bash
# Syntax/format smoke check when fluent-bit is installed
fluent-bit -c <generated-config> --dry-run

# Optional execution test
fluent-bit -c <generated-config>

If fluent-bit binary is missing:

  • Perform static checks manually:
    • section headers are valid ([SERVICE], [INPUT], [FILTER], [OUTPUT])
    • required plugin keys exist
    • tag/match patterns align
    • parser names/files referenced correctly
    • no hardcoded credentials
  • Report that runtime validation was skipped and why.

Stage 8: Response Contract

Always return:

  1. Generation path used (script or manual) and reason.
  2. Produced file(s) and key plugin choices.
  3. Validation results (or explicit skip reason with fallback checks performed).
  4. Assumptions, open risks, and what to customize next.

Done Criteria

This skill execution is complete only when all are true:

  • Trigger fit was explicitly confirmed.
  • Clarification questionnaire captured required fields or documented assumptions.
  • Script-vs-manual decision was explicit and justified.
  • Plugin lookup used deterministic chain: Context7 -> official docs -> web (as needed).
  • Commands were provided with cwd-safe examples.
  • Fallback behavior was applied for any missing tools/docs/network constraints.
  • Validation was executed (or skipped with explicit reason and fallback static checks).
  • Final response included generated artifacts, validation outcome, and next customization points.

Local Resources

  • Script: scripts/generate_config.py
  • Templates: examples/*.conf
  • Parsers baseline: examples/parsers.conf
  • Sample output directory: output/

Use these resources first before introducing new structure.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fluentbit Generator AI skill do?

Generate/create Fluent Bit configs — INPUT, FILTER, OUTPUT, parsers, log pipeline.

Why use Fluentbit Generator on TypingMind?

Because you install it once and use it with any model. Fluentbit Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fluentbit Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/fluentbit-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fluentbit Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fluentbit Generator?

As many as you like. As long as a model supports skills, you can use Fluentbit Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fluentbit Generator AI skill free?

Yes. It is published on GitHub by akin-ozer under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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