This commit introduces a dedicated AI assistant prompt for documentation
tasks, located at `ai/rsyslog_doc_assistant/base_prompt.txt`. This
prompt standardizes the creation and maintenance of Sphinx-RST
documentation, enforcing best practices for AI ingestion such as
metadata blocks, summary slices, and consistent anchors.
Modifications:
- Created `ai/rsyslog_doc_assistant/base_prompt.txt` with specific
roles, objectives, and workflow checklists for the rsyslog-doc agent.
- Updated the root `AGENTS.md` to include a reference to this new
prompt in the "Quick links for agents" section.
- Created `doc/ai/AGENTS.md` to serve as a guide for agents working
within the `doc/` subtree, explicitly linking to the new prompt and
reinforcing the requirements for metadata and structure defined in
`doc/ai/authoring_guidelines.md`.
These changes ensure that future documentation updates by AI agents will
be consistent, technically accurate, and optimized for both human
readers and RAG systems.
With the help of AI-Agents: Jules
- specify the coding_practices.rst path in doc AGENTS guidance to avoid ambiguity\n- update the doc builder base prompt to point to the explicit RST file\n\nAI-Agent: ChatGPT
- add a pattern entry describing how to document preconditions and defensive fallbacks when splitting helpers out of larger routines
- link the IPv4 tail antipattern to the general guidance and catalog it alongside other practices
AI-Agent: ChatGPT
Improve AI-facing documentation to speed up contributor onboarding after
the gpt5-codex agent update. The guides clarify bootstrap, testing, and
where to find module ownership so agents can work predictably and avoid
expensive CI-only paths.
Impact: documentation-only; no runtime changes. Clarifies when to use
direct test scripts vs. the autotools harness.
This introduces repository-wide AGENTS guides (root, plugins/, contrib/,
tools/, doc/) plus module-focused guides for omelasticsearch, imkafka,
omkafka, and omruleset. It adds metadata templates for core and contrib
modules, initial MODULE_METADATA.yaml files for the Kafka and ES modules,
and a tools/MODULE_METADATA.json for built-ins. The top-level guide now
includes quick links, “priming a fresh AI session,” and explicit
autotools bootstrap instructions (run ./autogen.sh when autotools inputs
change). Testing guidance asks agents to prefer invoking ./tests/*.sh
directly, reserving `make check` for CI reproductions. The doc subtree
gains its own AGENTS.md and an AI doc-builder base prompt to keep edits
consistent. doc/ai/module_map.yaml was annotated to reference per-module
metadata.
Before: scattered or missing agent guidance; unclear when to bootstrap
autotools or run the full harness. After: structured, discoverable docs,
templates, and metadata that align contributor workflow across modules.
* docs: add rsyslog issue assistant build files
Add README and prompt used to build the external Issue Assistant that
drives rsyslog issue assistant which creates great and AI-friendly
GitHub issues. This improves triage readiness and contributor experience
without touching runtime code. The assistant itself is free via the
ChatGPT store and will be linked from documentation and other entry points.
Note that the use of the assistant directly benenfits rsyslog AI First
ecosystem which ensures high quality AI code generation support.
Before: repository had no assistant build inputs.
After: versioned prompt and README are present; assistant is distributed
externally.
Technical details:
- Add ai/rsyslog_issue_assistant/{README.md,base_prompt.txt}.
- Prompt yields ASCII-only JSON metadata plus a Markdown body aligned
with rsyslog templates; selects type, proposes repo, and adds minimal
labels (always includes needs-triage).
- Heuristics default to rsyslog/rsyslog; librelp is chosen when clearly
indicated by the report.
- No runtime, module, ABI, or testbench changes; docs-only assets.
- README points to a web helper page to be linked from CONTRIBUTING.md.
Co-authored-by: gemini-code-assist[bot]
This modernizes contributor experience by simplifying PR templates and
removing the lengthy GDPR disclaimer that often discouraged or confused
new contributors. The update encourages more participation and shows how
responsible AI can be used to improve open source workflows.
Impact: none on runtime behavior; contributor workflow improved.
Before: PR template included long GDPR block; commit message rules were
scattered and partly implicit.
After: PR template is concise, GDPR text removed, and commit assistant
usage is documented across README, CONTRIBUTING, and AGENTS.md.
Technical changes include:
- PR template: drop GDPR notice, add commit-assistant references.
- CONTRIBUTING.md: add explicit commit rules and workflow guidance.
- AGENTS.md: require canonical base prompt and commit-first workflow.
- README.md: point to assistant and updated guidance.
- base_prompt.txt: enforce "Findings:" colon format.
- Minor formatting corrections in comments.
This introduces the first building block of our "responsible AI First"
approach. The base prompt defines how the rsyslog commit assistant
supports contributors in crafting consistent, high-quality commit
messages, thereby lowering entry barriers and improving project-wide
quality.
Impact: contributor workflow only, no runtime effect.
Technically this adds a structured prompt file under
ai/rsyslog_commit_assistant/. It codifies message format, workflow
checks, and error handling guidance. No rsyslog core code, config, or
tests are affected. This is a purely contributor-facing addition that
can evolve independently of runtime behavior.
Refs: https://www.rsyslog.com/clarifying-ai-first-what-it-really-means-for-rsyslog/
GPT 5 has problems accessing the RAG which GPT 4 did not have. But it
may also be that GPT 4 simply did not tell us about these problems.
My guess is that the new anti-hallucination algo is at work here. This
is generally good, albeit it broke the rsyslog Assistant a bit.
As a short-term measure we have enabled it to search rsyslog.com. That
will still remove all the garbagged. My first impression is that this
might be even better than a RAG based system with larger files. This
needs to be seen in practice now.
see also https://github.com/rsyslog/rsyslog/issues/5903
* AI tools: Add directory for future AI/ML tooling
Establish a new top-level directory, `ai/`, to serve as a consistent
location for future Artificial Intelligence and Machine Learning tools
that will work alongside rsyslog.
These tools are intended to run as separate processes, external to the
rsyslog daemon, to ensure the core remains stable and performant.
This commit contains only the empty directory and a README.md file
describing the vision and purpose.
