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spranab | 6 days ago

Thanks for the excellent references! We actually just added a SKILL.md following the agentskills.io spec based on your suggestion — it's live now: https://github.com/spranab/saga-mcp/blob/master/.skills/saga...

Great point about sqlite-utils FTS. Saga already has full-text search via tracker_search across all entities (projects, epics, tasks, notes), but we're using basic LIKE queries — proper SQLite FTS5 indexes would be a meaningful upgrade for larger projects. Adding it to the roadmap.

The bugwarrior/syncall direction is interesting too. A github_sync tool that pulls issues into Saga's local tracker (and optionally pushes status back) would close the loop between local agent planning and team-visible issue trackers. Definitely something worth exploring.

Re: SWE-bench agents and issue tracking — most SWE-bench agents read the issue description as input but don't write back to GitHub. Having a local structured tracker that an agent can read/write freely (without API rate limits or auth concerns) is exactly the gap Saga fills.

discuss

order

westurner|5 days ago

Np. Still learning about how to reference agentskills from my coding LLM here. Haven't yet worked with MCP or A2A much. I should port instruction prompts from each project's AGENTS.md to reusable skills in .claude/skills/skillname/SKILL.md ; so that I can reference "/skillname" in a prompt to reuse that prompt text.

Copilot looks for agent skills in .claude/skills/, .github/skills/, or .agents/skills/ .

FWIU there are package managers for agent skills?

PAKS: https://github.com/stakpak/paks

vercel-labs/skills: https://github.com/vercel-labs/skills :

  export DISABLE_TELEMETRY=1
  npx skills add vercel-labs/agent-skills
https://skills.sh/docs/cli

A reproducibility, traceability, and auditability concern:

How to show the text of the exact version of the skill referenced in each input prompt in each chat log? GH copilot's JSON export format is a fairly complete trace compared to e.g. Gemini Chat.

.

ElasticSearch (Java) and Meilisearch (Rust) do at least porter stemming before indexing.

MeiliSearch can be used as a vector database for langchain.

meilisearch/meilisearch: https://github.com/meilisearch/meilisearch :

Meilisearch docs > langchain guide > Performing Similarity Search: https://www.meilisearch.com/docs/guides/langchain#performing...

  from langchain.vectorstores import Meilisearch
  from langchain.embeddings.openai import OpenAIEmbeddings
meilisearch/meilisearch-mcp : https://github.com/meilisearch/meilisearch-mcp

openobserve/openobserve : https://github.com/openobserve/openobserve :

> OpenObserve is an open-source observability platform for logs, metrics, traces, and frontend monitoring. A cost-effective alternative to Datadog, Splunk, and Elasticsearch with 140x lower storage costs and single binary deployment. with SQL in Rust

OpenObserve (AGPL) docs > MCP: https://openobserve.ai/docs/integration/mcp/ :

> This capability is supported in the Enterprise edition of OpenObserve

https://github.com/openobserve/openobserve/issues/6804

openobserve docs > Comparison with Alternatives > How does OpenObserve compare to Elasticsearch: https://openobserve.ai/docs/overview/comparison/

meilisearch blog > "What is GraphRAG: Complete guide [2026]" https://www.meilisearch.com/blog/graph-rag :

> How is GraphRAG different from baseline RAG?

> GraphRAG represents one of the different types of RAG, and its retrieval process differs from baseline RAG.

> Baseline RAG is vector search-based, while GraphRAG uses structured relationships to get the end result. GraphRAG can still use vector and full-text search, but relationships drive what gets retrieved.

/? graphrag sqlite fts : https://www.google.com/search?q=graphrag+sqlite+fts