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westurner | 6 days ago

Though the README is probably enough context, would there be value in an agent skill for this?

https://agentskills.io/what-are-skills

SKILL.md format: https://agentskills.io/specification#skill-md-format

The skill creator skill SKILL.md: https://github.com/anthropics/skills/blob/main/skills/skill-...

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SWE bench involves solving GitHub issues.

Which of the SWE bench agents read from or write to GitHub issues?

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There could be a local cache of remote issues, and/or an overlayfs-like write cache

From https://github.com/GothenburgBitFactory/bugwarrior :

> bugwarrior - Pull tickets from github, bitbucket, bugzilla, jira, trac, and others into taskwarrior

https://github.com/bergercookie/syncall :

> Bi-directional synchronization between services such as Taskwarrior, Google Calendar, Notion, Asana, and more

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ditz is on the filesystem

ditz: https://github.com/jashmenn/ditz :

> Ditz is a simple, light-weight distributed issue tracker designed to work with distributed version control systems like git, darcs, Mercurial, and Bazaar

git-ditz: https://github.com/ihrke/git-ditz

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sqlite-utils does SQLite FTS full text search config. There are additional tables for FTS indexes.

sqlite-utils: https://github.com/simonw/sqlite-utils

sqlite-utils > Configuring full-text search: https://sqlite-utils.datasette.io/en/stable/cli.html#configu... :

  sqlite-utils enable-fts mydb.db documents title summary

  # after subsequent writes
  sqlite-utils populate-fts mydb.db documents title summary

  # rebuild the FTS indexes
  sqlite-utils rebuild-fts mydb.db documents
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One of these mentions sensitivity analysis and basically cache poisoning;

From "I built a memory system for Claude that solves the context loss issue" https://news.ycombinator.com/item?id=45546276 :

- buildautomata_memory_mcp: https://github.com/brucepro/buildautomata_memory_mcp

Notes from "Show HN: Recall: Give Claude memory with Redis-backed persistent context" a few days ago: https://news.ycombinator.com/context?id=45517613 :

- recall: https://github.com/joseairosa/recall

sediment: https://github.com/rendro/sediment :

> Semantic memory for AI agents. Local-first, MCP-native

sediment-benchmark: https://github.com/rendro/sediment-benchmark :

> Benchmark suite for memory systems (Sediment, ChromaDB, Mem0, Letta)

discuss

order

spranab|5 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.

westurner|4 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.

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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