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Building on `codex app-server`: a developer's guide to OpenAI Codex's JSON-RPC interface

Practical, no-fluff reference for building applications, IDE plugins, agents, batch tools, or alternative client harnesses on top of OpenAI Codex's app-server interface.

This guide distills:

What codex app-server actually is on the wire (transports, handshake, framing).
The full method surface (threads, turns, review, MCP) and event/notification stream.
How filesystem-resident features (hooks, subagents, skills, MCP, plugins, AGENTS.md) interact with the protocol.
Where state lives on disk (Codex core state vs. companion state).
The official Python SDK surface — what it covers, what it omits, when to drop to request(...).

A tool earns its surface by replacing complexity the model can't reliably write, not by replacing complexity the model can.

xlsx info and xlsx get clear that bar:

They wrap a Rust calamine parser, streaming, format detection, hidden-sheet handling, formula vs value resolution, range parsing, sparse-cell encoding. Re-implementing that in pandas is a 200–500 line job per task, slow, memory-heavy, and easy to get wrong. The tool is the only sane path.
xlsx analyze clears it too, but for a different reason: it's an LLM call with a curated prompt,

Fine-Tuning Sparse Encoders for Neural Sparse Retrieval: Complete Reproducible Guide (SPLADE, OpenSearch Neural Sparse, CSR)

Fine-Tuning Sparse Encoders for Neural Sparse Retrieval

A Complete Reproducible Guide (SPLADE, OpenSearch Neural Sparse, CSR)

Last updated: 2026-02 Scope: Covers SPLADE / SPLADE++ / SPLADE-v3, OpenSearch Neural Sparse v1–v3 (including inference-free), and CSR (Contrastive Sparse Representation). Includes full training recipes, loss functions, architecture internals, and practical tips.

pen2pptx

Convert .pen design files to PowerPoint presentations.

Installation

npm install pptxgenjs

	---
	name: tla-verification
	description: TLA+/PlusCal formal verification for concurrent protocol design. Use when modeling async systems, checking race conditions, or verifying state machine correctness before implementation.
	---

	# TLA+ Protocol Verification — Pilot's Checklist

	You are a formal verification specialist. You help design, write, debug, and run PlusCal/TLA+ specifications for concurrent systems, with a focus on asyncio-style cooperative concurrency patterns. You know the sharp edges of PlusCal, TLC model checking, and how formal models map to real implementation code.

	## When to Use This Skill

	ok I'd liek to undertsand the memory thoroughly in hermes agent. Start with docs and undertstand hermes agent architecture amnd then dive into memory first in docs and then in code.
	I'd like to deploy memeoy in my applicaytion and i tyhink hermes agent ha really interesting approach to memory

	⏺ Let me explore the codebase systematically - starting with docs, then architecture, then memory implementation.

	Searched for 2 patterns, listed 1 directory (ctrl+o to expand)

	⏺ Good - I can see there's rich documentation. Let me read the architecture doc and memory-related docs in parallel.

	Read 4 files (ctrl+o to expand)

	#!/usr/bin/env bash
	# ──────────────────────────────────────────────────────────────────────
	# litellm supply-chain attack auditor (CVE pending — 2026-03-24)
	#
	# Checks for indicators of compromise from litellm 1.82.7 / 1.82.8
	# which were published to PyPI with a malicious .pth payload that
	# steals credentials, exfiltrates to models.litellm.cloud, and
	# attempts lateral movement via Kubernetes.
	#
	# Safe versions: anything < 1.82.7

	#!/usr/bin/env python3
	"""Extract Claude Code session JSONL to lean readable markdown.

	Usage: python3 claude_session_extract.py <session.jsonl> [output.md]

	If output.md is omitted, writes to <session_id>.md in current directory.
	"""
	import json
	import sys
	from pathlib import Path

	#!/usr/bin/env python3
	"""
	RLM v2 (Recursive Language Model) Module for Smolagents

	Implements DSPy RLM-style strategies for handling large contexts:
	- Peeking: Look at data structure before processing
	- Grepping: Use string/regex matching before LLM calls
	- Partition + Map: Chunk data and process with batched sub-LLM calls
	- Summarization: Hierarchical compression for understanding the whole
	- Hybrid: Combine strategies as needed