context

Builds and refreshes the project context stack before non-trivial engineering work.

20|2|Updated May 16, 2026
One-click install
npx skills add https://github.com/reddb-io/red-skills --skill context-reddb-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context
Source: https://github.com/reddb-io/red-skills/tree/main/plugins/dev/skills/engineering/context
Command: npx skills add https://github.com/reddb-io/red-skills --skill context-reddb-io

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Agents starting non-trivial work in a repository often lack grounding: they miss committed docs, repeat past mistakes, and re-derive knowledge that already exists. This Skill grounds the session by reading project context, recalling memory, mapping the codebase, and capturing durable learnings. ## Core Features & Use Cases - Context Stack Loop: Reads CLAUDE.md/AGENTS.md, domain glossaries, ADRs, and context maps before asking the user to repeat information. - Memory & Graph Recall: Runs targeted memory recall and optional graph ingestion to surface prior decisions, gotchas, and structural relationships. - LLM Wiki & Self-Improvement: Queries a project wiki for durable knowledge, stores durable lessons, and inspects skill telemetry for curation signals. - Use Case: Before implementing a large feature in an unfamiliar monorepo, run this Skill to orient from ADRs, recall prior decisions, zoom out on module boundaries, and report a grounded context posture before writing code. ## Quick Start Ask the agent to get context and onboard to this repository before planning the upcoming change.

Frequently Asked Questions about context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I onboard an AI agent to a new repository?▼

Run the context workflow to read CLAUDE.md or AGENTS.md, the domain glossary in .red/CONTEXT.md, and recent ADRs first. It then recalls prior memory, maps module boundaries, and reports a grounded context posture before any implementation begins.

How to recall prior decisions before making a large code change?▼

Use targeted memory recall with red-skills-memory recall before deep investigation. In graph mode, recall is neighborhood-expanded and supersede-aware; treat hits as historical claims and verify them against the current worktree before relying on them.

Does the context workflow require Memory to be configured?▼

No, Memory is an optimization, not a hard dependency. If Memory is absent, uninitialized, or failing, the workflow falls back to ordinary repository reads and continues with committed docs and ADRs.

When should I run graph ingestion for a codebase?▼

Run red-skills-memory ingest only for large or unfamiliar codebases, impact analysis, onboarding, or repeated cross-session work, and only when Memory is initialized in graph mode. Re-run after large refactors or when the graph is stale.

What learnings should be stored in agent memory?▼

Store only durable operational decisions, root causes, and gotchas using red-skills-memory store. Do not store secrets, personal facts, transient progress, issue or PR numbers, commit SHAs, or task-done logs that go stale quickly.