guild-learn-explain

A focused explanation of one named code target, such as a file, function, class, or module, based on the project's knowledge graph. A knowledge graph is a structured record of code elements and their relationships.

In plain words
What is it for?
Use it to understand a target's role, dependencies, dependants, layer, or domain, using a path, line range, symbol, or module name. It is read-only and covers one target at a time.
Why use it?
It explains how a specific part works and how it fits into the wider project without starting the investigation from raw source code each time.

Skill for Claude CodeCodex

Part of the guild plugin — 19 skills, 22 commands, 13 agents, 10 hooks, 2 MCP servers shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/lookatitude/guild/learn-explain
Any agent
npx skills add lookatitude/guild --skill learn-explain
Clone the repo
git clone --depth 1 https://github.com/lookatitude/guild

Made for: Claude Code, Codex.

Or install guild, the plugin that ships this one along with the rest of its 19 skills, 22 commands, 13 agents, 10 hooks, 2 MCP servers.

Per session 186 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,988 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00186 $0.01988
Opus 5 $0.00093 $0.00994
Sonnet 5 $0.00037 $0.00398
Haiku 4.5 $0.00019 $0.00199

Measured 2d ago against content hash a1cb07ff0fe2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

guild-learn-explain scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/knowledge/learn-explain/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

When to use it

Use to produce a focused, graph-grounded explanation of one named target — a file, function, class, or module: what it does, how it fits the layer/domain map, what it depends on, and what depends on it. The explanation is built from the KnowledgeGraph (guild:learn-graph) so it reuses the calibrated source_refs + confidence already attached to nodes, rather than re-reading and re-interpreting raw source from scratch.

When not to use it

Not for the cheap-scan map (guild:learn-map), building the graph (guild:learn-graph), narrating the tour (guild:learn-onboard), or per-run diff analysis (guild:learn-diff). Not for a broad cross-graph query — that is the bounded kg-query retrieval path (wired into guild:context-assemble) / guild:wiki-query. Not for ingesting an external source (guild:wiki-ingest). This skill is scoped to one named target at a time.

Required inputs

  • The target identifier — a path, path#Lx-Ly, a symbol name, or a module.
  • .guild/indexes/knowledge-graph.json (guild.knowledge_graph.v1) from guild:learn-graph when present (preferred grounding). The output-locations table is owned by guild:learn-map.
  • If the graph is absent or does not cover the target, the ask-before-deep-scan gate applies before any bounded targeted scan of just that target's file(s) — never a full deep scan to answer one explain.

Output format

  • A focused explanation surfaced to the requester (and usable by guild:context-assemble as a task-dependent source): the target's role, its place in the layer/domain map, inbound (calls/depends_on) and outbound edges, and the key behaviours — each claim citing the graph node's source_refs (path#Lx-Ly) + confidence.
  • On explicit request only, a .guild/wiki/concepts/* page candidate for the target (promotion stays with guild:wiki-ingest — this skill does not self-promote).

This skill is read-only over the codebase; it writes no index and mutates no source.

Read the full file on GitHub · 138 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 138 lines · 186 tokens per session scan A a1cb07ff0fe2

Subscribe to this mod's changes

guild-learn-explain is a skill published in the GitHub repository lookatitude/guild (7 stars, last pushed 2d ago), licensed MIT. It adds 186 tokens to every session and 1,988 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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