Ce Explain logo

Ce Explain

OrganizationPopular
EveryInc
ce-explain

Explain how and why something has its current shape, or what happened over a window of work, grounded in evidence. Use when the user asks for an explanation. Use ce-pov for a judgment or recommendation.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-explain
Stars
25.1K
Forks
2.1K
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EveryInc on GitHub. Read the source before you install it.

Installation

Install the Ce Explain AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-explain .claude/skills/ce-explain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Explain in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ce Explain on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ce Explain is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Explain How and Why

Produce an explanation that answers the scoped question and gives its consumer enough understanding for the intended use. The subject and purpose come from the request and available context, whether a person or another workflow supplied them. Ground project behavior in source evidence; distinguish documented rationale, inference, and unknowns.

Done: deliver the explanation with supporting evidence and material unanswered questions, or return the specific blocker. When an artifact is requested, deliver the artifact and its location. Publication is a separate action, not a condition of having explained the subject.

Consumer and interaction

Adapt depth and presentation to the intended readers and use. A person may need a working answer; a calling agent may need a teaching artifact for someone else. Do not infer the output from the caller's identity alone. When contributing to an ongoing workflow, deliver the requested result and leave continuation to its owner, the calling workflow. Do not add destination menus or follow-up offers to that return.

Resolve discoverable facts before asking. Ask only when missing information materially changes the answer and cannot be resolved from the request or evidence. If interaction is unavailable, return the unresolved question and its consequence rather than waiting or inventing an answer. A result may explain verified behavior while reporting that its historical rationale is unknown.

Read references/orchestration.md before grounding, the first blocking question, or subagent dispatch. It defines evidence gathering, tool use, model tiers, and their fallbacks.

Artifact Root

An explainer lands under <root>/explainers/ only when archived to the repo, and learnings may be read under <root>/solutions/. Resolve <root> only when you compose such a path; a scratch-only or external-concept run never composes one. Pass the resolved path to any subagent, not the config.

Resolve the CE artifact root <root> before composing any artifact path.

  • Read docs_root from <repo-root>/.compound-engineering/config.yaml only (<repo-root> = git rev-parse --show-toplevel). Do not read it from config.local.yaml. Unset -> <root> is docs, exactly as before.
  • Validate a set value: a repo-relative directory whose real, symlink-resolved path stays inside the repo and is neither the repo root nor under .git/. Otherwise stop with an error naming docs_root and the value -- never fall back to docs.
  • Use <root> as the sole artifact location: create it if absent, compose each path as <root>/<subdir> with this skill's own subdirectory, and never also read docs.

Execution Flow

Phase 1: Establish the question and use

Read references/intake.md now. It defines how the subject and time window are resolved, the input tokens, and how the delivery form is chosen. Explain only the requested subject. A bare invocation with no recoverable subject needs clarification under the interaction rule above, not an invented topic or default artifact.

Phase 2: Ground

Follow references/orchestration.md for the scoped evidence pass. Use existing evidence when it is adequate and current; check claims whose support is missing, disputed, or affected by source changes.

Create a run directory only when an artifact or an evidence dossier needs one. Use this block before writing either; it rejects a symlink or a scratch root owned by another user:

bash
SCRATCH_ROOT="/tmp/compound-engineering-$(id -u)";
[ ! -L "$SCRATCH_ROOT" ] && (umask 077; mkdir -p "$SCRATCH_ROOT") 2>/dev/null && [ ! -L "$SCRATCH_ROOT" ] && [ -O "$SCRATCH_ROOT" ] && [ -w "$SCRATCH_ROOT" ] || SCRATCH_ROOT="${TMPDIR:-/tmp}/compound-engineering-$(id -u)";
if [ -L "$SCRATCH_ROOT" ]; then echo "unsafe scratch root symlink: $SCRATCH_ROOT" >&2; exit 1; fi;
(umask 077; mkdir -p "$SCRATCH_ROOT") || exit 1;
if [ -L "$SCRATCH_ROOT" ] || [ ! -O "$SCRATCH_ROOT" ]; then echo "scratch root is not owned by the current user: $SCRATCH_ROOT" >&2; exit 1; fi;
chmod 700 "$SCRATCH_ROOT" || exit 1;
RUN_DIR="$SCRATCH_ROOT/ce-explain/$(date +%Y%m%d)-$(openssl rand -hex 3)";
(umask 077; mkdir -p "$RUN_DIR") || exit 1; chmod 700 "$RUN_DIR" || exit 1;
echo "$RUN_DIR";
  • Diff mode. Empty range or missing subject: do not silently explain something else. Report that before explaining an adjacent thing. Use a substitute only when the request permits it or the user agrees; name the substitution in the result and artifact Subject when present. Otherwise return the unresolved scope to the caller.
  • Recap mode. Do not pre-scan, count, or characterize the window in the main conversation. Instead dispatch a generic subagent directly at the extraction tier, seeded with references/agents/work-recap-scout.md and passed the resolved window, repo root, and $RUN_DIR. Empty window: report the absence of activity and finish without an explainer artifact. When the harness exposes no subagent primitive, run the scout inline with its prompt's sources and budgets, still write recap-evidence.md, and form no view of the window until it is done. If dispatch fails, follow the fallback rule in references/orchestration.md.

Phase 3: Compose the explanation

Answer the question using the evidence, preserving material constraints and uncertainty. Before delivery, check every factual claim against its source. A function call does not establish guarantees about its uninspected implementation. Remove unsupported claims or state their uncertainty where they appear, including in diagrams and exercise answers. Choose prose, code, tables, or visuals when they improve understanding; no particular arrangement is required. Keep attribution accurate when explaining work by multiple people. When selecting from more evidence than the requested scope or depth can hold, disclose the selection; never silently present a partial account as exhaustive.

For an answer or material another workflow will incorporate, return that content directly. Each passage must carry the qualifications needed to use it accurately without separate notes. Do not create a standalone artifact unless the intended use needs one.

For a standalone artifact, read references/explainer-html.md or references/explainer-markdown.md at compose time for the selected format's compatibility and metadata requirements. For teaching artifacts, also read references/check-in.md. The run never blocks on the check-in; any exercises are static content in the artifact. Write $RUN_DIR/explainer.html or explainer.md, then deliver an inline summary plus the file path.

Phase 4: Deliver

A delivered answer or local artifact completes the explanation. Do not require a destination choice or manufacture follow-on work. If a destination was requested, read references/destinations.md before acting; it defines each destination and the consent publishing needs. When a calling workflow owns the surrounding document, return the content to it rather than placing or publishing it yourself.

Publishing to ht-ml.app is never headless and never inferred. Naming it is a choice of destination rather than confirmation after its public-publishing warning. If confirmation cannot be obtained, do not publish; preserve the canonical HTML and report its local $RUN_DIR/explainer.html path.

Boundaries

  • Use ce-pov to judge whether an approach should be adopted or changed. Explaining a historical choice is not endorsing it today.
  • Use ce-compound to capture durable project learning. Producing an explanation does not authorize maintaining repo memory.
  • Explain an idea as supplied; generating alternatives and scoping implementation belong to ce-ideate, ce-brainstorm, and ce-plan.
  • A reported failure to diagnose or fix belongs to ce-debug; a factual explanation of current behavior remains here.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ce Explain AI skill do?

Explain how and why something has its current shape, or what happened over a window of work, grounded in evidence. Use when the user asks for an explanation. Use ce-pov for a judgment or recommendation.

Why use Ce Explain on TypingMind?

Because you install it once and use it with any model. Ce Explain is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ce Explain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-explain. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ce Explain?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ce Explain?

As many as you like. As long as a model supports skills, you can use Ce Explain with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ce Explain AI skill free?

Yes. It is published on GitHub by EveryInc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇