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Ce Compound Refresh

OrganizationPopular
EveryInc
ce-compound-refresh

Refresh the repo's captured learnings against the current codebase. Use when auditing stale, overlapping, superseded, or drifted learnings; avoid general refactor, debugging, or code review unless the learnings store is explicit.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-compound-refresh
Stars
25.1K
Forks
2.1K
Bundled files
15
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.

  • 15 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 Compound Refresh 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-compound-refresh .claude/skills/ce-compound-refresh
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Compound Refresh 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 Compound Refresh 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 Compound Refresh 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.

Compound Refresh

Audit the learnings under <root>/solutions/ against the current codebase, apply the maintenance actions the evidence supports, and deliver a complete per-doc report plus committed changes. The report and the corrected document set are the deliverables. The store only compounds value if every doc can be trusted.

Mode

Read references/modes.md now. It reads the mode off the arguments and defines what each mode may apply unattended, the stale-marking fallback, the question tools, and the CONCEPTS.md bootstrap.

Two rules hold in both modes. A failed write is recorded as recommended, and the run continues. And a question is asked through the host's blocking tool, or through the numbered-options fallback that reference defines. It is never silently skipped.

Worth lens

The ordinary refresh judges accuracy: is each doc still true and still distinct. It never deletes an accurate doc for holding knowledge the repo states elsewhere. That second judgment, worth, runs only when the user asked for it and confirmed it. It reads the whole scope against the codebase and can delete accurate docs.

Read the invocation arguments for that intent: the user wants the store cleaned up, culled, pruned, trimmed, upgraded, or brought to the capture bar, in any wording, rather than checked for drift. When the intent is present, state the reading back and confirm before any investigation:

text
You asked to clean up the learnings. Which do you want?
1. Delete or shorten docs the codebase already explains. A doc goes when a test, a code comment, or the instructions file states the same reasoning, and every cut quotes that file. Drift is fixed too. Scope: <scope>.
2. Fix drift only. Stale paths and links, duplicate docs, and guidance the code no longer supports. Nothing accurate is deleted.

On option 1, read references/worth-audit.md before Investigate; it adds the bar, the evidence rule, and the routing. On option 2, or when the intent is absent, do not read that reference and do not apply its test; the accuracy refresh is the whole run. Non-interactive mode cannot confirm, so references/modes.md states what an inferred intent does there.

Artifact Root

Resolve <root> when you first compose a <root>/solutions/ path. Pass the resolved <root>/solutions/ path to any subagent, not the config. Every subagent spawn omits the mode parameter, so the user's permission settings apply.

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.

Scope

Candidates are the .md files under <root>/solutions/, excluding README.md and anything under _archived/. A hint that matches nothing never widens the scope. Read references/scope.md for the narrowing strategy, what each mode does on a miss, the empty-store message, triage order, and the README-row cleanup each action carries.

Investigate

Read references/investigate.md for the staleness dimensions, auto-memory rules, subagent roles, and category-shape notes.

Check each learning against the current codebase, then check the set for overlap, supersession, and contradiction. A contradiction misleads actively, so it outranks individual staleness.

A knowledge-track learning sometimes points at a guidance file it names or links, such as a skill's SKILL.md, a runbook, or an instruction file. Compare only guidance the learning names. Never search the guidance layer for one.

Every investigation subagent's prompt carries that reference's three Subagent prompt clauses verbatim. Two are search tools and auto-memory. The third is this:

If the learning is knowledge-track and names or links a guidance file (a skill's SKILL.md, a runbook, a root instruction file), read that file and, when it states a different order or a contradictory rule for the same procedure, return both conflicting quotes plus which side current code follows — or that code witnesses neither. Read only guidance the learning names; do not search for one, and do not edit it.

Classify

Every doc gets exactly one outcome: Keep, Update, Consolidate, Replace, or Delete. A doc is never archived in place: there is no _archived/, since version history is the archive.

Read references/classify.md before assigning any of them. It defines each outcome's meaning, the Update/Replace boundary, the auto-delete rule and its pre-checks, the relocation and split rules, the retrieval-value test, unverifiable-is-not-false, pattern docs, and what interactive mode asks.

Two boundaries hold whatever the evidence says. This skill never changes product code. A claim about current mechanics follows current code, but independently supported guidance does not become false merely because implementation stopped satisfying it: classify the doc from the guidance evidence and report the implementation conflict as a potential product regression. And when a learning contradicts guidance, the refresh reports that; it must never edit a skill, runbook, or instruction file.

Execute

Read references/per-action-flows.md and follow the section matching each doc's classification, one flow per doc. It defines the criteria, the relocation and split procedures, the replacement subagent contract, and citation cleanup.

Vocabulary Capture

After the per-doc actions, reconcile the domain terms flagged during investigation with CONCEPTS.md. Read references/concepts-vocabulary.md unconditionally. Its qualifying criteria are non-obvious, so a "nothing qualifies" judgment reached without reading it is a shortcut, not a result.

Edits apply silently in every mode. The report's CONCEPTS.md line records what the scan found, including "scanned, no qualifying terms".

Report

Print the full report as markdown. It is the deliverable, not an internal summary, and in non-interactive mode it is the only one. Keep it self-contained and never abbreviated, split into Applied and Recommended. Read references/report.md for the summary block, per-file detail, and what belongs under Recommended.

Commit

Skip if nothing changed. Otherwise stage only the files this refresh modified, and commit in the repo's convention. Read references/commit.md for the per-mode branch decision and the git-failure fallback.

Discoverability Check

After the report, check that the project's instructions would lead an agent to <root>/solutions/ before working in a documented area. Do this every time: the store only compounds value when agents can find it. Read references/discoverability.md for what the reader must learn, the smallest-addition rule and its tone, the CONCEPTS.md variant, consent versus a report line per mode, and folding a late edit into the commit.

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 Compound Refresh AI skill do?

Refresh the repo's captured learnings against the current codebase. Use when auditing stale, overlapping, superseded, or drifted learnings; avoid general refactor, debugging, or code review unless the learnings store is explicit.

Why use Ce Compound Refresh on TypingMind?

Because you install it once and use it with any model. Ce Compound Refresh 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 Compound Refresh in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-compound-refresh. 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 Compound Refresh?

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 Compound Refresh?

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

Is the Ce Compound Refresh 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.

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