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Mstar Compound Refresh

Community
btspoony
mstar-compound-refresh

Knowledge maintenance 与项目知识 bootstrap:审查/更新/合并/清理 `{KNOWLEDGE_DIR}` 文档,或从代码库提炼 CONCEPTS.md 与基线 knowledge。触发:可合并文档、定期维护、stale scaffolding、显式 bootstrap/refresh。

Overview

Publisherbtspoony
Repositorymstar-harness
Skill namemstar-compound-refresh
Stars
67
Forks
4
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Mstar 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.
    https://github.com/btspoony/mstar-harness/tree/main/skills/mstar-compound-refresh
  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/btspoony/mstar-harness.git /tmp/mstar-harness
mkdir -p .claude/skills
cp -r /tmp/mstar-harness/skills/mstar-compound-refresh .claude/skills/mstar-compound-refresh
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mstar 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 Mstar 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 Mstar 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.

mstar-compound-refresh(知识维护)

Load order

Read mstar-harness-core first. Path symbols → mstar-conventions. On conflict, mstar-harness-core wins.

Purpose

Knowledge documents in {KNOWLEDGE_DIR} age. Code changes, conventions evolve, patterns become obsolete. mstar-compound-refresh audits the knowledge store against the current codebase and makes it trustworthy again.

产物与操作路径

SSOT: mstar-conventions/references/artifact-storage-paths.md。本 skill 仅操作 {HARNESS_DIR}/knowledge/**/*.md + {HARNESS_DIR}/knowledge/README.md(散文,见 Phase 4)+ <repo-root>/CONCEPTS.md;知识关联经 mstar catalog link,不写 status/snapshot metadata。禁止操作 docs/、{PLAN_DIR}/、{ITERATION_DIR}/、{SPECS_DIR}/。

Engine check (when available): run mstar compound validate <doc-path> --knowledge-dir <dir> (or import { scopeGuard, compoundRefreshScope } from "@mstar-harness/engine" in a host hook) to resolve the allowed scope above ({HARNESS_DIR}/knowledge/**, knowledge/README.md, <repo-root>/CONCEPTS.md) and guard every write against it; this scope never authorizes ACTIVE status/snapshot writes. On fail -> do not proceed; fix and re-run. Skill text below remains authoritative when the runtime is absent.

When to use

TriggerExample
mstar-compound detected overlapping docs"Two docs cover N+1 queries — consider refresh"
Scheduled maintenance"It's been a quarter, let's audit knowledge"
Domain refactoredAfter a major module rewrite
Explicit user request/pm compound-refresh performance-issues
Project knowledge bootstrapNo/stale/partial STRATEGY.md, CONCEPTS.md, or {KNOWLEDGE_DIR} — see below

Bootstrap vs refresh

ModeWhenProcedure
Refresh{KNOWLEDGE_DIR} exists; audit accuracy, merge overlaps, delete stale docsThis skill § Process (Phases 1–6)
BootstrapNo knowledge scaffolding, or artifacts are absent/stale enough to warrant full distillation from codebasereferences/project-knowledge-bootstrap.md (7-phase: survey → STRATEGY.md → CONCEPTS.md → baseline knowledge → indexing → harness init → commit)

Read the bootstrap reference on demand; do not paste its body into this SKILL.md.

Maintenance outcomes

For each candidate document, classify into one of five outcomes:

OutcomeMeaningDefault action
KeepStill accurate and usefulNo edit; report reviewed
UpdateCore solution correct, references driftedIn-place edits (paths, module names, code snippets)
ConsolidateTwo+ docs overlap heavily, both correctMerge unique content into canonical doc, delete subsumed
ReplaceOld doc is misleading; known better replacement existsCreate trustworthy successor, then delete old
DeleteNo longer useful, applicable, or distinctDelete — git history preserves it

Guardrail — rejected approaches: a captured rejected approach stays only while the losing idea remains a tempting, meaningful mistake; delete it once obsolete or no longer plausible. Calibration: first real run of this axis — 9 size-divergent knowledge docs (36–78 lines) under {KNOWLEDGE_DIR}/architecture-patterns/ classified at fold time, all kept by future value (evidence: the knowledge-prose-folds plan ## Review Gate Summary → Task evidence); length proved not to be a criterion.

Core rules

  1. Evidence over opinion. Signals are inputs, not a scorecard. Use engineering judgment.
  2. Prefer no-write Keep. Do not update a doc just to leave a review breadcrumb.
  3. Match docs to reality. When code differs from doc, update the doc — not ask whether the code change was "intentional."
  4. Be decisive. When evidence is clear (file renamed, class moved), apply. Only ask PM when genuinely ambiguous.
  5. Avoid low-value churn. Don't edit for typos, polish, or cosmetic changes that don't improve accuracy.
  6. Delete, don't archive — except formal iteration-start §1.6 corpus hygiene (mstar-iteration/references/iteration-corpus-hygiene.md), which moves superseded/redundant knowledge/specs to {HARNESS_DIR}/archived/knowledge|specs/. Outside that gate, git history is the archive; git log --diff-filter=D -- <path> finds deleted docs.

    Not adopted: frozen-archive seal machinery, Archived: triplets, or a verifier — they contradict rule 6 (delete, don't archive; git history is the archive); do not reintroduce.

  7. Evaluate document-set design. Check whether two+ docs overlap and should be consolidated. Redundant docs silently drift apart.

Scope selection

Default: user-guided

  1. Ask PM for scope: "All knowledge docs, a specific category, or a keyword?"
  2. If category: narrow to {KNOWLEDGE_DIR}/<category>/.
  3. If keyword: search frontmatter (module:, tags:, problem_type:) and filenames.

PM-directed: specific scope

PM provides a scope hint (directory name, filename, module name, or keyword). Match in this order:

  1. Directory match under {KNOWLEDGE_DIR}/
  2. Frontmatter field match
  3. Filename match
  4. Content search

Process

Phase 1: Inventory

  1. List all .md files under {KNOWLEDGE_DIR}/ (excluding README.md and index files).
  2. Read frontmatter of each candidate.
  3. Group by category / module for impact clustering.

Phase 2: Assess per doc

For each doc, check:

  1. Referenced code still exists? — grep for file paths, class names, function names mentioned.
  2. Referenced conventions still match? — check against current AGENTS.md, CONCEPTS.md, lint configs.
  3. Solution still the recommended approach? — has a newer pattern superseded it?
  4. Overlap with other docs? — search for same module/tags to find duplicates.
  5. Future decision value? — rationale / alternatives considered / negative guarantees / reintroduction conditions that would still guide a future change → Keep, regardless of length. A completed decision whose body has no future leverage left (one-off fix detail, superseded mechanics) enters the Delete candidate pool. Word count and age remain discovery aids, never criteria.

Phase 3: Classify and act

Classify each doc → Keep / Update / Consolidate / Replace / Delete. Apply changes.

Phase 4: Reconcile the catalog

  1. For every doc whose verdict changed its metadata or lifecycle (Update / Consolidate / Replace / Delete), update its catalog row: mstar catalog update for title/description/path/source hash and for the catalog lifecycle (active / archived / superseded), and mstar catalog link for provenance/supersession relations (identity is never patched; the revision guard is in --help). A doc deleted outside the iteration-start §1.6 archive gate leaves git history as its record — set/keep its catalog lifecycle so the row is not a silent dangling claim.
  2. A knowledge body with no catalog row is a completeness gap, not a pass: register it (mstar catalog register, or reviewed mstar catalog discover + mstar catalog import) or report it. {KNOWLEDGE_DIR}/README.md index tables are not maintained as a register — README is prose.
  3. The knowledge-association carrier is catalog relations (mstar catalog link). Snapshot plans[].metadata.knowledge_refs and root status knowledge metadata are legacy read-only, not write targets.

Phase 5: Report

Produce a maintenance report:

  • Docs reviewed (count)
  • Docs kept (count)
  • Docs updated (list with what changed)
  • Docs consolidated (list with which canonical doc merged into)
  • Docs replaced (list with successor path)
  • Docs deleted (list with reason)
  • Docs flagged for PM review (ambiguous cases)

Phase 6: CONCEPTS.md reconciliation

If CONCEPTS.md exists, reconcile it with the refreshed knowledge:

  • Terms mentioned in updated/replaced docs may need updating in CONCEPTS.md
  • Terms no longer referenced may be candidates for removal

If CONCEPTS.md doesn't exist but knowledge docs contain qualifying domain terms, propose bootstrapping it (full repo-wide seed). Use references/concepts-vocabulary.md from mstar-compound for rules (read on demand — cross-skill path resolution via skill directory).

Cross-skill coordination

mstar-compound-refresh reads vocabulary rules from mstar-compound's references. At runtime, resolve the path from the loaded skill directory:

The CONCEPTS.md vocabulary rules are in the mstar-compound skill at:
references/concepts-vocabulary.md
Read that file from the mstar-compound skill directory before Phase 6.

NOT to do

  • Do not delete docs without checking for inbound links
  • Do not "archive" — delete instead
  • Do not ask PM about mechanical updates (path fixes, renamed modules)
  • Do not change code to match outdated docs — update docs to match code
  • Do not run without PM approval for destructive actions (Delete, Replace)

Evidence

正确结果 = 每篇候选文档有明确 verdict(Keep / Update / Consolidate / Replace / Delete)并落到产物:文档改动 + catalog 行对账(Phase 4:mstar catalog update / register,生命周期 active / archived / superseded)+ catalog relations 知识关联(mstar catalog link;status/snapshot knowledge_refs 仅 legacy 只读) + 维护报告(Phase 5:reviewed / kept / updated / consolidated / replaced / deleted / flagged)+ CONCEPTS.md 对账(Phase 6)。

References

  • 捕获侧规则与 CONCEPTS.md vocabulary 规则 → mstar-compound(Phase 6 前读 references/concepts-vocabulary.md)
  • 路径符号与产物存储 SSOT → mstar-conventions(references/artifact-storage-paths.md)

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 Mstar Compound Refresh AI skill do?

Knowledge maintenance 与项目知识 bootstrap:审查/更新/合并/清理 `{KNOWLEDGE_DIR}` 文档,或从代码库提炼 CONCEPTS.md 与基线 knowledge。触发:可合并文档、定期维护、stale scaffolding、显式 bootstrap/refresh。

Why use Mstar Compound Refresh on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/btspoony/mstar-harness/tree/main/skills/mstar-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 Mstar 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 Mstar Compound Refresh?

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

Is the Mstar Compound Refresh AI skill free?

Yes. It is published on GitHub by btspoony 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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