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First Principles Review

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GanyuanRan
first-principles-review

Use when asked for first-principles or Occam's-razor review, or when high-risk decisions involve competing constraints, fallback growth, duplicate owners, or architecture direction risk. Ordinary bug fixes stay on the fast path.

Overview

PublisherGanyuanRan
RepositoryAegis
Skill namefirst-principles-review
Stars
1.2K
Forks
52
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the First Principles Review 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/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/skills/first-principles-review .claude/skills/first-principles-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable First Principles Review 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 First Principles Review 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 First Principles Review 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.

First Principles Review

Purpose

Use this as a lightweight decision review before another Aegis workflow makes a directional choice. It is a compositional skill, not a standalone workflow.

Do not replace brainstorming, systematic-debugging, writing-plans, requesting-code-review, or verification-before-completion. Use it to clean the decision surface those skills will act on.

When this review materially changes the direction, surface Aegis Visibility in natural prose: name the first principle, dropped assumption, smallest sufficient path, or owner / retirement falsifier that changed the decision. Keep it advisory and task-specific; do not turn the lens into approval authority or a generic skill trace.

Use When

  • The user asks for first principles, first-principles thinking, or Occam's razor.
  • A design, plan, or fix has multiple plausible paths and unclear selection criteria.
  • The task has ambiguous goals, competing constraints, or product/architecture direction risk.
  • Debugging is drifting into repeated fixes, fallback growth, duplicate owners, consumer-side patches, or "just add another branch" reasoning.
  • A review finds that the implementation may be locally correct but directionally wrong.

Do Not Use

  • Simple Q&A, status checks, tiny wording/config edits, or clearly bounded single-owner changes.
  • Mechanical execution of an approved plan unless a new directional conflict appears.
  • As a required step for every task, every turn, or every TDD cycle.

Five-Line Review

Answer only what is needed, usually in five short lines:

text
First Principle: What irreducible outcome must this satisfy?
Non-negotiables: What constraints cannot be broken?
Assumptions to Drop: What is habit, inherited shape, or unproven preference?
Smallest Sufficient Path: What is the least complex path that satisfies the first principle?
Escalation Signal: What finding would require spec/design/architecture review?

When the direction depends on a new mechanism or an unfamiliar domain, insert one optional line between path and escalation:

text
Known Prior Art: proven external pattern worth adopting or adapting to project
constraints (cite source), or `unknown` when precedent cannot be verified here

For repair choices, "smallest" means smallest sufficient stable repair, not the smallest textual diff:

text
Minimality Check:
- Smallest textual diff:
- Correct owner:
- Bug class fixed:
- New branch/fallback added:
- Old path retired or scheduled:
- Verdict: sufficient repair | local patch | needs first-principles review

Decision Hygiene Review

Use this escalation only when a design, fix, or plan needs endorsement before it is written into a spec or implementation plan.

Escalate from the five-line review when any of these risk signals appear:

  • multiple plausible paths and no clear selection criteria
  • a new owner, duplicate owner, fallback, adapter, or compat-only carrier
  • an old path that may need delete-first handling or a retirement trigger
  • an unverified assumption that the proposal depends on
  • user language such as "more elegant", "long-term stable", "first principles", or "Occam"
  • a plan could encode the wrong owner, abstraction, compatibility boundary, or retirement schedule
  • an existing object, behavior, responsibility, contract, or relationship may be reinterpreted, narrowed, replaced, or retired, and the proposal could lose a legitimate role or explicit reference while removing invalid authority

Use this compact shape:

text
First-principles invariants:
- Non-negotiable goal:
- Non-negotiable constraints:
- Historical assumptions to delete:

Bounded preservation reminder:
- Evidence-backed behavior that must remain correct:
- Highest-risk counterexample:
- Material unknown / uninspected surface:
- Known explicit anchors / upstream-downstream refs and disposition:
- Role-before-value ambiguity, if any:

Owner / retirement matrix:
- New canonical owner:
- Old owner:
- Compat-only carrier:
- Delete-first / retirement trigger:

Falsification matrix (evaluate in order; stop at the first failing gate):
- Gate 0 - Premise evidence (stage-graded): design stage accepts spec/logic
  refs; implementation/runtime stage requires log, telemetry, or test
  evidence. No evidence -> park as watch-listed, do not enter value
  evaluation.
- Gate 1 - Decidability: no obtainable evidence to judge it -> park as a
  watched falsifier, do not act.
- Gate 2 - Value (any one): lowers future fix probability / shrinks the
  solution set / exposes a missing actionable acceptance criterion / the same
  broken assumption is reused elsewhere. Passing here still requires
  gates 3-5.
- Gate 3 - Cost match: change cost vs business priority, costed at the
  current stage; a valid but deferred counterexample is recorded in the owner
  doc, never parked orally.
- Gate 4 - Goal regression: the constrained solution still meets the original
  goal; re-read the accepted-constraint list at existing checkpoints (design
  review, plan approval, pre-completion verification).
- Gate 5 - Minimize then classify: boundary-shaped destructive claims must be
  minimized first; premise attacks classify as destructive directly.
  Classes: destructive (refactor/retirement track) | supplementary (boundary
  constraint) | watch-listed (falsifier). Conflicting counterexamples are
  decided by business-goal ranking, never by stacking constraints.
- Gate 6 - Adoption trace: the cheapest checkable form (regression test for
  destructive; contract, assertion, or checklist line for supplementary),
  written into the existing owner doc.

Verdict:
- Adopt / revise / reject / needs evidence:
- Blocking gaps:
- Next evidence:

Architecture Integrity Lens

Use this narrower lens when a proposal is executable but may still encode the wrong owner, abstraction, contract boundary, or retirement path. It is advisory method-pack output and may be embedded inside Decision Hygiene Review when that is enough.

Trigger it when any of these appear before approach selection, task decomposition, review, or completion-risk reporting:

  • responsibilities may overlap or a canonical owner is unclear
  • the smallest diff adds a caller-side fallback, guard, adapter, or compat-only carrier
  • an existing source-of-truth or contract could solve the class of problem at a higher level
  • a stale owner, fallback, or old path may keep carrying real logic
  • the work makes a long-term stability, "cleaner architecture", or higher-level simplification claim

Use this compact shape:

text
Architecture Integrity Lens:
- Invariant: What must remain true for the system to be coherent?
- Canonical owner / contract: Which owner, contract, or source-of-truth should carry the behavior?
- Responsibility overlap: What duplicate owner, caller-side patch, fallback, or stale path might still carry real logic?
- Higher-level simplification: Can the problem be solved at the owner / contract / source-of-truth layer instead of by another local branch?
- Retirement / falsifier: What old path retires, or what evidence would disprove this architecture judgment?
- Responsibility / capability boundary: Which invalid authority retires, and
  does the same carrier still serve a separately evidenced legitimate role?
- Verdict: proceed | revise design | split owner | return to baseline | needs ADR/baseline sync

Bounded preservation reminder is a risk-triggered reasoning aid, not a universal artifact or an exhaustive behavior inventory. Inspect the smallest relevant contract, consumer, test, or history evidence. Preserve, rebind, retire, or reject each known explicit reference; state unresolved relationships as unknown instead of re-inferring them or claiming semantic completeness. The Method Pack does not build an authoritative relationship graph, prove referential integrity or input lineage, calculate complete behavior coverage, or issue a runtime gate.

Do not run this lens for every low-risk task. If it does not change the decision surface, return to the active workflow immediately.

Composition

  • With brainstorming: run before approach selection when the request is broad, ambiguous, likely to inherit a poor product shape, or involves owner / retirement / fallback / adapter risk. Use Decision Hygiene Review or the narrower Architecture Integrity Lens before recommending or selecting an approach when those signals appear.
  • With systematic-debugging: run after evidence shows repeated fixes, fallback growth, duplicate owners, or consumer-side patching.
  • With writing-plans: run before task decomposition when the plan could encode the wrong owner, abstraction, compatibility boundary, fallback, adapter, or retirement schedule. If the approved spec did not already cover this, use Decision Hygiene Review or the Architecture Integrity Lens before writing tasks.
  • With requesting-code-review: run when review should check direction and owner integrity, not just code quality.
  • With verification-before-completion: use only to name residual directional risk. It does not grant completion authority.

Boundaries

  • Prefer evidence from current project files, baseline docs, tests, logs, and user requirements. If evidence is missing, mark the line as unknown rather than inventing a principle.
  • Keep the result advisory. This skill may recommend escalation, but it does not create authoritative GateDecision, PolicySnapshot, or completion authority.
  • If the five-line review does not change the decision surface, return to the active workflow immediately.

Frequently asked questions

What does the First Principles Review AI skill do?

Use when asked for first-principles or Occam's-razor review, or when high-risk decisions involve competing constraints, fallback growth, duplicate owners, or architecture direction risk. Ordinary bug fixes stay on the fast path.

Why use First Principles Review on TypingMind?

Because you install it once and use it with any model. First Principles Review 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 First Principles Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GanyuanRan/Aegis/tree/main/skills/first-principles-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use First Principles Review?

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 First Principles Review?

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

Is the First Principles Review AI skill free?

Yes. It is published on GitHub by GanyuanRan 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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