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Phd Idea Evaluator

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Immortalqx
phd-idea-evaluator

Evaluates a preliminary research idea against a five-dimension framework (Higher, Faster, Stronger, Cheaper, Broader) plus idea-lifecycle and student-capability matching, paradigm-shift probing, and a fatal-flaws audit. Returns a reviewer-style verdict. Use when the user has a draft research idea and asks whether it is worth pursuing, asks to 'evaluate this idea', 'score this idea', 'assess feasibility', 'novelty check', 'is this a good research direction', or before committing to a paper scope.

Overview

PublisherImmortalqx
Repositorymy_codex_skills
Skill namephd-idea-evaluator
Stars
56
Forks
0
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Phd Idea Evaluator 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/Immortalqx/my_codex_skills/tree/main/phd-idea-evaluator
  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/Immortalqx/my_codex_skills.git /tmp/my_codex_skills
mkdir -p .claude/skills
cp -r /tmp/my_codex_skills/phd-idea-evaluator .claude/skills/phd-idea-evaluator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Phd Idea Evaluator 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 Phd Idea Evaluator 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 Phd Idea Evaluator 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.

Idea Evaluator

Overview

This skill evaluates a preliminary research idea from the combined perspective of a top-venue reviewer and an experienced advisor. It scores the idea against five improvement dimensions from the idea-generation guide (Higher, Faster, Stronger, Cheaper, Broader), matches the idea's lifecycle against the user's actual capability and available hours per week, probes whether the idea has paradigm-shift potential, flags fatal flaws, and returns one of three verdicts: Strong Accept, Accept with Revisions, or Reject and Pivot.

The goal is to kill weak ideas before the student invests months, and to shape promising-but-underdeveloped ideas into stronger forms before writing begins.

When to use this skill

  • The user has a draft idea and asks whether it is worth pursuing.
  • The user asks for novelty check, feasibility assessment, or scoring.
  • Before the user commits to a paper scope or starts implementation.
  • The user is comparing two or three candidate ideas and needs a structured trade-off.
  • The user suspects scope creep and wants an external check.
  • The user mentions 'evaluate this idea', 'score this idea', 'assess feasibility', or 'is this a good research direction'.

When NOT to use this skill

  • The user has already implemented the idea and is writing the paper. Use phd-intro-drafter, phd-tech-paper-template, or phd-benchmark-paper-template instead.
  • The user explicitly wants brainstorming of new ideas from scratch. Use plain conversation first, then draw from references/paradigm-shift-probe.md and references/paradigm-examples.md if disruptive framing becomes relevant.
  • The user asks for review of an existing manuscript. Use phd-pre-submission-reviewer.
  • The user asks to evaluate a benchmark contribution specifically. Use phd-benchmark-paper-template in targeted mode.

Core procedure

Step 1: First impression and paper-type positioning

Read the user's idea description. In one paragraph, state whether the idea reads as Novel Problem, Novel Method, or New Setting. Is the story compelling in one sentence? If you cannot write that sentence, the idea itself is probably not yet clear enough for evaluation; ask the user to restate.

Step 2: Fatal-flaws audit (early gate)

See: references/fatal-flaws.md for the ten canonical fatal flaws, each with a detection rule and a defense strategy.

Run the fatal-flaws audit before the scoring steps rather than after them. Identify at most two fatal flaws. For each, state the flaw, cite the detection rule, and recommend a concrete defense.

Short-circuit rule. If any fatal flaw is tagged CRITICAL in the severity taxonomy (single-handedly causes rejection, unfixable within the lifecycle), stop here and emit the verdict directly:

  • Verdict: Reject and Pivot.
  • Output sections 1 (First impression), 2 (Fatal flaws with the CRITICAL flaw), and 8 (Verdict with the flaw-driven rationale) only.
  • Do not run the five-dimension scoring, paradigm-shift probe, feasibility check, or integrity gate. Those would be decoration on a rejection.

If no CRITICAL flaw is found, continue to Step 3.

Step 3: Lifecycle and capability matching

See: references/lifecycle-capability-matching.md for the six-category lifecycle matrix, capability self-assessment rubric, and mismatch recovery strategies.

Map the idea onto one of six categories (Application, Foundational Theory, Cross-Disciplinary, Frontier Exploration, Data-Intensive, Innovative Technique). Match against the user's declared capability (effective hours per week, skill depth, theoretical versus applied strength). Output a mismatch flag if lifecycle is shorter than the user's realistic execution window.

Step 4: Five-dimension scoring

See: references/five-dimensions.md for each dimension's entry strategies, scoring rubric, and worked examples.

Score the idea on each of:

  • Higher: effectiveness and accuracy gains.
  • Faster: efficiency and cost reduction.
  • Stronger: robustness, noise tolerance, generalisation.
  • Cheaper: data, annotation, or solution cost reduction.
  • Broader: cross-domain transplantation or unification.

Score each 1-10 with explicit evidence from the user's stated contribution. Identify the two or three dimensions where the idea has the highest ceiling and recommend emphasising those in the paper.

Step 5: Paradigm-shift probe

See: references/paradigm-shift-probe.md for the four probing principles (First Principles, Elephant in the Room, Technology Cycle, Hamming's Rule) and escalation rules for deeper disruptive framing.

Test the idea against four questions:

  1. Does it challenge a hidden assumption the field takes for granted?
  2. Does it address an elephant-in-the-room problem everyone sees but nobody wants to touch?
  3. Does it ride a technology-cycle shift (for example, LLMs making a previously impractical approach now feasible)?
  4. If this problem solved itself, would the field change meaningfully? (Hamming's Rule)

Two or more yes answers means the idea has disruptive potential. Note that, and recommend revisiting references/paradigm-shift-probe.md plus references/paradigm-examples.md to deepen the disruptive framing before the user commits to a paper story.

Step 6: Feasibility check

Against the user's stated resources (hardware, data access, team size, engineering skills, timeline), assess:

  • Compute risk: does the experiment fit on stated hardware?
  • Data risk: is the required data accessible, or does it need expensive annotation or private sources?
  • Engineering risk: does the implementation match the user's skill stack?
  • Timeline risk: does the estimated end-to-end duration (coding, experiments, writing, revision) fit within the idea's lifecycle?

If any risk is high, flag it explicitly with a suggested mitigation.

Step 7: Integrity gate

Before emitting the verdict, run the checks in the Integrity gate section below.

Step 8: Final verdict

Issue one of three verdicts:

  • Strong Accept: execute now. Two or more dimensions at 8+, no fatal flaws, capability match green, lifecycle fit.
  • Accept with Revisions: pivot the scope per recommendations before starting. Some dimensions weak, fixable flaws, or lifecycle mismatch that can be shortened.
  • Reject and Pivot: do not pursue this version. Dominated by a prior benchmark or method, unfixable capability mismatch, or more than one fatal flaw.

Emit the evaluation in the Output format below.

Integrity gate

Each bullet is tagged with an enforceability class. [inspection] means the LLM can verify the bullet from the produced output alone. [attestation] means the LLM states it has done the check, but the user remains responsible for verification. [user-attest] means the bullet is a user-side rule the skill cannot confirm.

Before returning the verdict:

  1. [inspection] Every dimension score cites specific evidence from the user's stated contribution; no score is "gut feeling".
  2. [inspection] Feasibility claims reference the user's stated resources, not generic assumptions.
  3. [inspection] Novelty claims either cite specific prior work or are labelled "unverified; literature check required".
  4. [inspection] Fatal flaws are specific and actionable; "this might not work" is not a flaw statement.
  5. [inspection] Verdict is consistent with scoring: Strong Accept requires at least two dimensions at 8+ and zero CRITICAL flaws.
  6. [inspection] Paradigm-shift claim cites which probing question was answered positively.
  7. [attestation] Lifecycle prediction is reasoned from the field's recent pace; the user should sanity-check against their own knowledge of the subfield before acting on it.

If any [inspection] check fails, downgrade the verdict and mark the corresponding output section as "needs user attention". For [attestation] bullets, the skill states the check was run and the user confirms the result.

Output format

1. First impression

2. Fatal-flaws audit (early gate)

If any CRITICAL flaw is present, skip sections 3-7 and go to section 8 with verdict Reject and Pivot.

3. Lifecycle and capability match

4. Five-dimension radar

5. Paradigm-shift probe

Disruptive potential: <none, possible, strong>.

6. Feasibility

7. Integrity gate result

8. Verdict

Top three actions to take first: 1. ... 2. ... 3. ...

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 Phd Idea Evaluator AI skill do?

Evaluates a preliminary research idea against a five-dimension framework (Higher, Faster, Stronger, Cheaper, Broader) plus idea-lifecycle and student-capability matching, paradigm-shift probing, and a fatal-flaws audit. Returns a reviewer-style verdict. Use when the user has a draft research idea and asks whether it is worth pursuing, asks to 'evaluate this idea', 'score this idea', 'assess feasibility', 'novelty check', 'is this a good research direction', or before committing to a paper scope.

Why use Phd Idea Evaluator on TypingMind?

Because you install it once and use it with any model. Phd Idea Evaluator 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 Phd Idea Evaluator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Immortalqx/my_codex_skills/tree/main/phd-idea-evaluator. 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 Phd Idea Evaluator?

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 Phd Idea Evaluator?

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

Is the Phd Idea Evaluator AI skill free?

Yes. It is published on GitHub by Immortalqx 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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