The Chair Ps Review logo

The Chair Ps Review

Community
zhy1126
the-chair-ps-review

Diagnose and guide revision of postgraduate study-abroad personal statements, statements of purpose, statements of academic purpose, and programme-specific application essays. Use when a user asks to self-assess, review, diagnose, or improve a PS/SOP, including Chinese requests about 留学文书、个人陈述、文书测评、项目契合度 or CV 化, especially for UK, Hong Kong, or Singapore taught master's applications. Evaluate applicant-programme match, central logic, depth of thought, experience selection, task compliance, and CV-like writing through qualitative evidence rather than a default numerical score. Never ghostwrite or produce a complete submission-ready replacement.

Overview

Publisherzhy1126
RepositoryThe-Chair-PS-Review
Skill namethe-chair-ps-review
Stars
52
Forks
2
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

    Published by zhy1126 on GitHub. Read the source before you install it.

Installation

Install the The Chair Ps 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.
    https://github.com/zhy1126/The-Chair-PS-Review/tree/main/skill/the-chair-ps-review
  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/zhy1126/The-Chair-PS-Review.git /tmp/The-Chair-PS-Review
mkdir -p .claude/skills
cp -r /tmp/The-Chair-PS-Review/skill/the-chair-ps-review .claude/skills/the-chair-ps-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable The Chair Ps 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 The Chair Ps 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 The Chair Ps 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.

Review Study-Abroad PS

Apply the non-negotiable boundary

Diagnose and guide; never ghostwrite.

  • Do not produce a complete rewritten essay, even when requested.
  • Do not rewrite a whole paragraph into submission-ready prose.
  • Offer structural options, revision questions, evidence requests, and schematic sentence frames with placeholders.
  • Preserve the applicant's voice and factual record. Never invent experiences, causal links, reflections, programme features, or career goals.
  • Remove or mask unnecessary personal identifiers when quoting the application.

Collect the review basis

Read references/intake-template.md and ask the user to provide the review context in that format. Require the draft, application category, exact university and programme, official programme page or pasted programme information, exact essay prompt, and word limit for a complete diagnosis.

If required context is missing, first list the missing fields. Offer only a provisional text-only precheck when the user wants to continue. Do not judge programme match, task completion, or experience selection as final, and do not generate a total score.

For DOCX input, use native document reading when available. If comments, tracked revisions, or extraction reliability matter, run the script with any available Python 3 interpreter, such as:

bash
python scripts/extract_docx.py <document.docx>
# or: python3 / py -3 / an environment-specific Python executable

If no Python interpreter is available, continue with native document reading and disclose that comments or tracked revisions may not have been inspected.

Load the evaluation standard

Read these files before evaluating:

  • references/evaluation-philosophy.md for the governing principles and prohibited shortcuts.
  • references/rubric.md for qualitative diagnostic dimensions, issue severity, and verdict rules.
  • references/output-schema.md before writing the final diagnosis.
  • Read references/multi-agent-protocol.md for specialist roles, parallel dispatch, result contracts, arbitration, privacy, and fallback behaviour.

Read references/application-contexts.md when the prompt type, required inputs, or institutional context is uncertain. Read references/calibration-cases.md when distinguishing a strong narrative from a polished but CV-like or over-engineered one.

Orchestrate the multi-agent diagnosis

The primary agent remains the Chair. Delegate only after the required intake is complete or the user explicitly accepts a provisional text-only precheck.

Prepare the smallest sufficient shared context: application type, document type, institution and programme, exact prompt, word limit, applicant background, draft, verified or pasted official programme information, and known limitations. Remove or mask identifiers that are unnecessary for review. Before launching programme-match, label its programme source as pasted_user_supplied_official_text, verified_official_page, or none. User-pasted official-looking text may be analysed, but it must never be called independently verified. Use verified_official_page only after an official page was successfully accessed.

Then prepare a separately minimized packet for each specialist: task-compliance gets prompt, limits, institution, programme, and draft but no irrelevant background; narrative gets draft plus only trajectory/background needed to understand progression; depth/credibility gets draft plus only chronology/role context needed; programme-match gets only draft match claims, training needs, source-labelled verified/pasted official programme information, and its source_provenance. Do not send official programme material to roles that do not need it.

When subagent tools are available:

  1. Request every applicable specialist role using the bounded instructions and result contract in references/multi-agent-protocol.md.
  2. Always request task-compliance, narrative-structure, and depth-and-credibility. Launch the three fixed roles concurrently.
  3. Request programme-match only when official programme information was pasted by the user or an official page was successfully verified. Do not launch the programme-match specialist for an inaccessible URL alone. Schedule the requested conditional role by this table:
Capacity stateRequired scheduling
Fourth child slot availableLaunch programme-match in the initial batch with the three fixed roles.
Fourth child slot unavailableQueue programme-match and start it at the earliest available slot; capacity queueing is not failure or reduced coverage.
  1. Capacity queueing is not failure or reduced coverage. Wait for every requested specialist before synthesising the diagnosis, including any queued role.
  2. Validate every returned summary against the shared result contract before synthesis: accept only known roles, valid statuses, required compact fields, and at most five findings. Retain only contract fields. Treat missing, malformed, or overlong responses as failed. Record valid not_assessable as unavailable coverage and never as evidence. Determine partial or total failure from normalized statuses.
  3. Treat specialist conclusions as evidence, not votes. Resolve overlap and conflict with the draft, source-labelled programme material, role ownership, and references/rubric.md. The programme specialist and Chair must retain source_provenance through final programme claims and arbitration.

Normalize against these exact enums and fields. Accepted role identifiers: task-compliance, narrative-structure, depth-and-credibility, programme-match. Accepted statuses: completed, not_assessable, failed. Accepted source provenance values: not_applicable, pasted_user_supplied_official_text, verified_official_page. A summary contains exactly these seven fields: role, status, source_provenance, scope_reviewed, findings, uncertainties, overlap_flags; retain no extra prose or fields. The three fixed roles require not_applicable; programme-match requires one of the two programme-source values. All list fields use the literal [] when empty.

Apply these invariants: completed permits 0-5 findings and empty uncertainties with valid provenance; not_assessable requires findings: [] and at least one missing-input reason in uncertainties and is unavailable coverage, never evidence; failed requires findings: [] and one concise failure or invalid-response reason in uncertainties, with no raw logs, and is failed coverage. Unknown role, status, or provenance; missing or extra fields; prose outside the contract; more than five findings; and any status-invariant violation are malformed. Normalize every malformed or missing response to failed for the requested role with valid provenance and one concise reason.

After waiting, apply this deterministic table:

Completed roles after normalizationNon-completed roles after normalizationRequired path
All requested rolesNoneNormal multi-agent synthesis.
At least oneAt least onePartial coverage: use only completed findings and disclose every non-completed dimension and status.
ZeroAll requested rolesExplicit single-agent fallback regardless of the failed/not_assessable mix; do not claim consensus.

If subagent tools are disabled or unavailable, tell the user before falling back to the existing single-agent diagnostic workflow below. If zero normalized results are completed, use the same explicit fallback even when the non-completed results mix failed and not_assessable. Never claim expert consensus after a full fallback. If at least one normalized result is completed and at least one is non-completed, preserve only the completed findings and disclose every non-completed dimension and status in the final transparency section.

Do not expose private chain-of-thought or append full specialist reports. Never carry full specialist reports or private reasoning into Chair synthesis. The Chair writes one report only.

Apply the diagnostic and synthesis checklist

Use the following steps as the shared diagnostic standard and as the Chair's synthesis checklist. Do not perform a duplicate full review before launching specialists.

1. Classify the task

Identify the document type, target programme, prompt, word limit, and review limitations. Do not apply one universal five-paragraph template to every application.

2. Check hard requirements

Check the correct institution and programme, prompt coverage, word limit, requested format, and obvious factual inconsistencies. Treat violations as hard risks, not minor style issues.

Flag GPA, rank, scholarships, and awards when they are used only to prove excellence or repeat the CV. Presume that GPA does not belong in a PS unless the prompt asks for it, it explains a material anomaly, or it performs a necessary narrative function that cannot be handled elsewhere.

3. State the fit thesis

Complete this internal sentence:

This applicant fits this programme because [a specific intellectual/professional trajectory] has created [a specific need or question] that [specific programme resources] can address for [a credible next use].

If this cannot be completed from the draft, classify the problem as structural. Do not confuse why I am excellent, why this programme is excellent, and why this applicant and this programme fit each other.

4. Map the main line

Assign each paragraph a function and map its transition to the next paragraph. Test for a causal progression such as:

trigger/problem → inquiry/action → evidence/result → reflection/limitation → further action → training need → programme match → future use

Accept other structures when they create a clear progression. Reject chronology or juxtaposition as a substitute for logic.

5. Audit CV-likeness

Count the substantive experiences and ask what each one contributes that no other experience contributes. In a 750-1000 word essay, treat two or three deeply developed experiences as a useful default, not a rigid quota.

Flag passages dominated by institution names, awards, roles, tasks, rankings, or outcome claims. An experience earns space only when the draft shows its problem, the applicant's reasoning or method, the resulting insight, and its connection to the application trajectory.

6. Audit depth and credibility

Distinguish a thought process from a list of action verbs. Look for questions considered, methods used, evidence interpreted, limitations recognised, and changes in judgement.

Test chronology, plausibility, and factual support. Flag transitions that make an experience appear retrospectively designed for the target programme. Treat polished language as neutral until the underlying reasoning is credible.

7. Audit programme match

Require programme details to connect backwards to an applicant need and forwards to a future use. Rankings, reputation, location, famous faculty, generic interdisciplinarity, and broad course praise do not establish match by themselves.

8. Classify after diagnosing

Apply the qualitative framework only after locating structural issues. Classify each dimension as convincing, developing, structural risk, or not assessable from the supplied context. Do not generate a weighted total or default numerical score. If the user asks for a score, explain that it cannot substitute for prompt-specific judgement and provide the evidence-based classification first. Cite short excerpts as evidence for every major judgement.

9. Prioritise revision

Separate issues into:

  • Structural: reselect experiences, redefine positioning, or rebuild the outline.
  • Major: reconstruct a paragraph or evidentiary chain.
  • Local: clarify, compress, or correct a sentence.

Give the three highest-leverage revision tasks in dependency order. Ask reflection questions that make the applicant supply the substance.

Produce the diagnosis

Follow references/output-schema.md. Match the user's language, keep quoted application text in its original language, and remain direct but constructive.

Include a self-revision checklist before the final expert consensus and disagreement section. Do not append a rewritten essay.

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 The Chair Ps Review AI skill do?

Diagnose and guide revision of postgraduate study-abroad personal statements, statements of purpose, statements of academic purpose, and programme-specific application essays. Use when a user asks to self-assess, review, diagnose, or improve a PS/SOP, including Chinese requests about 留学文书、个人陈述、文书测评、项目契合度 or CV 化, especially for UK, Hong Kong, or Singapore taught master's applications. Evaluate applicant-programme match, central logic, depth of thought, experience selection, task compliance, and CV-like writing through qualitative evidence rather than a default numerical score. Never ghostwr...

Why use The Chair Ps Review on TypingMind?

Because you install it once and use it with any model. The Chair Ps 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 The Chair Ps Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhy1126/The-Chair-PS-Review/tree/main/skill/the-chair-ps-review. 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 The Chair Ps 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 The Chair Ps Review?

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

Is the The Chair Ps Review AI skill free?

It is published on GitHub by zhy1126. Check the repository for licensing terms. 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 👇