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Academic Letter Architect

Community
Barrios88
academic-letter-architect

Use when writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy, comparative statements, or evidence-based character assessment.

Overview

PublisherBarrios88
Repositorybarrios-skills
Skill nameacademic-letter-architect
Stars
64
Forks
30
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by Barrios88 on GitHub. Read the source before you install it.

Installation

Install the Academic Letter Architect 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/Barrios88/barrios-skills/tree/main/skills/optional/career-and-grants/academic-letter-architect
  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/Barrios88/barrios-skills.git /tmp/barrios-skills
mkdir -p .claude/skills
cp -r /tmp/barrios-skills/skills/optional/career-and-grants/academic-letter-architect .claude/skills/academic-letter-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Letter Architect 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 Academic Letter Architect 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 Academic Letter Architect 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.

Academic Letter Architect

Table of Contents

Purpose

This skill guides the creation of effective academic recommendation letters that provide evidence-based advocacy. Strong letters combine concrete examples, meaningful comparisons, and genuine enthusiasm to differentiate candidates and support their applications for positions, awards, or opportunities.

When to Use

Use this skill when:

  • Student recommendations: Graduate school applications, fellowship applications, job applications
  • Postdoc recommendations: Faculty position applications, grant applications
  • Colleague recommendations: Promotion letters, award nominations
  • Award nominations: Prize nominations, recognition letters
  • Letters of support: Collaboration letters, grant support letters

Trigger phrases: "recommendation letter", "reference letter", "nomination", "write a letter for", "letter of support", "endorse", "vouch for"

Do NOT use for:

  • Personal statements (use career-document-architect)
  • Cover letters to journals (use scientific-email-polishing)
  • Grant proposals (use grant-proposal-assistant)

Core Principles

1. Show, don't tell: Concrete examples beat adjectives

  • ❌ "She is brilliant"
  • ✅ "She independently developed a novel assay that our lab now uses routinely"

2. Comparisons give context: Readers need reference points

  • ❌ "He is a strong student"
  • ✅ "He is among the top 5% of graduate students I've mentored in 20 years"

3. Enthusiasm is evidence: Tone conveys conviction

  • Lukewarm letters damage candidates
  • Genuine enthusiasm must come through

4. Address what matters: Match content to opportunity

  • Academic job: Research potential, teaching, mentorship
  • Industry job: Practical skills, teamwork, adaptability
  • Award: Specific achievements matching award criteria

Workflow

Copy this checklist and track your progress:

Letter Architect Progress:
- [ ] Step 1: Gather context (candidate, opportunity, relationship)
- [ ] Step 2: Collect evidence (specific examples, achievements)
- [ ] Step 3: Draft opening (credibility, relationship, expectation)
- [ ] Step 4: Build body (evidence paragraphs, comparisons)
- [ ] Step 5: Craft closing (strong endorsement, availability)
- [ ] Step 6: Calibrate tone (enthusiasm level, superlatives)
- [ ] Step 7: Final polish (length, format, signature)

Step 1: Gather Context

Identify: Who is the candidate? What opportunity? Your relationship (advisor, collaborator, instructor)? How long have you known them? In what capacity? See resources/methodology.md for information checklist.

Step 2: Collect Evidence

List 3-5 specific examples demonstrating excellence: Research achievements, intellectual contributions, professional qualities, overcoming challenges. Quantify where possible. See resources/methodology.md for evidence types.

Step 3: Draft Opening

Establish your credibility (position, experience). State relationship to candidate (role, duration, context). Set expectation (strong recommendation signal). See resources/template.md for opening structure.

Step 4: Build Body

Structure evidence into 2-4 paragraphs covering different dimensions (research, intellect, character). Include comparative statements ("top 5%", "best I've seen"). Connect evidence to opportunity requirements. See resources/template.md for paragraph templates.

Step 5: Craft Closing

Provide unambiguous endorsement statement. Offer availability for follow-up. Include professional signature with title/contact. See resources/template.md for closing structure.

Step 6: Calibrate Tone

Ensure enthusiasm matches actual assessment. Check superlative use (too many dilutes impact). Verify letter reads as advocacy, not obligation. See resources/methodology.md for calibration guide.

Step 7: Final Polish

Check length (typically 1-2 pages). Ensure formal formatting. Verify all specific claims are accurate. Validate using resources/evaluators/rubric_academic_letter.json. Minimum standard: Average score ≥ 3.5.

Letter Structure

Opening Paragraph

Purpose: Establish credibility and relationship

Elements:
1. Your identity and position
2. How you know the candidate (role, context)
3. Duration of relationship
4. Capacity of observation (direct supervision, collaboration)
5. Clear statement of recommendation

Example: "I am writing to provide my strongest recommendation for Dr. Jane Smith for the position of Assistant Professor. As the Director of the Structural Biology Center at X University, I have had the privilege of working closely with Jane for the past four years, first as her postdoctoral mentor and subsequently as a research collaborator. During this time, I have observed her exceptional scientific abilities, intellectual creativity, and professional maturity firsthand."

Body Paragraphs

Purpose: Provide evidence-based assessment

Paragraph 1: Research/Technical Excellence

  • Specific project achievements
  • Technical skills demonstrated
  • Independent thinking
  • Problem-solving ability
  • Publications/outputs

Paragraph 2: Intellectual Contributions

  • Creativity and innovation
  • Scientific insight
  • Critical thinking
  • Ability to ask important questions
  • Conceptual contributions

Paragraph 3: Professional Qualities

  • Work ethic and reliability
  • Collaboration and teamwork
  • Communication skills
  • Mentorship of others
  • Leadership potential

Paragraph 4: Comparative Assessment

  • Direct comparison to peers
  • Ranking in your experience
  • Prediction of future success

Closing Paragraph

Purpose: Summarize and endorse

Elements:
1. Overall assessment statement
2. Specific recommendation (enthusiastic, unambiguous)
3. Prediction for future success
4. Offer of availability for follow-up
5. Professional sign-off

Example: "In summary, Jane is an outstanding scientist with exceptional research abilities, intellectual depth, and professional maturity. I give her my highest and most enthusiastic recommendation without reservation. She will make an excellent faculty member and I am confident she will develop an impactful, independent research program. Please do not hesitate to contact me if you require any additional information."

Tone and Language

Enthusiasm Levels

Highest ("absolutely top"):

  • "My strongest possible recommendation"
  • "Without reservation"
  • "The best I have mentored in 20 years"
  • "Truly exceptional"

Strong ("top tier"):

  • "Highly recommend"
  • "Outstanding"
  • "Top 5-10% of students"
  • "Excellent"

Moderate ("good but not stellar"):

  • "I recommend"
  • "Strong"
  • "Above average"
  • "Solid"

Lukewarm (damaging):

  • "I am pleased to recommend"
  • "Adequate"
  • "Met expectations"
  • "Did fine work"

Comparative Statements

Strong comparisons:

  • "Among the top 2-3 students I've trained in my career"
  • "The most creative thinker I've mentored"
  • "Will outperform 95% of candidates you consider"
  • "Best [X] I've seen in [Y] years"

Weak comparisons (avoid):

  • "One of our better students"
  • "Above average"
  • "Compares favorably to peers"

Specificity Examples

Vague (Weak)Specific (Strong)
"Productive researcher""Published 5 first-author papers including 2 in Nature journals"
"Good communicator""Regularly invited to present at lab meetings and gave a talk at the Gordon Conference"
"Works well with others""Mentored 3 undergraduate students, all of whom went to top graduate programs"
"Technically skilled""Independently established our lab's CRISPR screening platform"

Guardrails

Critical requirements:

  1. Truthfulness: Only write what you genuinely believe. Dishonest letters harm candidates and your reputation.

  2. Evidence-based: Every claim should have a supporting example. "Smart" means nothing without evidence.

  3. Appropriate comparison: Compare to relevant reference class (other postdocs, not all scientists ever).

  4. Match content to opportunity: Emphasize research for academic jobs, practical skills for industry.

  5. Candidate voice preservation: Reflect the candidate's actual achievements, not fabricated ones.

  6. Cultural awareness: US letters are more superlative than other cultures. Calibrate appropriately.

Common pitfalls:

  • ❌ Lukewarm language: "Adequate", "met expectations" - these hurt
  • ❌ No comparisons: Reader can't calibrate "excellent" without context
  • ❌ Generic adjectives: "Brilliant, creative, hardworking" with no evidence
  • ❌ Too short: Brief letters signal lack of enthusiasm
  • ❌ Wrong focus: Research focus for teaching position
  • ❌ Damning with faint praise: "Did everything asked" sounds minimal

Quick Reference

Key resources:

Letter length guidelines:

  • Graduate school: 1-1.5 pages
  • Faculty position: 1.5-2 pages
  • Award nomination: 1-2 pages (check requirements)
  • Brief reference: 0.5-1 page

Information to gather from candidate:

  • CV/resume
  • Personal statement or cover letter
  • Position/opportunity description
  • Specific points they'd like emphasized
  • Any concerns to address proactively

Time estimates:

  • Strong letter (well-known candidate): 1-2 hours
  • Standard letter (good candidate): 30-60 minutes
  • Brief reference: 15-30 minutes

Inputs required:

  • Candidate information (CV, statement)
  • Opportunity details (position, institution)
  • Your relationship context (duration, capacity)
  • Specific examples of excellence

Outputs produced:

  • Complete recommendation letter
  • (Optional) Commentary on strength calibration

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 Academic Letter Architect AI skill do?

Use when writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy, comparative statements, or evidence-based character assessment.

Why use Academic Letter Architect on TypingMind?

Because you install it once and use it with any model. Academic Letter Architect 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 Academic Letter Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Barrios88/barrios-skills/tree/main/skills/optional/career-and-grants/academic-letter-architect. 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 Academic Letter Architect?

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 Academic Letter Architect?

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

Is the Academic Letter Architect AI skill free?

Yes. It is published on GitHub by Barrios88 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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