
academic-letter-architect
Guides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/lyndonkl/claude --skill academic-letter-architect- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads academic-letter-architect automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Academic Letter Architect
Table of Contents
Related skills (use instead for):
- Personal statements:
career-document-architect - Cover letters to journals:
scientific-email-polishing - Grant proposals:
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
Key requirements:
-
Truthfulness: Only write what you genuinely believe. Dishonest letters harm candidates and your reputation.
-
Evidence-based: Every claim should have a supporting example. "Smart" means nothing without evidence.
-
Appropriate comparison: Compare to relevant reference class (other postdocs, not all scientists ever).
-
Match content to opportunity: Emphasize research for academic jobs, practical skills for industry.
-
Candidate voice preservation: Reflect the candidate's actual achievements, not fabricated ones.
-
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:
- resources/methodology.md: Context gathering, evidence collection, tone calibration
- resources/template.md: Opening, body, closing templates
- resources/evaluators/rubric_academic_letter.json: Quality scoring
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
FAQ
What does the academic-letter-architect skill do?
Guides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
How do I install academic-letter-architect?
Run npx skills add https://github.com/lyndonkl/claude --skill academic-letter-architect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/academic-letter-architect/ (personal) or .claude/skills/academic-letter-architect/ (project).
Which AI agents can use academic-letter-architect?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is academic-letter-architect safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains academic-letter-architect?
It is published by lyndonkl in lyndonkl/claude. It has 123 recorded installs.
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