Tamar Resume Tailor

Tailor resumes for specific job applications using the Tamar AI API

Tamar Resume Tailoring

Use the tamar CLI to tailor resumes for specific job applications via the Tamar API.

Triggers

Activate when the user says anything like:

• "tailor my resume"
• "customize my resume for"
• "help me apply for this job"
• "make a resume for this role"
• "adapt my resume"
• "target my resume at"

Prerequisites

• The tamar CLI must be installed by the user before using this skill: npm install -g tamar-cli
• An API key must be configured. Verify by running tamar status. If it fails with "No API key configured", ask the user to run tamar auth --key <their-key> (keys are obtained from https://ask-tamar.com → Profile → API Keys)
• Do NOT read or inspect ~/.tamarrc directly — use tamar status to check auth

Pipeline

1. Check if the user has an experience profile

bash

tamar profile

Shows existing profiles with name, role, seniority, skills, and enrichment depth. If a profile exists, use its ID with tamar tailor --profile <id> for higher-quality output. If none exists, proceed to step 2.

2. Ensure user has a resume uploaded

If the user has a resume file and hasn't uploaded one yet:

bash

tamar upload <resume-file>

3. Get the job description

Ask the user for the job description. It can be:

• A URL (LinkedIn, company careers page, etc.)
• A file path — read the file content first
• Pasted text

4. Tailor the resume

IMPORTANT — Input safety: Never interpolate user-provided strings directly into shell commands. Always write job descriptions or multi-word arguments to a temporary file and pass the file path, or use the -- separator and single-quote the argument. This prevents shell injection from malicious input.

bash

# Safe: write JD to a temp file, pass the file
echo '<job description text>' > /tmp/jd.txt
tamar tailor --job /tmp/jd.txt

# Safe: single-quote the argument to prevent shell expansion
tamar tailor --job 'https://example.com/jobs/12345'

If the user also provides a resume file and hasn't uploaded before:

bash

tamar tailor --job /tmp/jd.txt --resume '<resume-file>'

5. Review the output

The command returns JSON with:

• id — the generated resume ID (stored for later commands)
• quality — "enriched" (has profile) or "basic" (resume-only)
• analysis — job match analysis
• changes — list of changes made

Present the analysis summary conversationally. Highlight key matches and gaps.

6. Handle feedback

If the user wants changes:

Write the feedback to a temp file to avoid shell injection:

bash

echo '<feedback text>' > /tmp/feedback.txt
tamar feedback "$(cat /tmp/feedback.txt)"

Or for a specific resume:

bash

tamar feedback "$(cat /tmp/feedback.txt)" --id '<resume-id>'

7. Download the result

bash

tamar download                    # PDF (default)
tamar download --format json      # structured JSON

The PDF is saved to the current directory. Tell the user the file path.

Error Handling

Error	Action
No API key configured	Guide user to run tamar auth --key <key>
401 Invalid or expired	Prompt to re-run tamar auth with a new key
422 Could not parse URL	Site blocks scraping (common with LinkedIn). Paste the JD text instead
429 Rate limited	Tell user to wait and retry
402 Plan limit reached	Direct to https://ask-tamar.com for upgrade
Network error / timeout	Check connection. AI calls can take 15–60s — ensure client timeout is ≥120s

Quality Notes

• Enriched quality (user has an experience profile — check via tamar profile) = higher quality tailoring
• Basic quality (resume-only, no profile) = still useful but less nuanced
• If user has no profile, suggest building one at https://ask-tamar.com via the interactive Q&A, or use the enrichment API flow

Example Interaction

text

User: Can you tailor my resume for this job? https://linkedin.com/jobs/12345

Agent: Let me tailor your resume for that role.

[runs: tamar tailor --job 'https://linkedin.com/jobs/12345']

Agent: Done! Here's what I found:
- Quality: enriched (used your experience profile)
- Key alignments: Python, data pipelines, team leadership
- Adjusted: Reframed your experience to emphasize data platform work
- Gaps: Kubernetes — no production experience listed

Want me to tweak anything? I can also download the PDF for you.