Tamar Resume Tailor

---

name: tamar-resume-tailor description: Tailor resumes for specific job applications using the Tamar AI API metadata: {"openclaw":{"requires":{"bins":["tamar"],"env":["TAMAR_API_KEY"]},"primaryEnv":"TAMAR_API_KEY","install":[{"id":"npm","kind":"node","package":"tamar-cli","bins":["tamar"],"label":"Install Tamar CLI (npm)"}]}}

---

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 (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

tamar profile

Shows existing profiles with name, role, seniority, skills, and enrichment depth. If a profile exists, use its ID with tamar tailor --profile 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:

tamar upload 

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.

# Safe: write JD to a temp file, pass the file
echo '' > /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:

tamar tailor --job /tmp/jd.txt --resume ''

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:

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

Or for a specific resume:

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

7. Download the result

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 | | 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

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.

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 (keys are obtained from https://ask-tamar.com → Profile → API Keys)

Do NOT read or inspect ~/.tamarrc directly — use tamar status to check auth