Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jain777/jobclaw-skills --skill build-profilegit clone --depth 1 https://github.com/jain777/jobclaw-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jain777/jobclaw-skills/build-profile)<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/build-profile"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/build-profile/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/build-profile"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/build-profile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.02831 |
| Opus 5 | $0.00036 | $0.01416 |
| Sonnet 5 | $0.00014 | $0.00566 |
| Haiku 4.5 | $0.00007 | $0.00283 |
Grade A, and why
build-profile scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
build-profile
Produce profile/master-profile.md from whatever the user already has, then fill gaps with a short, track-aware interview. Ingest first, ask last.
1. Gather what exists (don't ask the user to retype anything)
Collect any of these the user provides; actively offer the options:
- Resume / CV — see step 1a for PDFs (mandatory link-extractor first), then read the body with your runtime's file-read tool.
- LinkedIn PDF — read it. (Point them to LinkedIn → profile → Resources → Save to PDF if they don't have it.)
- AI-memory (proactively offer it). Ask once: "Have you used ChatGPT, Claude, or Gemini for your job search or career? Paste this prompt into the one you use most and bring back the result — I'll fold in recent interviews, feedback, and career details you'd otherwise have to retype." Then surface the prompt from
../../profile/IMPORT_MEMORIES_PROMPT.md(it pulls only job-search-relevant memories, not everything). On ingest, merge only new facts — never duplicate what the resume/profile already has; mark uncertain items[VERIFY]. - Links — record every link, then enrich the user's own ones (see §1b). LinkedIn stays record-only (ToS).
Read everything before asking anything.
1a. For every PDF input — run the link extractor FIRST
A typical agent file-read tool surfaces visible anchor text only (it shows the word "LinkedIn" but not the URL it's hyperlinked to). LaTeX/Word/Docs resumes routinely embed \href{url}{anchor} annotations whose URLs are invisible to a plain read. You will silently lose the user's LinkedIn / GitHub / portfolio URLs if you skip this step — which is why extract_links.py exists.
python3 skills/build-profile/scripts/extract_links.py <path-to-resume.pdf>
The script decompresses every FlateDecode stream and pulls every /URI annotation it can find. Stdout is JSON:
{
"urls": ["https://...", ...], // every URL found, in order
"classified": {
"linkedin": "https://linkedin.com/in/...",
"github": "https://github.com/...",
"other": { "<host-or-label>": "<url>", ... }
}
}
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 147 lines · 72 tokens per session scan A 4d0e55b6dd5c
build-profile is a skill published in the GitHub repository jain777/jobclaw-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 2,831 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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