Borrowing it
Nothing to install: this file belongs to suraj-davariya/ai-job-search. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/suraj-davariya/ai-job-search/main/.claude/commands/setup.mdgit clone --depth 1 https://github.com/suraj-davariya/ai-job-searchWrote 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/commands/suraj-davariya/ai-job-search/setup)<a href="https://agentmods.dev/commands/suraj-davariya/ai-job-search/setup"><img src="https://agentmods.dev/badge/commands/suraj-davariya/ai-job-search/setup/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/commands/suraj-davariya/ai-job-search/setup"><img src="https://agentmods.dev/badge/commands/suraj-davariya/ai-job-search/setup.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 | $0.00017 | $0.03556 |
| Opus 5 | $0.00009 | $0.01778 |
| Sonnet 5 | $0.00003 | $0.00711 |
| Haiku 4.5 | $0.00002 | $0.00356 |
Grade A, and why
setup 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 10d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup — Candidate Profile Onboarding
Spec:
docs/requirements/functional-requirements-onboarding.md(REQ-0001–0017) Data model:docs/requirements/data-requirements.md(§1–§9, §13, §17) Merge rules:docs/requirements/business-rules-and-validation.md(§7) Flow:docs/requirements/user-flows.md(§1)
You are running the CareerForge onboarding workflow. Your job is to populate the
user's profile files — replacing every [UPPER_SNAKE_CASE] placeholder token
with their real data — through one of three convergent paths.
This is a prompt-as-code command (ARCH-0001): there is no compiled program
and no settings/profile.json. The profile lives in Markdown files (file-as-DB,
ARCH-0004). You read those files, gather the user's data, and write it back.
Hard Invariants (apply to every step, every write)
- Read-before-write (REQ-0002). Always read a target file's current contents before proposing changes to it. Existing file state is the idempotency baseline — anything already present is not proposed again.
- Idempotency (business-rules §7.3). Re-running
/setupwith the same inputs produces no new changes. Never re-propose content already present in a file in any form. - No fabrication (ARCH-0007). Never invent a token value. If you don't have data for a token, ask the user or leave the token in place — never guess a name, date, employer, skill, or achievement.
- No writes without confirmation (REQ-0009). Present all proposed changes and obtain explicit user approval before writing any file.
- Human-in-the-loop (ARCH-0006). The user reviews and approves; you draft and execute. When in doubt, ask.
Target files (the convergence set — REQ-0016):
| File | Tokens populated |
|---|---|
.claude/skills/job-application-assistant/01-candidate-profile.md |
Identity, education, experience, projects, skills, publications, awards, references, [AI_TOOL_NAME] |
.claude/skills/job-application-assistant/02-behavioral-profile.md |
Overview, drives, behaviors, preferences, growth areas, posting keywords, management style, application usage |
.claude/skills/job-application-assistant/04-job-evaluation.md |
Strong/moderate/weak skills, strong/moderate/entry experience, career goals, energizing/draining tasks |
.claude/skills/job-application-assistant/05-cv-templates.md |
[PROFILE_STATEMENT_*] (one per applicable role type) |
.claude/skills/job-application-assistant/07-interview-prep.md |
[STAR_*] examples and [STUB_*] Path-A stubs |
CLAUDE.md (user fork — from CLAUDE.md.template) |
Union of the above + workflow sections |
.claude/skills/job-scraper/search-queries.md |
Search query + location-tier tokens |
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.
- 10d ago First seen · 299 lines · 17 tokens per session scan A 711e934ceb5c
setup is a command published in the GitHub repository suraj-davariya/ai-job-search (22 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 3,556 once invoked, about $0.0001 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 2026-08-30.
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