job-hunter AGENTS.md

job-hunter AGENTS.md is an instructions file for Codex, OpenCode from abdulrbasit/job-hunter. It costs 1,093 tokens per session, scanned A, original, MIT.

Repository instructions for Job Hunter, a project that helps process and tailor job applications. They describe its Python engine, agent skills, configuration, execution modes, and important files.

In plain words
What is it for?
Use them when running or changing Job Hunter, choosing between interactive and automated modes, or working with profiles, resumes, job scoring, and cover letters.
Why use it?
They give the coding agent the project structure and operating rules needed to work safely in the repository.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/abdulrbasit/job-hunter/agents-md
Clone the repo
git clone --depth 1 https://github.com/abdulrbasit/job-hunter

Made for: Codex, OpenCode.

Wrote 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.

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README.md
[![agentmods](https://agentmods.dev/badge/instructions/abdulrbasit/job-hunter/agents-md.svg)](https://agentmods.dev/instructions/abdulrbasit/job-hunter/agents-md)
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<a href="https://agentmods.dev/instructions/abdulrbasit/job-hunter/agents-md"><img src="https://agentmods.dev/badge/instructions/abdulrbasit/job-hunter/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,093 This file is loaded in full into every session.
When invoked 1,093 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01093 $0.01093
Opus 5 $0.00547 $0.00547
Sonnet 5 $0.00219 $0.00219
Haiku 4.5 $0.00109 $0.00109

Measured 5d ago against content hash 6ff4c8fe34e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

job-hunter AGENTS.md 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 5d 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.

AGENTS.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Job Hunter

This is the agent context source of truth for the product repo. CLAUDE.md and any other per-agent files defer here. Keep durable operating context in this file, skills, config, or code; do not add separate architecture docs.

This repo combines the Python engine (job_hunter/) and the agent skill layer (.claude/skills/, .agents/skills/).

Execution Modes

Mode What runs When to use
agent Agent skills handle scoring, tailoring, cover letters. Python prepares context. Default interactive review.
llm-api Full autonomous pipeline; LLM APIs called directly inside Python. Unattended runs and GitHub Actions.

Set mode: in config/job_hunter.yml.

Repo Map

  • job_hunter/: Python package, CLI, pipeline, source adapters, LLM API mode.
  • .claude/skills/: skills for Claude Code (mirrored to .agents/skills/ for Codex).
  • config/job_hunter.yml: user-editable machine config.
  • config/schemas/job_hunter.schema.json: schema for job_hunter.yml.
  • job_hunter/templates/workspace/profile/: canonical starter profile and resume templates.
  • examples/profile/: filled fictional examples; not a template source.
  • outputs/state/jobs.db: SQLite — single source of truth for all job state (URL dedup, scraped candidates, application lifecycle).
  • docs/architecture.md: current package ownership, dependency boundaries, pipeline, state, and template architecture.

Skills

/job-hunter          job search command center (batch, one, finalize, screen,
                     tailor, score, research, interview, outreach, stories, linkedin)
/linkedin            LinkedIn content center (ideas, draft, engage, network)
/setup               workspace setup center (onboard, doctor, region, style, stories)

Developer contributor context:

/commit              preflight checks, safe staging, and commit format

Use /commit before developer commits. It still requires full preflight:

python -m pytest tests/ -q --tb=short
python -m ruff format --check job_hunter tests .github/scripts
python -m ruff check job_hunter tests .github/scripts
ty check job_hunter tests

Read the full file on GitHub · 103 lines

Changes

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.

  1. 5d ago First seen · 103 lines · 1,093 tokens per session scan A 6ff4c8fe34e9

Subscribe to this mod's changes

job-hunter AGENTS.md is an instructions file published in the GitHub repository abdulrbasit/job-hunter (24 stars, last pushed 1mo ago), licensed MIT. It adds 1,093 tokens to every session, about $0.0055 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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