application-filling

application-filling is a skill for Claude Code, Codex from sayantan94/AppliedIn. It costs 84 tokens per session (1,039 once invoked), scanned A, original, MIT.

A job-application assistant that completes online application forms in your real Chrome browser using only facts you approve.

In plain words
What is it for?
Applying to jobs, uploading a tailored résumé, answering free-text questions from approved information, and submitting applications for review when needed.
Why use it?
It reduces repetitive form filling while avoiding invented answers and duplicate submissions. It pauses when it reaches an unknown question, a login barrier, or a CAPTCHA.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Applying to jobs, uploading a tailored résumé, answering free-text questions from approved information, and submitting applications for review when needed.

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Install with agentmods
npx agentmods add skills/sayantan94/appliedin/application-filling
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.

Any agent
npx skills add sayantan94/AppliedIn --skill application-filling
Clone the repo
git clone --depth 1 https://github.com/sayantan94/AppliedIn

Made for: Claude Code, Codex.

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.

agentmods badge for application-filling

README.md
[![agentmods](https://agentmods.dev/badge/skills/sayantan94/appliedin/application-filling/github.svg)](https://agentmods.dev/skills/sayantan94/appliedin/application-filling)
Your own site
<a href="https://agentmods.dev/skills/sayantan94/appliedin/application-filling"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/application-filling/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.

agentmods 80×15 button for application-filling

Your own site · 80×15
<a href="https://agentmods.dev/skills/sayantan94/appliedin/application-filling"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/application-filling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,039 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00084 $0.01039
Opus 5 $0.00042 $0.00519
Sonnet 5 $0.00017 $0.00208
Haiku 4.5 $0.00008 $0.00104

Measured 9d ago against content hash 3b6d89ed354c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

application-filling 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 9d 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.

src/agent/skills/application-filling/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Application filling

You orchestrate one call — apply_to_job() — and turn its result into either a "done" or a human gate. The heavy lifting is the engine below; you do not drive the browser field-by-field yourself.

What apply_to_job() does internally

It hands the posting to a claude --chrome subprocess that acts in the owner's own browser — a real profile with real history, which is what portals accept. There is no headless driver and no second engine.

  1. Refuse outright if tracking already marks this job applied (a duplicate under the owner's real name is worse than a missed application).
  2. Rewrite the posting to the ATS board's direct URL where one exists — a cross-origin embed blocks the résumé upload.
  3. Open the posting and reach the actual form (Ashby "Application" tab, Greenhouse/Lever "Apply" button).
  4. Set the tailored résumé on the real résumé input, never the optional "autofill from resume" uploader, and never by clicking an "Attach" button (that opens an OS file chooser and freezes the browser).
  5. Fill from approved facts only. A free-text question with no banked answer goes to a writer model grounded in the résumé + GitHub + JD.
  6. Submit, read the FORM's own validation errors, fix the flagged fields, resubmit. The form is the source of truth, not a DOM read.
  7. Stop at the real blocker: a required field with no answer, an account wall, or a CAPTCHA — filled form left open for the human.

Every value the agent writes passes guard_value() first, so the guarantees hold whatever the model decides: self-identification questions (disability, veteran status, race, gender) can be declined but never affirmed, sanctions and restricted-country questions always take the safe answer, and placeholder text never reaches a field. A refusal is enforced in code, not requested in a prompt.

Instructions

Step 1: Apply

Call apply_to_job(). It returns one of:

  • {"status": "applied", "confirmation": ...} — a real submission was confirmed (confirmation text or a confirmation redirect). Report it; you're done.
  • {"status": "gate", "reason": ..., "question": ...} — a genuine blocker: unknown_field (a required field with no approved answer), no_account (login/signup wall), or captcha.
  • {"status": "failed", "reason": ..., "detail": ...} — a real block: duplicate_application, application_limit, already_applied, or a guardrail refusal. Close it out; do not retry.
  • {"status": "uncertain" | "unknown", "detail": ...} — the submit could not be confirmed (e.g. the browser was closed, or the run ended with no confirmation on the page). It did NOT resubmit.

Read the full file on GitHub · 80 lines

Files

What ships with it

1 file 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.

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. 9d ago First seen · 80 lines · 84 tokens per session scan A 3b6d89ed354c

Subscribe to this mod's changes

application-filling is a skill published in the GitHub repository sayantan94/AppliedIn (7 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 1,039 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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