outcome

outcome is a command for Claude Code from ishandutta2007/Awesome-AI-Job-Hunting. It costs 0 tokens per session (4,041 once invoked), scanned A, a copy of outcome, MIT.

A command for recording what happens after a job application, such as interviews, offers, rejection, or no response. It also handles follow-ups for applications that have gone quiet.

In plain words
What is it for?
It is for updating application statuses, saving outcomes in the application archive, and drafting and logging follow-up messages. It does not change the job-evaluation rules or profile files.
Why use it?
It keeps application results and follow-up activity in the job-search records, so later parts of the system can use that information.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: reads .claude/ paths.

Good fit It is for updating application statuses, saving outcomes in the application archive, and drafting and logging follow-up messages. It does not change the job-evaluation rules or profile files.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/ishandutta2007/awesome-ai-job-hunting/outcome
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.

Clone the repo
git clone --depth 1 https://github.com/ishandutta2007/Awesome-AI-Job-Hunting

Made for: Claude Code.

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 outcome

README.md
[![agentmods](https://agentmods.dev/badge/commands/ishandutta2007/awesome-ai-job-hunting/outcome/github.svg)](https://agentmods.dev/commands/ishandutta2007/awesome-ai-job-hunting/outcome)
Your own site
<a href="https://agentmods.dev/commands/ishandutta2007/awesome-ai-job-hunting/outcome"><img src="https://agentmods.dev/badge/commands/ishandutta2007/awesome-ai-job-hunting/outcome/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 outcome

Your own site · 80×15
<a href="https://agentmods.dev/commands/ishandutta2007/awesome-ai-job-hunting/outcome"><img src="https://agentmods.dev/badge/commands/ishandutta2007/awesome-ai-job-hunting/outcome.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,041 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 98% copy Near-identical to another mod 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 $0.00000 $0.04041
Opus 5 $0.00000 $0.02021
Sonnet 5 $0.00000 $0.00808
Haiku 4.5 $0.00000 $0.00404

Measured 12d ago against content hash 3762ee504c9d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

outcome 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.

Origin

This is a copy

98% identical to outcome — 54 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/commands/outcome.md · 196 lines

How it starts

The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/outcome - Record the Result of an Application

You are recording what happened to a job application: progress updates (interview invitations, stages completed, offers) and final resolutions (hired, rejected, no response). The data lands in two places the framework already reads but nothing systematically writes:

  • job_search_tracker.csv - the status column that /scrape and /rank use for dedup and exclusion
  • documents/applications/<company>_<role>/ - the per-application archive (posting, submitted drafts, outcome.md) that /setup Path A mines to calibrate 04-job-evaluation.md and surface STAR candidates

/outcome writes the data; /setup interprets it. This command never edits the evaluation framework or profile files itself.

The command also owns the stretch before there is an outcome to record: the follow-up branch (Step 2b) surfaces open applications that have gone quiet, drafts a brief follow-up note in the user's voice, and logs it - so the chase and the resolution it eventually leads to live in one flow.

Follow these steps in order.


Step 0: Parse Input

$ARGUMENTS may contain:

  • Nothing → list open applications and ask which one to update
  • A company name (optionally with a role), e.g. /outcome acme or /outcome acme ml engineer → target that application
  • followup → enter the follow-up branch (Step 2b) over every quiet open application, using the default threshold of 10 days
  • followup <N>, e.g. /outcome followup 14 → follow-up branch with an N-day threshold
  • followup <company>, e.g. /outcome followup acme → draft a follow-up for that application now, regardless of threshold

Step 1: Load State and Identify the Application

  1. Read job_search_tracker.csv. If it does not exist, create it with the standard header:
    date,company,sector,role,role_type,channel,status,contact_person,fit_rating,notes,cv_file,cover_letter_file,source,deadline
    
    If the file exists and its header does not end in ,deadline, append ,deadline to the header line only - no data row is touched. Legacy rows then read as an empty deadline. This is the one edit to an existing tracker this command may make outside a matched row, and Step 4's "never restructure the CSV" governs that row, not this header line.
  2. With an argument: match rows case-insensitively on company (and role, if given). One match → proceed. Several → list them and ask. None → the application was made outside the workflow; collect company, role, date applied, channel, and posting URL from the user and add a tracker row.
  3. Without an argument: list all rows whose status is not final (see Tracker status vocabulary below) as a numbered table (company, role, date applied, current status, deadline, days quiet, follow-ups sent) and ask which to update. The two derived columns come straight from existing data: days quiet counts from the row's date or the latest dated entry in notes, whichever is more recent; follow-ups sent counts the followed up YYYY-MM-DD markers in notes. If any open row is 10+ days quiet with fewer than two follow-ups sent, add one line under the table: "Some of these have gone quiet - want a follow-up draft? (Step 2b)". If every row is resolved, say so and stop.

Read the full file on GitHub · 196 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. 12d ago First seen · 196 lines · 0 tokens per session scan A 3762ee504c9d

Subscribe to this mod's changes

outcome is a command published in the GitHub repository ishandutta2007/Awesome-AI-Job-Hunting (3 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,041 tokens. A static security scan graded it A with 0 findings. It is 98% identical to outcome, differing in 54 lines, and is treated as a copy.