outcome

A command for recording updates and final results for job applications. It stores statuses such as interview progress, offers, rejection, or no response in the application tracker and the related application archive.

In plain words
What is it for?
Use it to record an application’s progress or resolution. It can also find applications that have gone quiet, draft a short follow-up message, and log that follow-up.
Why use it?
It keeps application outcomes in the places used by the job-search system, so later searches can avoid unsuitable repeats and the setup process can learn from past results.

Command for Claude Code

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 commands/madslorentzen/ai-job-search/outcome
Clone the repo
git clone --depth 1 https://github.com/MadsLorentzen/ai-job-search

Made for: Claude Code.

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 4,040 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00000 $0.04040
Opus 5 $0.00000 $0.02020
Sonnet 5 $0.00000 $0.00808
Haiku 4.5 $0.00000 $0.00404

Measured 2d ago against content hash 351cb7a5a23f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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

Copies of this mod

2 near-identical copies found in the catalogue:

  • outcome — 98% identical, 2 lines differ
  • outcome — 97% identical, 4 lines differ
.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. 2d ago First seen · 196 lines · 0 tokens per session scan A 351cb7a5a23f

Subscribe to this mod's changes

outcome is a command published in the GitHub repository MadsLorentzen/ai-job-search (39,400 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,040 tokens. 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.