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.
git clone --depth 1 https://github.com/ishandutta2007/Awesome-AI-Job-HuntingWrote 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/ishandutta2007/awesome-ai-job-hunting/outcome)<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.
<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>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 | $0.00000 | $0.04041 |
| Opus 5.5 | $0.00000 | $0.01616 |
| Sonnet 5.5 | $0.00000 | $0.00808 |
| Haiku 4.5 | $0.00000 | $0.00404 |
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 1mo 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.
This is a copy
98% identical to outcome — 58 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.
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/scrapeand/rankuse for dedup and exclusiondocuments/applications/<company>_<role>/- the per-application archive (posting, submitted drafts,outcome.md) that/setupPath A mines to calibrate04-job-evaluation.mdand 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 acmeor/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 daysfollowup <N>, e.g./outcome followup 14→ follow-up branch with an N-day thresholdfollowup <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
- Read
job_search_tracker.csv. If it does not exist, create it with the standard header:
If the file exists and its header does not end indate,company,sector,role,role_type,channel,status,contact_person,fit_rating,notes,cv_file,cover_letter_file,source,deadline,deadline, append,deadlineto 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. - 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.
- 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
dateor the latest dated entry innotes, whichever is more recent; follow-ups sent counts thefollowed up YYYY-MM-DDmarkers innotes. 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.
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.
- 1mo ago First seen · 196 lines · 0 tokens per session scan A 3762ee504c9d
outcome is a command published in the GitHub repository ishandutta2007/Awesome-AI-Job-Hunting (3 stars, last pushed 2mo 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 58 lines, and is treated as a copy.
Other commands, from other repositories
ab-master
Master controller for the AB Method - an incremental tasking workflow system that transforms problems into tasks, then into focused missions.
skip-reflect
Discard queued learnings without processing.
setup
Verify ClauDex prerequisites — Codex CLI, authentication, and git.
view-queue
View the learnings queue without processing.
analyze-codebase
Generate comprehensive analysis and documentation of entire codebase.
tdd
This outlines the development practices and principles we require you to follow. Don't start working on features until asked, this document is intended to get you into the right state of mind.