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.
npx agentmods add commands/madslorentzen/ai-job-search/html-reportgit clone --depth 1 https://github.com/MadsLorentzen/ai-job-searchWhat 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 | $0.00000 | $0.02233 |
| Opus 5 | $0.00000 | $0.01117 |
| Sonnet 5 | $0.00000 | $0.00447 |
| Haiku 4.5 | $0.00000 | $0.00223 |
Grade A, and why
html-report 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- html-report — 100% identical, 0 lines differ
- html-report — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/html-report - Generate Application Tracker Dashboard
Generate a self-contained HTML dashboard from job_search_tracker.csv and the application archives under documents/applications/. The output is a single .html file — no server, no dependencies — that can be opened directly in a browser.
Step 0: Parse Arguments
- No argument → output to
reports/application-dashboard.html - A path argument (e.g.
/html-report ~/Desktop/report.html) → use that path --openflag → after writing, tell the user to open the file (cannot open a browser directly)
Create reports/ if it does not exist.
Step 1: Collect Data
Read in parallel:
-
job_search_tracker.csv— the primary source. Parse every row into a record with fields:date,company,sector,role,role_type,channel,status,contact_person,fit_rating,notes,cv_file,cover_letter_file,source,deadlineRows written before
deadlineexisted have thirteen fields and no fourteenth value. Treat the missing field as empty - never drop the row, and never infer a deadline from itsdate. -
documents/applications/*/outcome.md— for each resolved application, read the outcome file to get the exact interview stages reached (the checkboxes) and any notes. Merge this into the matching tracker row by company+role fuzzy match (lowercase, ignore punctuation). If an archive exists for a row but there is no match, attach it as extra context anyway.
Status normalisation — map tracker values to six canonical buckets before computing stats:
drafted→ Drafted (documents written by/apply, not yet submitted)applied→ Active (resume submitted, no further signal)interview→ Interviewoffer→ Offerhired→ Hiredrejected/no_response/no response/offer_declined/offer declined/withdrawn→ Rejected/Closed- anything else → Rejected/Closed, and name the unrecognised value once in the status breakdown — matching is case-insensitive
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.
- 2d ago First seen · 147 lines · 0 tokens per session scan A 36a41dbe56aa
html-report 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 2,233 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.
Other commands, from other repositories
resume
Generate a tailored resume and cover letter from a job description, score both, create DOCX files, and update the tracker.
resume-team
Run the role-separated, fail-closed Resume Team workflow against a job description.
writing-coach
Human-voice writing coach — rewrite resumes and cover letters with brevity, burstiness, plain language, and authentic impact. Blocks AI-sounding prose.
cover-letter
Create a one-page cover letter for a job description and generate the final DOCX.
find-jobs
Search live job boards for roles that match the master resume, then rank them by fit.
job-fit
Run the deterministic, digest-bound candidate-fit gate before any resume tailoring.