Borrowing it
Nothing to install: this file belongs to cypggs/ai-job-search-cn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cypggs/ai-job-search-cn/master/.claude/commands/html-report.mdgit clone --depth 1 https://github.com/cypggs/ai-job-search-cnWrote 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/cypggs/ai-job-search-cn/html-report)<a href="https://agentmods.dev/commands/cypggs/ai-job-search-cn/html-report"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/html-report/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/cypggs/ai-job-search-cn/html-report"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/html-report.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 | $0.01580 |
| Opus 5 | $0.00000 | $0.00790 |
| Sonnet 5 | $0.00000 | $0.00316 |
| Haiku 4.5 | $0.00000 | $0.00158 |
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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 134 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 -
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 five canonical buckets before computing stats:
applied→ Active (resume submitted, no further signal)interview→ Interviewoffer→ Offerhired→ Hiredrejected/no_response/no response/offer_declined/interview_only/withdrawn→ Rejected/Closed
Step 2: Compute Summary Stats
From the normalised data compute:
- Total applications
- By status bucket: count per bucket
- By sector: count per unique sector value
- By channel: online vs referral vs other
- By year/season: group by the
datefield (which may be a year like2025or a full date) - Funnel rates: what % progressed past resume screen (reached Interview or beyond)
- Rejection rate: Rejected/Closed ÷ Total with a resolved status (exclude Active)
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.
- 11d ago First seen · 134 lines · 0 tokens per session scan A a958e0b246c2
html-report is a command published in the GitHub repository cypggs/ai-job-search-cn (61 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,580 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.