Borrowing it
Nothing to install: this file belongs to ncalavera/llm-job-pipeline. 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/ncalavera/llm-job-pipeline/main/.claude/commands/jobs-eval.mdgit clone --depth 1 https://github.com/ncalavera/llm-job-pipelineWrote 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/ncalavera/llm-job-pipeline/jobs-eval)<a href="https://agentmods.dev/commands/ncalavera/llm-job-pipeline/jobs-eval"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-eval/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/ncalavera/llm-job-pipeline/jobs-eval"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-eval.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 | $0.00056 | $0.01959 |
| Opus 5 | $0.00028 | $0.00979 |
| Sonnet 5 | $0.00011 | $0.00392 |
| Haiku 4.5 | $0.00006 | $0.00196 |
Grade A, and why
jobs-eval 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 7d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/jobs-eval
Does the scorer actually agree with you? This command answers that with one
number. It builds a golden set — a few dozen of your own vacancies each
labelled fit or nofit with a short reason — scores them with the live
scripts/prompts/vacancy-scoring.md, and reports how often the model's verdict
matches your label, plus precision/recall at the score threshold and a readable
list of every disagreement.
Method: Hamel Husain's "Critique Shadowing" (https://hamel.dev/blog/posts/llm-judge/). Cheap, honest, no external service.
The golden set is personal data (real titles, orgs, your reasons). It lives
in evals/ (gitignored) and is never committed. Works the same in full mode
(Postgres) and simple mode (SQLite) — it reads through the same data layer.
Everything is one script: python3 scripts/golden_set.py <subcommand>.
There are two surfaces to check, each with its own personal golden set:
- vacancy (default) — the vacancy-scoring prompt vs your like/pass verdicts.
- company — the company-scoring (WANT) prompt vs your keep/reject verdicts
on companies. Add
--kind companyto any subcommand below.
1. Build the set
Fast path — seed from your verdicts. Every like/pass you have already made
is a label: a liked-basket status means fit, a passed/skipped status means
nofit. Seed straight from that history (read-only):
python3 scripts/golden_set.py seed --limit 50
Positives are usually scarce, so seed keeps ALL of your fit verdicts and fills
the rest with recent nofit ones — a set with no positives can't yield a
meaningful precision/recall. Probable auto-expired passes (a passed role whose
deadline had already lapsed) are excluded, since those aren't fit judgements.
Re-running seed later only appends new verdicts; it never rewrites old labels.
Blind path — hand-label fresh vacancies. For coverage the verdict history lacks, label some vacancies WITHOUT seeing their score:
python3 scripts/golden_set.py template --limit 25 # writes evals/label_template.jsonl (no scores shown)
# edit each line: set "label" to fit or nofit, add a one-line "reason"
python3 scripts/golden_set.py add-template # appends the ones you labelled
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.
- 7d ago Changed c9d0ba7a229e
- 12d ago First seen · 172 lines · 56 tokens per session scan A 9782549f98df
jobs-eval is a command published in the GitHub repository ncalavera/llm-job-pipeline (2 stars, last pushed 3d ago), licensed MIT. It adds 56 tokens to every session and 1,959 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.
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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.