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-night.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-night)<a href="https://agentmods.dev/commands/ncalavera/llm-job-pipeline/jobs-night"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-night/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-night"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-night.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 | $0.00094 | $0.01695 |
| Opus 5 | $0.00047 | $0.00847 |
| Sonnet 5 | $0.00019 | $0.00339 |
| Haiku 4.5 | $0.00009 | $0.00169 |
Grade A, and why
jobs-night 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/jobs-night — one preparation gate, unattended
The default daily run emits prepare_screening with discovery payloads: one
call per vacancy. Unscored roles request a numeric score and quoted facts/profile
comparison; existing roles below 40 request only stale or missing facts; roles
at or above 40 are untouched. The other gate names remain legacy paths.
Arguments: $ARGUMENTS = <gate> <night_dir> <phase> where <gate> is one of
screen_companies | score_companies | score_vacancies | prepare_screening,
<night_dir> is this night's private directory (vacancies/nightly/<date>/),
and <phase> names the pass inside the gate (screen | escalate for
score_vacancies, screen for screen_companies, score for
score_companies, prepare for prepare_screening).
This is the NIGHT variant of the /jobs-new gate protocol. The pipeline
orchestration already ran in scripts/run_daily.py --unattended; your only job
is the judgment for ONE gate. The wrapper that launched you resumes the driver
after you stop — you never run the driver yourself. Everything below overrides
the interactive habits of /jobs-new.
Overrides — read first, they are absolute
- Never ask a question. There is no human. If something is ambiguous, take the safe direction (skip the item, record it failed in the log) and keep going.
- Subagent model comes from the wrapper. Read
<night_dir>/session.jsononce and pass itsmodelto EVERY scoring subagent. Python resolvedscreen_model(),scoring_model()orcompany_screen_model()for this gate and phase before starting you. Never use a shell to discover it. - Spawn only the
night-scoreragent type. One payload file = onenight-scorersubagent. No other agent type, ever. At most 5 subagents at a time (rolling waves). 1 item = 1 subagent — batching is untested here. - File in, file out. Each subagent reads its own payload
<night_dir>/score_in/NNN.jsonand writes its one result to<night_dir>/score_out/NNN.json(same NNN). Subagents have Read and Write only. Neither you nor a subagent has shell, database or network tools. - Write each result immediately. The Python wrapper saves completed JSON files while you work and performs a final sweep when you stop or time out. Never run a save command yourself. A malformed file is skipped independently.
- Log after every wave. Append one line per wave to
<night_dir>/scoring_log.md: time, gate, items in the wave, written count, failures with their NNN. Findings and anomalies go HERE, not to an issue tracker. - Never resume the driver. The wrapper owns the driver loop: it runs
--resumeitself after your session ends. When every score_in item is written or recorded failed in the log, finish the End-of-session step below 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.
- 2d ago Changed · +7 lines 6c2648ee050e
- 7d ago Changed c7af6c1eb1a5
- 12d ago First seen · 131 lines · 94 tokens per session scan A 32a8c29eec2d
jobs-night is a command published in the GitHub repository ncalavera/llm-job-pipeline (2 stars, last pushed 3d ago), licensed MIT. It adds 94 tokens to every session and 1,695 once invoked, about $0.0005 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.
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