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-new.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-new)<a href="https://agentmods.dev/commands/ncalavera/llm-job-pipeline/jobs-new"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-new/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-new"><img src="https://agentmods.dev/badge/commands/ncalavera/llm-job-pipeline/jobs-new.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 | $0.02697 |
| Opus 5 | $0.00000 | $0.01349 |
| Sonnet 5 | $0.00000 | $0.00539 |
| Haiku 4.5 | $0.00000 | $0.00270 |
Grade A, and why
jobs-new scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
probing the public ATS APIs with `curl` How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/jobs-new
One command a day. The deterministic orchestration — stage ORDER, batching,
checkpoints, the heartbeat, and the publish gate — lives in
scripts/run_daily.py, NOT in this file. You cannot run the stages out of
order, because you do not drive them; the driver does. Your job is only the
JUDGMENT it pauses for. Works with any coding agent that runs shell and
follows this file: the driver does the Python; you prepare evidence at its gates.
Human keep/put-aside decisions happen in the dashboard.
Reply in the user's product language. Before you say anything to the user,
read the ## OUTPUT_LANGUAGE section of config/user_profile.md (resolve it
with python3 -c "import sys;sys.path.insert(0,'scripts');import product_language as p;print(p.resolve())" → en/ru). Write ALL your chat, gate summaries and
progress notes in that language. The driver already prints its banner/summary in
it; match it. (Scoring/verdict data stays as the pipeline emits it — you are
translating your own words, not the DB.)
The loop
-
Launch the driver in the background and show the live card. A foreground command's stdout is invisible until it exits, so a long fetch looks frozen. Launch with
run_in_background: true, then every ~20–30s runpython3 scripts/run_card.pyand post its one-line output to chat (fetch ▕███░░░▏ 18/40 · LinkedIn · +12 · 6m02s). Pace polls with the wait /Monitorprimitive — never a foregroundsleep(blocked).python3 scripts/run_daily.pyBoards you have enabled (via
/jobs-add boardorpython3 scripts/sources.py enable-board <id>) fetch automatically every run — nothing to pass.--boards "a,b,c"adds more boards for THIS run only, unioned ON TOP of the persisted set; it never has to be repeated to keep an enabled board on. See what's enabled withpython3 scripts/sources.py(catalogue indocs/job-boards-catalogue.md).Not sure which boards fit?
python3 scripts/sources.py recommendproposes the ones that match your profile (target field, roles, geography) — an engineer is proposed engineering boards, not someone else's fixed set. It only suggests; enable what you want withenable-board. Boards never auto-enable.
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 · -54 lines ba42816e78f3
- 7d ago Changed · +2 lines 3b2041cf51b4
- 12d ago First seen · 268 lines · 0 tokens per session scan A 7e21683cfaeb
jobs-new is a command published in the GitHub repository ncalavera/llm-job-pipeline (2 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,697 tokens. A static security scan graded it A with 1 finding (makes network calls). 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.