Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add spinningrachel/career-engine/plugin install career-engineWrote 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/skills/spinningrachel/career-engine/career-engine-edit)<a href="https://agentmods.dev/skills/spinningrachel/career-engine/career-engine-edit"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/career-engine-edit/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/skills/spinningrachel/career-engine/career-engine-edit"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/career-engine-edit.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 | $0.00102 | $0.23651 |
| Opus 5 | $0.00051 | $0.11825 |
| Sonnet 5 | $0.00020 | $0.04730 |
| Haiku 4.5 | $0.00010 | $0.02365 |
Grade C, and why
career-engine-edit scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
For each role fetched in Step E0, extract the structured JD from `$RUN_PIPE/needs-editing-role-properties.md` (read once here, not re-read per step). The `JD Body` property was already captured in Step E0 as part of the How it starts
The opening of the file, as written. The whole thing — 562 lines — stays where its author put it; the contents beside it link to each section on GitHub.
New Application — Editing Pipeline
Registry: this pipeline is listed in the Pipeline Registry in
skills/career-engine/SKILL.md. Actions owned by another pipeline's registry row are out of scope here — route to that pipeline instead of improvising.
This skill handles the editing pipeline for roles the user has flagged as needing revision. It runs separately from the main pipeline and is triggered by Status = Needs editing in the Job Applications database.
The key difference from the main pipeline: agents are not starting from scratch. Existing CV text, cover letter text, coach properties, and reviewer feedback are all in the Notion row. The goal is to improve what exists, informed by what is already documented there.
Needs editing always means edit from the Notion entry. Every role with Status = Needs editing uses whatever is already inside its Notion row as the starting point — existing CV text, cover letter, coach properties, reviewer notes. Nothing is discarded. This rule holds regardless of what state.json says. state.json is crash recovery only (see State file section below).
career-datadata root (R-37). The personal-data files —01-writing-rules.md,02-professional-background.md,03-framework.md,linkedin-profile.md,pipeline-preferences.json,delivered-letters/, and the user's.dotx— load from${CAREER_DATA}/references/, the path the orchestrator resolves in itscareer-datadiscovery preflight. Every other file (self-checks,REFERENCES.md, skill docs, default.dotxtemplates) stays on${CLAUDE_PLUGIN_ROOT}. If${CAREER_DATA}is not set (direct or standalone invocation outside the orchestrator), locate thecareer-dataskill yourself, confirmcareer-data-marker.json, and apply the orchestrator's healthy / damaged / absent outcomes before reading. A configured user's missingcareer-datais a hard stop — never silently fall back to blank templates.
Universal spawn parameter —
gap_handling_mode(2026-07-14). Resolve$GAP_HANDLING_MODEonce at run start from${CAREER_DATA}/references/pipeline-preferences.json→gap_handling(disabledwhen the value isdisabledor empty,enabledotherwise — an absent key on an older config meansenabled). Every subagent spawn in this pipeline passesgap_handling_mode=$GAP_HANDLING_MODE, exactly likeCAREER_DATA— every agent, every round, every re-spawn and crash-recovery fallback, no exceptions. Whendisabled, no agent may produce, request, or enforce gap framing anywhere (see the identical rule incareer-engine-new-application/SKILL.mdfor the per-agent meaning). A one-word routing token — for the letter-writer it rides inside its input contract's routing-token allowance, never as content.
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.
- 9d ago First seen · 562 lines · 102 tokens per session scan C d3aba228bc34
career-engine-edit is a skill published in the GitHub repository spinningrachel/career-engine (4 stars, last pushed 28d ago), licensed MIT. It adds 102 tokens to every session and 23,651 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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