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-intake)<a href="https://agentmods.dev/skills/spinningrachel/career-engine/career-engine-intake"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/career-engine-intake/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-intake"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/career-engine-intake.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 | $0.00150 | $0.21905 |
| Opus 5 | $0.00075 | $0.10953 |
| Sonnet 5 | $0.00030 | $0.04381 |
| Haiku 4.5 | $0.00015 | $0.02191 |
Grade B, and why
career-engine-intake scanned grade B 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 10d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
2. `~/.claude/settings.json` → `gap_handling` (legacy location, reachable only in Claude Code on the user's own machine). How it starts
The opening of the file, as written. The whole thing — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intake 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 has two modes with different procedures — Steps 0 through 0.9 below are Notion-fetch mode's procedure only. Inline mode is a separate, much shorter flow that ends after the bullet below; it never reaches Step 0, never creates $PIPE (Step 0.4 explicitly skips it), and never touches Steps 0.6 through 0.9 (priority queue, the batch coach spawn, the Coach Output Check, or Notion writeback) — there is no Notion row to write back to and no batch to queue.
- Inline mode — the user provides a URL or JD text directly in chat. No Notion fetch, no
$PIPE, noqueue.md. Run JD acquisition (the Step 0.5 fetch ladder, applied to the single provided URL/JD) on that one role, then spawncareer-coachwith Option 1 — Inline (not Option 2 — Option 2 is exclusively for the batch/Notion path, per its own "Always invoked... for Needs Research roles" scope statement), passing the fetched content directly in the spawn prompt along withCAREER_DATA=${CAREER_DATA}(resolved at Step −0.5 or by the orchestrator preflight, same as every other coach spawn in this file) — the coach needs it to read01/02/03for this role's analysis exactly as it does in the batch path; omitting it here is not a smaller ask, it is a missing required input. Deliver the coach's response conversationally — no Notion writeback, no further steps. Use when the user says something like "coach me on this role" and pastes a URL or JD, outside of the batch Notion-fetch flow. - Notion-fetch mode — queries the Notion database for Needs Research roles, runs JD acquisition and the career coach (Option 2) for each, writes all results to Notion, and updates Status to Researched. This is the standard "run intake" path, and the one Steps 0 through 0.9 below describe.
The career coach runs for every role that is not already coach-complete. If all roles in the queue are coach-complete, the coach spawn is skipped and the pipeline proceeds directly to Step 0.9 using existing values. The goal of this pipeline is to give the career coach complete information — full JD data for every role — before it makes any prioritization or writing decisions.
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.
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.
- 10d ago First seen · 415 lines · 150 tokens per session scan B dbfb99504637
career-engine-intake is a skill published in the GitHub repository spinningrachel/career-engine (4 stars, last pushed 28d ago), licensed MIT. It adds 150 tokens to every session and 21,905 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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