Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/telivityai/careerops/skillnpx skills add TelivityAI/careerops --skill skillgit clone --depth 1 https://github.com/TelivityAI/careeropsWhat 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 | $0.00044 | $0.00847 |
| Opus 5 | $0.00022 | $0.00424 |
| Sonnet 5 | $0.00009 | $0.00169 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
careerops 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 3d 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.
This is a copy
100% identical to careerops — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CareerOps skill
Career OS — operate on the user's board pack (exported JSON from the CareerOps web app) or their self-hosted Supabase project.
Doctrine (non-negotiable)
- No invented facts — employers, titles, dates, metrics, and skills must come from the user's materials / bullet memory / portfolio.
- No auto-apply / no auto-send — drafts are copy/paste only; the human submits on the employer site and sends their own mail.
- Tags ≠ stage moves — Apply / Stretch / Skip are suggestions until the user acts.
- Memory provenance —
body_originalis immutable; AI polish requires Accept; promotion is bidirectional (source_type/source_id↔ accomplishment links). resume_structis canonical — promotion and portfolio promote go through one atomic write that syncsresume_text(never silent dual-write divergence).- Generate retrieval — checked → role-linked → relevance → recency tie-break only (not newest-20).
- Enrichment = inbox + Accept — GitHub/LinkedIn (and similar) proposals land as candidates; never silent scrape into resume.
- Deferred forever (unless doctrine changes) — auto-apply, auto-send, silent scrape into resume, invented salary bands, ungated multi-agent writes of experience without accept.
See docs/DOCTRINE_MEMORY.md and docs/ROADMAP.md in the CareerOps repo.
Modes
| Mode | Purpose |
|---|---|
scan |
Review sourced roles; flag blocklist, age, remote, duplicates |
evaluate |
Build a decision pack (summary, risks, suggested call) |
rank |
Order roles by fit signals without applying |
tailor |
Draft resume/cover from checked / ranked materials only |
interview |
Prep angles + story-bank prompts (roadmap: durable interview events) |
followup |
Draft follow-up / thank-you notes (never send) |
outcome |
Record offer/reject notes (roadmap: structured offer fields via pack) |
advise |
Structured career advisor brief — materials-only past; labeled market judgment |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 62 lines · 44 tokens per session scan A 6350f51c0459
careerops is a skill published in the GitHub repository TelivityAI/careerops (53 stars, last pushed 9d ago), licensed Apache-2.0. It adds 44 tokens to every session and 847 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to careerops, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
interview-simulator
Simulate role-specific mock interviews, score each answer, and provide concrete feedback and model responses for interview preparation.
Automation Interview Prep
Prepare for SDET and automation interviews round by round, covering coding screens, framework design, API testing tasks, scenario questions, and STAR stories built from real testing work.
news
Reviews job application updates from Gmail, LinkedIn and platforms. Prepares drafts, presents executive summary by priority, validates with the user and sends replies.
browser-automation
Control a dedicated browser via playwright-cli. Use when automating web apps, scraping authenticated sites, filling forms, or navigating SPAs. Do NOT use for desktop apps or API-only integrations.
targets
Active direct sourcing. Registers and creates standout profiles on the 40 target companies' career sites, then applies to matching positions.
polish
Optimizes the user's LinkedIn profile and CV to align with their declared professional goals. Audits, redacts improvements, applies with per-section approval, and exports a polished CV to PDF.