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 skills add alexclowe/awesome-claude-cowork-plugins --skill reskilling-pathway-mappergit clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-pluginsWrote 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/alexclowe/awesome-claude-cowork-plugins/reskilling-pathway-mapper)<a href="https://agentmods.dev/skills/alexclowe/awesome-claude-cowork-plugins/reskilling-pathway-mapper"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/reskilling-pathway-mapper/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/alexclowe/awesome-claude-cowork-plugins/reskilling-pathway-mapper"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/reskilling-pathway-mapper.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 | $0.00027 | $0.00927 |
| Opus 5.5 | $0.00011 | $0.00371 |
| Sonnet 5.5 | $0.00005 | $0.00185 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
reskilling-pathway-mapper 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 23d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- reskilling-pathway-mapper — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in workforce transitions, internal mobility, and AI-resilient career pathways. When the user is working on layoff planning, internal mobility, redeployment, or career-pathway design, apply this knowledge automatically.
Core competencies
Role-to-role pathway mapping:
For displaced roles, identify three categories of adjacent destinations:
- Same-domain, AI-augmented — same field, but the worker now operates AI tools rather than doing the manual task (e.g., copywriter → AI content editor)
- Cross-domain, AI-resistant — judgment-heavy, relationship-heavy, or physically embodied work that's slower to automate (e.g., L1 support → field service tech)
- Adjacent and emergent — new roles that exist because of AI (e.g., QA tester → prompt engineer; junior dev → AI systems auditor; paralegal → AI legal-output reviewer)
Common high-displacement origin roles and viable pathways:
- QA tester → prompt engineer, AI evaluation specialist, AI red-teamer, test-automation engineer
- Junior developer → AI systems auditor, MLOps engineer, dev-tools support engineer, technical writer for AI products
- Customer support L1 → AI training data specialist, customer success ops, escalation specialist (high-context cases AI can't resolve)
- Copywriter / content marketer → AI content editor, brand voice steward, content strategist, SEO/AEO specialist
- Bookkeeper / data entry → financial operations analyst, controls/compliance reviewer, AI-output auditor
- Paralegal / legal research → e-discovery specialist, contract lifecycle ops, AI legal-output reviewer
- Translator → MT post-editor, localization QA, cultural consultant
- Recruiter sourcer → recruiting ops, candidate-experience specialist, hiring-process analyst
- Truck driver (long-haul) → last-mile/short-haul, logistics dispatcher, fleet maintenance
- Radiology tech (assistive role) → imaging informatics specialist, PACS administrator
- Telemarketing / outbound sales → relationship sales, partnerships, customer success
Pathway evaluation criteria:
- Skill overlap — what % of current capabilities transfer
- Reskilling time — weeks/months of learning required
- Wage retention — does the destination role preserve income?
- AI-resilience horizon — 2-year vs 5-year vs 10-year exposure
- Growth direction — is the destination role expanding or contracting?
- Geographic constraints — remote, hybrid, on-site requirements
Reskilling resource categories:
- Internal mobility programs and apprenticeships
- Community college and workforce-board partnerships (WIOA-funded)
- Vendor certifications (cloud, security, data)
- Bootcamps and accelerated programs
- On-the-job rotation and shadowing
- Employer-sponsored learning stipends
WARN Act and severance-window planning:
- WARN Act 60-day notice for 50+ layoffs at single site (federal); state mini-WARN laws may be stricter (CA, NY, IL, NJ)
- Use the notice window for active reskilling and internal-transfer matching
- Document redeployment offers as part of severance package — improves both employee outcomes and litigation defensibility
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.
- 23d ago First seen · 66 lines · 27 tokens per session scan A 718fc3676d27
reskilling-pathway-mapper is a skill published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 27 tokens to every session and 927 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-24.
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