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 w95/awesome-claude-corporate-skills --skill escalationgit clone --depth 1 https://github.com/w95/awesome-claude-corporate-skillsWrote 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/w95/awesome-claude-corporate-skills/escalation)<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/escalation"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/escalation/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/w95/awesome-claude-corporate-skills/escalation"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/escalation.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 | $0.00053 | $0.01774 |
| Opus 5 | $0.00026 | $0.00887 |
| Sonnet 5 | $0.00011 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
escalation 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 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.
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:
- escalation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Escalation Skill
You are an expert at determining when and how to escalate support issues. You structure escalation briefs that give receiving teams everything they need to act quickly, and you follow escalation through to resolution.
When to Escalate vs. Handle in Support
Handle in Support When:
- The issue has a documented solution or known workaround
- It's a configuration or setup issue you can resolve
- The customer needs guidance or training, not a fix
- The issue is a known limitation with a documented alternative
- Previous similar tickets were resolved at the support level
Escalate When:
- Technical: Bug confirmed and needs a code fix, infrastructure investigation needed, data corruption or loss
- Complexity: Issue is beyond support's ability to diagnose, requires access support doesn't have, involves custom implementation
- Impact: Multiple customers affected, production system down, data integrity at risk, security concern
- Business: High-value customer at risk, SLA breach imminent or occurred, customer requesting executive involvement
- Time: Issue has been open beyond SLA, customer has been waiting unreasonably long, normal support channels aren't progressing
- Pattern: Same issue reported by 3+ customers, recurring issue that was supposedly fixed, increasing severity over time
Escalation Tiers
L1 → L2 (Support Escalation)
From: Frontline support To: Senior support / technical support specialists When: Issue requires deeper investigation, specialized product knowledge, or advanced troubleshooting What to include: Ticket summary, steps already tried, customer context
L2 → Engineering
From: Senior support To: Engineering team (relevant product area) When: Confirmed bug, infrastructure issue, needs code change, requires system-level investigation What to include: Full reproduction steps, environment details, logs or error messages, business impact, customer timeline
L2 → Product
From: Senior support To: Product management When: Feature gap causing customer pain, design decision needed, workflow doesn't match customer expectations, competing customer needs require prioritization What to include: Customer use case, business impact, frequency of request, competitive pressure (if known)
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 · 182 lines · 53 tokens per session scan A 02de95d0ddbb
escalation is a skill published in the GitHub repository w95/awesome-claude-corporate-skills (198 stars, last pushed 6mo ago), licensed MIT. It adds 53 tokens to every session and 1,774 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
qa-session
Interactive QA: user reports bugs conversationally, agent files GitHub issues. Triggers: QA session, report bug, file issue, conversational QA, bug intake.
github-issue-creator
Turn error logs, screenshots, voice notes, and rough bug reports into crisp, developer-ready GitHub issues with repro steps, impact, and evidence.
bug-triage
Read all open bugs in production/qa/bugs/, re-evaluate priority vs. severity, assign to sprints, surface systemic trends, and produce a triage report. Run at sprint start or when the bug count grows enough to need re-prioritization.
fix
Applies targeted fix to known bug/lint error, verifies with same command that surfaced it. Triggers: fix, apply fix, fix bug, fix lint, targeted fix.
onboard
Sets up ai-toolkit in a project: symlinks, CLAUDE.md, intent interview. Triggers: onboard, setup project, install ai-toolkit, migrate project.
performance-profiling
Performance: golden signals, p50/p95/p99, flame graphs, load testing. Triggers: performance, slow, latency, p99, flame graph, bottleneck, memory leak.