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 2233admin/reverse-skill-evolver --skill competition-jwt-claim-confusiongit clone --depth 1 https://github.com/2233admin/reverse-skill-evolverWrote 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/2233admin/reverse-skill-evolver/competition-jwt-claim-confusion)<a href="https://agentmods.dev/skills/2233admin/reverse-skill-evolver/competition-jwt-claim-confusion"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-jwt-claim-confusion/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/2233admin/reverse-skill-evolver/competition-jwt-claim-confusion"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-jwt-claim-confusion.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 | $0.00128 | $0.00655 |
| Opus 5 | $0.00064 | $0.00328 |
| Sonnet 5 | $0.00026 | $0.00131 |
| Haiku 4.5 | $0.00013 | $0.00065 |
Grade A, and why
competition-jwt-claim-confusion 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 8d 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 competition-jwt-claim-confusion — 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition JWT Claim Confusion
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the decisive bug is not just "there is a JWT," but how headers, claims, and key selection turn into accepted identity.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Split the token path into parse, key lookup, signature or decryption, claim validation, and final acceptance.
- Record header fields, claims, key source, issuer, audience, and role mapping before mutating anything.
- Separate possession of a token from the exact service that accepts it.
- Keep parser behavior, trust policy, and resulting app session or privilege in one chain.
- Reproduce the smallest token-to-acceptance flow that proves the decisive confusion.
Workflow
1. Map Header And Key Selection
- Record header fields such as
alg,kid,typ,cty,jku, or embedded key material when present. - Note where keys come from: static config, JWKS, local file, cache, or dynamic lookup.
- Keep token parser, key selection path, and validation mode tied together.
2. Prove Claim-To-Privilege Acceptance
- Show how subject, audience, issuer, tenant, scope, role, or custom claims become app session, route access, or backend privilege.
- Record expiration, not-before, clock skew, issuer matching, audience matching, and claim normalization behavior.
- Distinguish token parse success from actual authorization success.
3. Reduce To The Decisive JWT Path
- Compress the result to the smallest sequence: token supplied -> parser or key path taken -> claim accepted -> resulting capability.
- Keep one canonical accepted token path and one mutated token path if confusion or bypass depends on a delta.
- If the task broadens into a larger OAuth redirect chain, hand back to the tighter OAuth skill.
What ships with it
2 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.
- 8d ago First seen · 51 lines · 128 tokens per session scan A f470a0467fdf
competition-jwt-claim-confusion is a skill published in the GitHub repository 2233admin/reverse-skill-evolver (13 stars, last pushed 28d ago), licensed MIT. It adds 128 tokens to every session and 655 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to competition-jwt-claim-confusion, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…