Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skillsnpx agentmods add skills/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfigWrote 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/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfig)<a href="https://agentmods.dev/skills/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfig"><img src="https://agentmods.dev/badge/skills/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfig/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/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfig"><img src="https://agentmods.dev/badge/skills/olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills/web3-case-study-role-misconfig.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 | $0.00040 | $0.03319 |
| Opus 5 | $0.00020 | $0.01659 |
| Sonnet 5 | $0.00008 | $0.00664 |
| Haiku 4.5 | $0.00004 | $0.00332 |
Grade A, and why
web3-case-study-role-misconfig 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 12d 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 web3-case-study-role-misconfig — 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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CASE STUDY: ROLE MISCONFIGURATION IN A YIELD AGGREGATOR
Bug Class: Access Control | Severity: Critical/Medium | Payout Range: $10K–$50K This file shows how to apply the full 10-class methodology to a real yield aggregator target.
TARGET PROFILE (Anonymized)
| Field | Value |
|---|---|
| Protocol Type | Yield aggregator — stablecoin → lending protocol → harvest → DEX → reward token |
| Max Bounty | $50K (Critical) |
| TVL | Low (fresh program, under $100K) |
| Core Contracts | Vault.sol, RewardsDistributor.sol |
| Program Age | ~5 days when hunted (fresh = low competition) |
| Prior Audits | Firm A (16 findings, all Risk Accepted) + Firm B (18 findings, all Risk Accepted) |
Scorecard: Max bounty (+2) + custom math (+1) + recent code (+1) + known prior audits (+1) + public source (+1) + program new (+2) = 8/10 → HUNT
Why this scores high: Fresh program on a live bounty platform + prior audits that accepted all risk = team is aware of issues but hasn't patched them. Hunt for what auditors missed or flagged but accepted.
ARCHITECTURE + FUND FLOW
User deposits Stablecoin
↓ deposit(uint256 amount)
Vault.sol stores:
- deposits[user] += amount
- totalDeposited += amount
- depositTimestamp[user] = block.timestamp
↓ safeTransferFrom(user, address(this), amount)
↓ lendingProtocol.supply(stablecoin, amount, address(this), 0)
Interest-bearing token accrues in Vault.sol balance
↓ (periodic) _performHarvest()
aToken balance > totalDeposited + DUST_THRESHOLD
↓ lendingProtocol.withdraw(stablecoin, harvestAmount - 1, address(this))
↓ dex.exactInputSingle(stablecoin → rewardToken)
↓ RewardsDistributor.distribute(rewardToken, amount)
RewardsDistributor tracks:
- cumulativeRewardPerShare updates
- users can call claimFor(user) to collect rewardToken
User withdraws:
↓ withdraw(uint256 amount)
if block.timestamp < depositTimestamp[user] + LOCK_PERIOD:
withdrawFee applies (e.g. 0.5%)
lendingProtocol.withdraw(stablecoin, amount, user)
What ships with it
1 file 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.
- 12d ago First seen · 344 lines · 40 tokens per session scan A b08494af64ce
web3-case-study-role-misconfig is a skill published in the GitHub repository Olaradiallysymmetrical491/web3-bug-bounty-hunting-ai-skills (5 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 3,319 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 web3-case-study-role-misconfig, differing in 0 lines, and is treated as a copy.
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