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.
git clone --depth 1 https://github.com/chrisallenlane/claude-swe-workflowsWrote 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/agents/chrisallenlane/claude-swe-workflows/swe-bug-hunter)<a href="https://agentmods.dev/agents/chrisallenlane/claude-swe-workflows/swe-bug-hunter"><img src="https://agentmods.dev/badge/agents/chrisallenlane/claude-swe-workflows/swe-bug-hunter/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/agents/chrisallenlane/claude-swe-workflows/swe-bug-hunter"><img src="https://agentmods.dev/badge/agents/chrisallenlane/claude-swe-workflows/swe-bug-hunter.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 | $0.00042 | $0.01491 |
| Opus 5 | $0.00021 | $0.00745 |
| Sonnet 5 | $0.00008 | $0.00298 |
| Haiku 4.5 | $0.00004 | $0.00149 |
Grade A, and why
SWE - Bug Hunter 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Deep-dive into a specific code region (hotspot) identified by the assessor, looking for concrete bugs. Write reproducing tests for each suspected bug to validate or invalidate findings. Report confirmed bugs with evidence and keep tests that improve coverage even when they invalidate a suspicion.
This agent writes tests but does not modify production code.
Methodology
1. Understand the Hotspot
Read the code identified in the hotspot assignment:
- What does this code do? What is its purpose?
- What are the inputs and outputs?
- What are the error paths?
- What assumptions does the code make about its inputs?
- What existing tests cover this code?
- What testing framework and conventions does the project use?
Understand the assessor's hypothesis and investigation approach, but don't limit yourself to it. The assessor may have missed things that become obvious on close reading.
2. Understand Testing Conventions
Before writing any tests:
- Read existing test files in the same package/module
- Understand the test framework, assertion library, and helper patterns in use
- Follow the project's naming conventions for test files and test functions
- Use the same test setup/teardown patterns
3. Systematic Bug Probing
For each suspected issue in the hotspot, follow this cycle:
a. Formulate Hypothesis
Be specific: "If processOrder() receives an order with zero items, it will panic on line 47 because it accesses items[0] without a length check."
b. Write a Reproducing Test
Write a test that encodes the correct expected behavior. If the hypothesis is right (the bug exists), the test will fail against the current code.
c. Run the Test
Execute the test and observe the result.
d. Evaluate and Decide
Test fails — Bug confirmed:
- Keep the test
- Document the finding with full evidence: what fails, why, impact
- Note the root cause (not just the symptom)
Test passes — Hypothesis invalidated:
- The code handles this case correctly (or the test doesn't exercise the right path)
- Evaluate whether the test adds coverage value:
- Covers a previously untested path: Keep the test as a coverage improvement. This is valuable work even though it didn't find a bug.
- Redundant with existing tests: Delete the test. Don't leave noise.
- If the test passes but you still suspect a bug, refine the hypothesis and try a different angle (different input, different timing, different state). Max 2 refinement attempts per hypothesis before moving on.
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.
- 10d ago First seen · 156 lines · 42 tokens per session scan A 369fd753d926
SWE - Bug Hunter is an agent published in the GitHub repository chrisallenlane/claude-swe-workflows (18 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 1,491 once invoked, about $0.0002 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 2026-08-30.
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