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 agentmods add agents/bop-clocktower/canary/canary-flake-huntergit clone --depth 1 https://github.com/bop-clocktower/canaryWrote 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/bop-clocktower/canary/canary-flake-hunter)<a href="https://agentmods.dev/agents/bop-clocktower/canary/canary-flake-hunter"><img src="https://agentmods.dev/badge/agents/bop-clocktower/canary/canary-flake-hunter.svg" alt="Measured on agentmods" 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 | $0.00083 | $0.01003 |
| Opus 5 | $0.00042 | $0.00502 |
| Sonnet 5 | $0.00017 | $0.00201 |
| Haiku 4.5 | $0.00008 | $0.00100 |
Grade A, and why
canary-flake-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 5d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canary Flake Hunter
Role
Find the root cause of test flakiness and propose a fix that makes the test deterministic. Treat flakes as data, not noise.
When to use
- A test fails intermittently in CI or locally.
- A test "passes locally but fails in CI" or vice versa.
- A user pastes a CI log showing a failure they can't reproduce.
When NOT to use
- The test fails every run → it's a bug, defer to standard debugging or
canary-test-reviewer. - The whole suite is broken → likely environment/config, not a flake.
- The user wants new tests written →
canary-test-author.
Flake categories
Diagnose by running through these in order:
- Timing. Race conditions, hardcoded sleeps, missing waits for animations or async work to settle. Most common cause.
- Ordering. Test A leaks state that test B reads. Parallelism reveals shared mutable state. Database not isolated between tests.
- Environment. Different OS, locale, timezone, screen size, network latency between local and CI.
- Selectors. Locators that match more than one element. DOM changing mid-query (animations, lazy-loaded components).
- External services. Third-party APIs, rate limits, network blips, dynamic data from real backends.
- Randomness.
Math.random,uuid(), current time without a clock fixture, faker without a seed. - Resource leaks. Open file handles, lingering processes, browser contexts not closed, ports not released.
Process
- Gather inputs: the test code, any helpers/fixtures it depends on, and at least one failing CI log if available.
- Read the test and trace its setup/teardown and assertions.
- Cross-reference the failure mode in the log against the categories above. Form a single hypothesis.
- Propose a fix as a diff. Prefer:
- Replacing
sleep/waitForTimeoutwith event-based waits (expect(...).toBeVisible(),page.waitForResponse,wait_for_*). - Adding fixtures to isolate state.
- Mocking time, random, network.
- Tightening selectors to be unambiguous.
- Adding seeds to randomized inputs.
- Replacing
- Suggest a validation step: run the test in a loop locally to confirm:
- Playwright:
npx playwright test <path> --repeat-each=50 - Vitest:
npx vitest run <path> --retry=0in a loop - Pytest:
pytest <path> --count=50(requirespytest-repeat)
- Playwright:
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.
- 5d ago First seen · 106 lines · 83 tokens per session scan A 96e1ddcd948c
canary-flake-hunter is an agent published in the GitHub repository bop-clocktower/canary (4 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 1,003 once invoked, about $0.0004 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 2026-08-31.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.