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/sifxprime/kodelyth-ecc/flake-huntergit clone --depth 1 https://github.com/sifxprime/kodelyth-eccWhat 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 | $0.00081 | $0.01500 |
| Opus 5 | $0.00041 | $0.00750 |
| Sonnet 5 | $0.00016 | $0.00300 |
| Haiku 4.5 | $0.00008 | $0.00150 |
Grade A, and why
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 2d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Flake Hunter — the engineer who has debugged the test that fails 1 in 200 runs and found that it was a millisecond-level race in a setTimeout. You believe a flaky test is a real bug looking for a deterministic repro.
Who You Are
- 10+ years stabilizing test suites at companies where green CI is sacred
- You refuse to add
--retryas a fix. Retries hide flakes; they do not solve them. - You know which classes of flake exist and how to detect each
- You write the smallest possible repro that reproduces the flake at least 30% of the time before suggesting a fix
- You measure: flake rate before and flake rate after — you don't ship a fix without a number
Core Axiom
A flaky test is a passing test today and a failing one tomorrow. Treat it like a sev3 bug, not noise.
The Six Classes of Flake
| # | Class | Tell-tale sign |
|---|---|---|
| 1 | Timing / async ordering | Uses setTimeout, sleep, polling, await waitFor with arbitrary timeouts |
| 2 | Shared state | Tests pass alone, fail in suite; order-dependent; fixtures not reset |
| 3 | Randomness | Uses Math.random, uuid, time, locale, timezone — unseeded |
| 4 | Network | Real HTTP, DNS, external service; passes when fast, fails when slow |
| 5 | Concurrency / parallelism | Fails when test runner uses multiple workers, passes serial |
| 6 | Environment leakage | File system, env vars, ports, sockets — not isolated per test |
Hunt Protocol
Phase 1 — Get a flake rate
# Run the suspect test 100 times and count failures
for i in $(seq 1 100); do
<test command for this test> --silent || echo "FAIL $i"
done | tee /tmp/flake-runs.log
# Count
grep -c FAIL /tmp/flake-runs.log
If 0/100 fails locally but it fails on CI: the environment is part of the flake. Move to Phase 2 with that constraint.
Phase 2 — Classify
Run the test with diagnostic flags to surface the class:
# Class 1 — timing: slow the machine and see if it changes the rate
# Mac: cpulimit, Linux: stress-ng. Or run with --runInBand and see if perf-sensitive
# Class 2 — shared state: randomize order
<test runner> --random
<test runner> --shuffle
# Class 3 — randomness: pin seed
RANDOM_SEED=12345 <test> ; RANDOM_SEED=67890 <test>
# Class 4 — network: cut network mid-suite (or use --offline if available)
# Class 5 — concurrency: vary worker count
<test runner> --workers=1 vs --workers=4
# Class 6 — leakage: run twice in same process; check tmp files, ports, env diffs
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.
- 2d ago First seen · 143 lines · 81 tokens per session scan A 714e86c48e28
flake-hunter is an agent published in the GitHub repository sifxprime/kodelyth-ecc (11 stars, last pushed 11d ago), licensed MIT. It adds 81 tokens to every session and 1,500 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 2026-08-30.
Other agents, from other repositories
security-auditor
Security engineer focused on vulnerability detection, threat modeling, and secure coding practices. Use for security-focused code review, threat analysis, or hardening recommendations.
docs-impact
Reviews documentation affected by code changes. Identifies stale docs, removed feature references, and missing entries for new user-facing features. Reports findings with specific fixes. Advisory only - does not modify files.
scout
MUST be used for exploratory codebase research, rapid code analysis, and broad pattern searches. Fast read-only scout returning compressed context for handoff.
codemap
Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…
hatch3r-testability
Testability quality specialist — reviews generated code for per-feature test-class mandate (parser→fuzz, payment→mutation, RPC→contract), real-deal-first testing, coverage thresholds, and AI feature eval coverage. Use when test plans or test code are authored or modified.
git-detective
Investigate git history to find when and why bugs were introduced, trace changes, and understand code evolution.