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 maddhruv/absolute --skill absolute-deflakegit clone --depth 1 https://github.com/maddhruv/absoluteWrote 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/maddhruv/absolute/absolute-deflake)<a href="https://agentmods.dev/skills/maddhruv/absolute/absolute-deflake"><img src="https://agentmods.dev/badge/skills/maddhruv/absolute/absolute-deflake/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/maddhruv/absolute/absolute-deflake"><img src="https://agentmods.dev/badge/skills/maddhruv/absolute/absolute-deflake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01286 |
| Opus 5.5 | $0.00029 | $0.00514 |
| Sonnet 5.5 | $0.00015 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade A, and why
absolute-deflake 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 1mo 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start your first response with the 🧪 emoji.
Absolute Deflake
Find tests that pass and fail nondeterministically, diagnose the root cause of each, and fix it — not by retrying or skipping, but by removing the source of nondeterminism. Output is evidence (failure rate per test) → cause → fix, verified by repeated runs.
Runs the shared engine in references/health-engine.md — read it for the
DETECT → SCAN → TRIAGE → FIX → VERIFY → REPORT loop and the safety contract. This file
covers only what's specific to flaky tests.
When to use
- "Our CI is flaky", "this test fails randomly", "fix the intermittent failures".
- A test passes locally but fails in CI (or vice versa), or fails ~1 in N runs.
- Burning down a backlog of
retry/skip-marked tests that mask real flakiness.
Not for tests that fail deterministically — that's a real bug or a real regression
(/absolute work for a fix, or just fix it). deflake targets nondeterministic failures.
What it scans
Establish flakiness empirically — a test isn't flaky because someone said so. Use
preferences.health.deflakeRuns from config as the default N for repeat-runs (else 20):
| Ecosystem | Repeat-run / detect |
|---|---|
| Jest/Vitest | run suite N× (--run loop), randomize order (--shuffle / testSequencer) |
| pytest | pytest-randomly + pytest --count=N (pytest-repeat); -p no:randomly to A/B |
| Go | go test -count=N -shuffle=on ./..., -race |
Also mine signals: existing retry/flaky/skip annotations, CI history if reachable, and
run the suite both in isolation and in full/parallel — order- and concurrency-
dependent failures only show one way. Record a failure rate per suspect test.
Common root causes (diagnose, don't guess)
| Cause | Tell | Fix |
|---|---|---|
| Test-order / shared state | passes alone, fails in suite (or vice versa) | isolate state; reset/teardown between tests |
| Time / clock | fails near midnight, DST, or under load | fake timers / inject clock; no real sleep |
| Async race / missing await | fails under parallelism or slow CI | await the actual condition; no fixed timeouts |
| Randomness | fails ~X% with no pattern | seed the RNG; fix the seed in tests |
| Network / external I/O | fails offline or on slow links | mock/stub the boundary |
| Unordered collections | fails on map/set iteration order | sort before asserting |
| Resource leak / port reuse | fails on repeat or parallel runs | unique resources; clean up |
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.
- 1mo ago First seen · 120 lines · 73 tokens per session scan A 6618e89e95a6
absolute-deflake is a skill published in the GitHub repository maddhruv/absolute (223 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 1,286 once invoked, about $0.0003 per session on Opus 5.5. 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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