ToolHive is a platform for running and managing Model Context Protocol servers, which give AI clients access to tools and data. Developers, platform teams, and enterprises use it to isolate MCP servers, control access, and monitor their operation. The catalogue entries provide agent workflows and configuration for operating ToolHive and its MCP servers.
Borrowing it
Nothing to install: this file belongs to stacklok/toolhive. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/stacklok/toolhive/main/.claude/skills/deflake/SKILL.mdgit clone --depth 1 https://github.com/stacklok/toolhiveWrote 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/stacklok/toolhive/deflake)<a href="https://agentmods.dev/skills/stacklok/toolhive/deflake"><img src="https://agentmods.dev/badge/skills/stacklok/toolhive/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/stacklok/toolhive/deflake"><img src="https://agentmods.dev/badge/skills/stacklok/toolhive/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 | $0.00048 | $0.01699 |
| Opus 5.5 | $0.00019 | $0.00680 |
| Sonnet 5.5 | $0.00010 | $0.00340 |
| Haiku 4.5 | $0.00005 | $0.00170 |
Grade A, and why
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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deflake Tests
Discovers, ranks, and plans fixes for flaky tests by analyzing GitHub Actions failures on main.
Arguments
/deflake # Full analysis: discover, rank, and plan fixes
/deflake --report # Report only: show flake rankings without planning fixes
/deflake --top N # Analyze and plan fixes for the top N flakes (default: 3)
Phase 1: Collect and Rank Flakes
Run the collection script. It handles all deterministic data collection and aggregation. If CI log formats change over time, update the script directly.
python3 .claude/skills/deflake/collect-flakes.py
The script outputs three sections:
- FLAKE REPORT — overall stats (total runs, failure rate, date range)
- RANKED FAILURES — table sorted by failure count with job, mode, and test name
- FAILURE DETAILS — per-test breakdown with links to each failed run
Phase 1 complete
Read the script output and use it directly for the report. The LLM's only job in this phase is to categorize each entry as a flake, real bug, or infra issue:
- Flake: Appears multiple times intermittently, interspersed with successful runs
- Real bug: Appeared after a specific commit and every run after that failed until a fix landed. Check
git logfor related fixes - Infra flake: Entries tagged
[INFRA]by the script, or failures with modeconnection refused/infra
Phase 2: Present the Report
Present the script output as a formatted report. Add categorization (flake / real bug / infra) to each entry. Example format:
## Flake Report — main branch
**Period**: 2026-04-01 to 2026-04-10
**Runs analyzed**: 23 total, 8 failed (35% failure rate)
### Top Flaky Tests
| Rank | Test | Job | Failures | Failure Mode |
|------|------|-----|----------|--------------|
| 1 | Workload lifecycle ... [It] should track ... | E2E (api-workloads) | 5/23 | timeout (120s) |
| 2 | ... | ... | ... | ... |
### Real Bugs (not flakes)
- [Test name] — Introduced by [commit], fixed by [commit/PR]
### Infra Failures
- [N] runs failed due to [description]
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.
- 1mo ago First seen · 158 lines · 48 tokens per session scan A a0967721a1c0
deflake is a skill published in the GitHub repository stacklok/toolhive (2,307 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 1,699 once invoked, about $0.0002 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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