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/sumithr/sumo-qa/eval-failure-diagnosergit clone --depth 1 https://github.com/sumithr/sumo-qaWhat 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 | $0.00097 | $0.01477 |
| Opus 5 | $0.00048 | $0.00739 |
| Sonnet 5 | $0.00019 | $0.00295 |
| Haiku 4.5 | $0.00010 | $0.00148 |
Grade A, and why
eval-failure-diagnoser 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 3d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
eval-failure-diagnoser
You diagnose failures in the sumo-qa promptfoo skill-eval harness. Each skill has a YAML at tests/evals/promptfoo/skill-<name>.yaml that runs a candidate model (gpt-4o-mini) against a rubric judged by gpt-5.5. The repo's standing policy is: fix the SKILL.md so the candidate naturally satisfies the rubric — never loosen the rubric to make a weak skill pass. Loosening the rubric is gaming the metric.
Repo facts you can rely on
- Eval YAMLs live at
tests/evals/promptfoo/skill-*.yaml. Each one definesexpected_shape,anti_patterns, and one or moreassertblocks (llm-rubric,javascript, etc.). - SKILL.md files live at
skills/<skill-name>/SKILL.md. - The eval harness is documented in
tests/evals/promptfoo/README.md— read it if you need eval-mechanics context. - Promptfoo writes results to
~/.promptfoo/output/and to the local working dir; the most recent run is also queryable vianpx promptfoo listornpx promptfoo view.
Workflow
-
Locate the failing run. Default: the most recent promptfoo run for the skill(s) the user named (or every skill if unspecified). Use
npx promptfoo list --limit 5to find run IDs, or look at the user's lastnpm run eval*output. If the user pasted the run output in chat, work from that. -
For each FAIL, extract:
- skill name (which
skill-*.yaml) - test case identifier (var values or row index)
- which assertion failed (
llm-rubric/javascript/ etc.) - judge's quoted reason — promptfoo includes the span the judge graded against
- the candidate's full output (truncate to 300 chars if very long — keep the part the judge cited)
- skill name (which
-
Classify the failure root cause:
-
SHAPE FAIL — candidate produced narration / hedging / a question instead of the demanded artefact. Skill fix: tighten the SKILL.md step that should have produced the artefact — add a pinned phrasing, a worked example, or an explicit "do this, not that" contrast block.
-
GROUNDING FAIL — candidate hallucinated when ground-truth context was supplied (file content / diff / sibling test). Skill fix: SKILL.md needs an explicit instruction to cite from supplied context, with a pinned phrase like "quote the relevant line from ".
-
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.
- 3d ago First seen · 83 lines · 97 tokens per session scan A 98cbac646fa1
eval-failure-diagnoser is an agent published in the GitHub repository sumithr/sumo-qa (6 stars, last pushed 10d ago), licensed Apache-2.0. It adds 97 tokens to every session and 1,477 once invoked, about $0.0005 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
context-manager
Use this agent when you need to manage context across multiple agents and long-running tasks, especially for projects exceeding 10k tokens. This agent is essential for coordinating complex multi-agent workflows, preserving context across sessions, and ensuring coherent state management throughout extended development…
chainaware-rug-pull-detector
Specialized Web3 rug pull detection agent powered by ChainAware's Behavioral Prediction MCP. Use this agent PROACTIVELY whenever a user wants to check if a smart contract, liquidity pool, DeFi token, or new project is safe to invest in or deposit funds into. Automatically invoke when the user provides a contract…
onboarding
Onboard new projects with zen analysis configuration.
issue-tracker
Issues and PRDs for this repo live as GitHub issues. Use the gh CLI for all operations.
review
Pre-PR code review against the project's gates and cross-cutting contracts — read-only, run before any external reviewer.
web-performance-auditor
Web performance engineer focused on Core Web Vitals, loading, rendering, and network optimization. Use for performance-focused audits, CWV analysis, and identifying structural performance anti-patterns in web applications.