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 RBraga01/Quality-Engineering-Skills --skill fishbone-analysisgit clone --depth 1 https://github.com/RBraga01/Quality-Engineering-SkillsWrote 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/rbraga01/quality-engineering-skills/fishbone-analysis)<a href="https://agentmods.dev/skills/rbraga01/quality-engineering-skills/fishbone-analysis"><img src="https://agentmods.dev/badge/skills/rbraga01/quality-engineering-skills/fishbone-analysis/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/rbraga01/quality-engineering-skills/fishbone-analysis"><img src="https://agentmods.dev/badge/skills/rbraga01/quality-engineering-skills/fishbone-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 125 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00074 | $0.02058 |
| Opus 5 | $0.00037 | $0.01029 |
| Sonnet 5 | $0.00015 | $0.00412 |
| Haiku 4.5 | $0.00007 | $0.00206 |
Grade A, and why
fishbone-analysis 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 9d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fishbone (Ishikawa) Analysis
When to use
Use fishbone analysis to brainstorm all possible causes of a quality problem before running 5-Why. It prevents tunnel vision and ensures no cause category is overlooked. Particularly valuable for complex defects with multiple potential contributing factors.
Typical use: 8D D4 brainstorming session, CAPA root cause investigation, initial problem analysis.
Prerequisites
- Problem clearly defined with Is/Is-Not or 5W2H
- Cross-functional team (quality, production, engineering at minimum)
- Access to the process, machine, or product where defect occurred
Required Fishbone Checklist
☐ Problem statement defined and agreed before starting — no cause language in the problem statement ☐ All 6M categories addressed — at least one entry per M, or documented justification if a category is not applicable ☐ Brainstorming completed before any evaluation or elimination — do not evaluate while generating ☐ Every cause classified as Confirmed, Probable, or Unlikely using objective evidence — not opinion alone ☐ Confirmed = supported by data or physical evidence; Probable = logical, consistent with Is/Is-Not, not yet confirmed; Unlikely = contradicted by data ☐ Confirmed and Probable causes cross-checked against existing PFMEA failure cause entries before proceeding to 5-Why ☐ Each Confirmed or Probable cause carries forward to its own 5-Why chain ☐ After root cause confirmed: horizontal deployment check — could the same cause exist in similar parts, processes, or product families?
The 6M Framework
The six main "bones" of the fish. All six must be addressed. If a category genuinely does not apply, document: "No causes identified in this category after structured team review — not applicable because [reason]."
1. Man (Human Factors)
Questions to ask:
- Was the task performed by a trained, qualified operator?
- Is competence documented and current?
- Was the operator following the correct work instruction?
- Could fatigue, shift change, or distraction contribute?
- Is the task ergonomically difficult or error-prone?
- Is the same defect found on all shifts or only one? (single-shift → operator-specific)
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.
- 9d ago First seen · 202 lines · 74 tokens per session scan A d23711b27fbd
fishbone-analysis is a skill published in the GitHub repository RBraga01/Quality-Engineering-Skills (28 stars, last pushed 4d ago), licensed MIT. It adds 74 tokens to every session and 2,058 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.
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