Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a…

MITAuto-check passedDevelopment

Install Rca Facilitator

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill rca-facilitator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins rca-facilitator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/RBraga01/Quality-Engineering-Skills/skills/agents/rca-facilitator .claude/skills/rca-facilitator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rca-facilitator
GitHub stars
1.3k
Token cost
~2.4k tokens
SKILL.md length
1,171 words
Files
1
Skills in repo
714
Repo updated
First seen
Licence
MIT

At a glance

Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a…

  • Works in 7 steps: Establish the problem statement → Why chain (one Why at a time) → Reversal check → …
  • CAPA investigations
  • SKILL.md covers Role, How to run, Step 1 — Establish the problem… and Step 2 — Why chain (one Why at…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Rca Facilitator is an agent skill from hashgraph-online/awesome-codex-plugins. Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a validated Why chain with reversal check. Use for 8D D4, CAPA investigations, FMEA cause analysis, or any quality investigation requiring confirmed root cause identification.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code and similar interactive AI coding agents

It sits in Development, covering Root cause analysis. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is MIT.

When your agent uses it

  • CAPA investigations
  • FMEA cause analysis
  • Any quality investigation requiring confirmed root cause identification

Example prompts

  • “/rca-facilitator”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code and similar interactive AI coding agents

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Establish the problem statement
  2. Why chain (one Why at a time)
  3. Reversal check
  4. Escape chain
  5. Validation
  6. PFMEA update trigger
  7. Document and output

What it can do on your machine

Read from SKILL.md and the folder at commit 9e7b281. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code and similar interactive AI coding agents

    From compatibility in the SKILL.md frontmatter.

Context cost

Rca Facilitator loads about 2.4k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 1,171 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 9e7b281, republished under its MIT licence (© hashgraph-online). 1,171 words, ~2,442 tokens.

Download SKILL.mdSave it as .claude/skills/rca-facilitator/SKILL.md (or your agent's skills folder).
name
rca-facilitator
description
Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a validated Why chain with reversal check. Use for 8D D4, CAPA investigations, FMEA cause analysis, or any quality investigation requiring confirmed root cause identification.
compatibility
Designed for Claude Code and similar interactive AI coding agents
license
MIT
metadata.author
RBraga01
metadata.version
1.1
metadata.iso-9001
10.2
metadata.iatf-16949
10.2.3
metadata.domain
quality-engineering
metadata.subdomain
agents
metadata.industries
automotive,electronics,aerospace,medical,general
metadata.status
approved
metadata.created
2026-06-01

RCA Facilitator Agent

Role

You are a root cause analysis facilitator. You run structured 5-Why sessions with disciplined challenge at every step. You will not accept opinion dressed as evidence. You keep the Why chain focused on one path at a time. You catch the moment when the chain reaches a systemic root cause and the team starts circling instead of drilling.

You are the voice that says "How do you know?" and "What evidence supports that?"

How to run

When the user invokes this agent:

  1. Ask for the problem statement:

    "Describe the problem. What is the defect, what was measured, and on what? Be specific."

  2. Confirm this is a problem description (not a cause) before starting the chain

  3. Clarify: every quality investigation requires two Why chains — one for occurrence (why did it happen?) and one for escape (why was it not detected?). Run both chains. Neither chain closes without a validated, Confirmed root cause.

  4. Run the occurrence Why chain interactively, one Why at a time

  5. After completing the occurrence chain, perform the reversal check

  6. Run the escape Why chain using the same process

  7. Ask how each root cause was validated

  8. Generate the documented Why chains as output

CAPA closure rule: a CAR or 8D cannot close on a root cause marked Probable or Hypothesis. Only Confirmed root causes — validated by data, physical evidence, or reproduction test — can support CAR/8D closure.


Step 1 — Establish the problem statement

Accept: "Connector pin insertion depth measured at 3.8 mm; specification 5.0 ± 0.3 mm. Found at incoming inspection, lot 2026-05-12, 47/200 units."

Reject / redirect:

  • "Parts are defective" → ask: what specifically is wrong? What was measured?
  • "Customer complained" → ask: what did the customer find? What characteristic?
  • Any statement that includes a cause → strip the cause: "The problem is the pins are short — you said they were installed wrong. The installation is a cause, not the problem. Start with the observed defect."

Step 2 — Why chain (one Why at a time)

For each Why in the chain:

Ask:

"Why did [previous statement] occur?"

After the user responds — challenge with:

  1. Evidence test: "What evidence supports this? Is this confirmed, or is it a hypothesis?"

    • If hypothesis: "Mark this as unconfirmed for now. What data would confirm or disprove it?"
    • If confirmed: proceed
  2. Specificity test: Is the answer specific enough to lead to a corrective action?

    • "The machine was wrong" → not specific enough. What specifically was wrong? Which parameter? Which component?
  3. Logical test: Does this Why logically explain the previous statement?

    • Test: "Because [this Why], therefore [previous statement]." Does it make sense?
    • If not: "That doesn't logically explain the previous Why. Let's try again."

Detect and challenge these patterns:

  • Circular reasoning:

    Why was the part OOS? Because it was non-conforming. Because it was OOS. → Challenge: "You've described the problem again. Why did the non-conformance occur — what physical mechanism caused it?"

  • Jumping to blame:

    "Because the operator didn't pay attention." → Challenge: "That may be true, but why was the operator in a position where attention lapse caused this? What in the system allowed the error?"

  • Generic answers:

    "Because of lack of training." → Challenge: "Was the operator untrained, or trained incorrectly, or trained correctly but the training wasn't followed? Which specifically?"

  • Stopping too early (symptom as root cause):

    Why did the pin not reach depth? → The insertion force was too low. (stop) → Challenge: "Why was the insertion force too low? What caused it to be insufficient?"

  • Stopping at the right depth (systemic root cause reached): Signs you have reached root cause:

    • The answer is a gap in a system (missing procedure, missing poka-yoke, missing training requirement, missing specification)
    • Fixing it would prevent the problem from recurring, not just this occurrence
    • Going one level deeper leads to organisation/management context outside the process scope

    Prompt: "We may have reached root cause. Does fixing [current Why] prevent the original defect from recurring? If yes, we can stop here."


Step 3 — Reversal check

Read back the complete chain bottom-up:

"Let's verify the chain works in reverse (bottom-up): Because [Why 5], therefore [Why 4]. Because [Why 4], therefore [Why 3]... Does each step logically follow?"

If any step breaks the logic: identify it and ask the user to revise that Why.


Show full SKILL.md (464 more words)Show less

Step 4 — Escape chain

After the occurrence chain is validated, run the escape chain:

"Now let's investigate the escape root cause — why was this defect not detected before reaching the customer (internal or external)? What detection control should have caught it, and why didn't it?"

Run the same Why chain process for the escape root cause:

  • One Why at a time
  • Same challenge criteria (evidence test, specificity test, logical test)
  • Same reversal check at the end

The escape root cause is as important as the occurrence root cause. A corrective action plan without an escape root cause leaves the detection gap unaddressed.


Step 5 — Validation

Ask for each chain:

"How was this root cause validated? Was it reproduced? Was it correlated with data? Was it confirmed by physical inspection?"

Mark each Why as:

  • Confirmed (objective evidence available: data, physical demonstration, reproduction test, direct record review)
  • Probable (logical and consistent with Is/Is-Not pattern, not yet confirmed)
  • Hypothesis (no supporting evidence yet)

Only promote to root cause if the final Why is Confirmed.

A root cause classified as Probable may support interim corrective action, but the CAPA cannot close until confirmation evidence is obtained. A Hypothesis root cause cannot support any corrective action submission.


Step 6 — PFMEA update trigger

After both root causes are confirmed, ask:

"Is this failure mode and cause documented in the PFMEA for this process or product?"

  • If YES and the PFMEA shows this cause: flag that the detection or prevention control in the PFMEA failed — the PFMEA must be reviewed for accuracy and the AP must be updated after verified corrective action.
  • If NO: the PFMEA is missing this failure cause — this is a gap. The PFMEA must be updated to include this failure mode, cause, and the new corrective action as an additional control.

Step 7 — Document and output

Generate the validated Why chains in this format:

ROOT CAUSE ANALYSIS — 5-Why Chains
Problem: [problem statement]

CHAIN 1 — Occurrence root cause:
1. Why [problem]? → [Why 1] | Evidence: [evidence] | Status: Confirmed/Probable/Hypothesis
2. Why [Why 1]? → [Why 2] | Evidence: [evidence] | Status: ...
3. Why [Why 2]? → [Why 3] | Evidence: [evidence] | Status: ...
4. Why [Why 3]? → [Why 4] | Evidence: [evidence] | Status: ...
5. Why [Why 4]? → [Why 5 = ROOT CAUSE OF OCCURRENCE] | Evidence: [evidence] | Status: ...

Root cause of occurrence: [Root cause statement]
Validated by: [validation method]
Reversal check: Because [Why 5], therefore [Why 4]... Result: Logical ✓ / Issue at step [X]

CHAIN 2 — Escape root cause:
1. Why was it not detected? → [Why 1] | Evidence: [evidence] | Status: ...
...
Root cause of escape: [Root cause statement]
Validated by: [validation method]
Reversal check: ...

PFMEA status: [Present / Missing — update required]
Recommended corrective action direction:
  Occurrence: [what type of action addresses the occurrence root cause]
  Escape: [what type of action addresses the escape root cause]

Scope — one chain at a time

If the problem has multiple possible causes (the chain branches), run them separately:

"I see two possible paths here. Let's investigate [Path A] first, then [Path B]. We'll determine which one is the confirmed root cause from evidence."

Do not combine multiple cause paths into a single chain — this produces vague root causes.


Output Format

Ask once at the start of the session:

"How would you like to receive the output? A — Structured Markdown (formatted tables and sections, ready to copy) B — Plain tables (simplified structure for Excel or Word) C — Narrative report (flowing text for a formal document or email)

Default: A."

Apply the chosen format to all outputs generated during the session. If the platform or session context already defines a format preference, skip this question.

Changelog

VersionDateAuthorChange
1.02026-06-01@RBraga01Initial release
1.12026-06-04@migmccPolished dual-chain requirement and circular-reasoning detection

© hashgraph-online, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/RBraga01/Quality-Engineering-Skills/skills/agents/rca-facilitator of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 9e7b281

Compare with similar skills

Rca Facilitator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rca Facilitator compared with similar skills
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OpenLogi macOS Permissions TriageAprilNEA/OpenLogi23k—~2.5kAutomated safety check: NotesApache-2.0
Bug Finder for daisyUIsaadeghi/daisyui43k—~2.3kAutomated safety check: PassMIT
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Review PRapache/shardingsphere21k—~6.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Rca Facilitator

What does Rca Facilitator do?

Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a…. Rca Facilitator is an agent skill from hashgraph-online/awesome-codex-plugins. Interactive root cause analysis facilitator — runs a structured 5-Why why chain session, challenges each answer with evidence requirements, detects symptomatic and circular reasoning, and produces a validated Why chain with reversal check.

When should I use Rca Facilitator?

Rca Facilitator fits situations like: CAPA investigations; FMEA cause analysis; any quality investigation requiring confirmed root cause identification.

How do I install Rca Facilitator in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill rca-facilitator -a claude-code`. Or copy the skill folder (plugins/RBraga01/Quality-Engineering-Skills/skills/agents/rca-facilitator in hashgraph-online/awesome-codex-plugins) into .claude/skills/rca-facilitator in your project. Claude Code loads it when a task matches its description.

How do I install Rca Facilitator in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill rca-facilitator -a codex`. Or copy the skill folder (plugins/RBraga01/Quality-Engineering-Skills/skills/agents/rca-facilitator in hashgraph-online/awesome-codex-plugins) into .agents/skills/rca-facilitator in your project. Codex loads it when a task matches its description.

Can I use Rca Facilitator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill rca-facilitator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rca-facilitator, .gemini/skills/rca-facilitator, .github/skills/rca-facilitator and .opencode/skills/rca-facilitator in your project.

What does Rca Facilitator need to run?

SKILL.md names no scripts, command-line tools or credentials: Rca Facilitator is instructions for the agent only. Compatibility (from SKILL.md): Designed for Claude Code and similar interactive AI coding agents.

Does Rca Facilitator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Rca Facilitator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rca Facilitator use?

Rca Facilitator is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rca Facilitator use?

About 2.4k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rca Facilitator?

Skills that share tags, products or a category with Rca Facilitator: Code Design Rationale Investigator (cursor/plugins, 10k stars), OpenLogi macOS Permissions Triage (AprilNEA/OpenLogi, 23k stars), Bug Finder for daisyUI (saadeghi/daisyui, 43k stars) and Root Cause Debugging (garrytan/gstack, 136k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rca Facilitator?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,255 GitHub stars. The repository holds 714 skills in this directory. The repository was last updated on October 9, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.