research-types-explanatory

research-types-explanatory is a skill for Claude Code, Codex from P47Phoenix/Claude-Plugins. It costs 77 tokens per session (1,161 once invoked), scanned A, original, MIT.

A research guide for explaining why something happened, using causal-analysis methods such as the five whys and fishbone diagrams. It is a supporting module used by a larger research workflow, not a standalone tool.

In plain words
What is it for?
Analyzing root causes, explaining incidents and defects, and organizing evidence about what led to an outcome.
Why use it?
It gives investigations a structured way to connect symptoms with their possible causes instead of only describing what happened.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/p47phoenix/claude-plugins/explanatory
Any agent
npx skills add P47Phoenix/Claude-Plugins --skill explanatory
Clone the repo
git clone --depth 1 https://github.com/P47Phoenix/Claude-Plugins

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for research-types-explanatory

README.md
[![agentmods](https://agentmods.dev/badge/skills/p47phoenix/claude-plugins/explanatory.svg)](https://agentmods.dev/skills/p47phoenix/claude-plugins/explanatory)
Your own site
<a href="https://agentmods.dev/skills/p47phoenix/claude-plugins/explanatory"><img src="https://agentmods.dev/badge/skills/p47phoenix/claude-plugins/explanatory.svg" alt="Measured on agentmods" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,161 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00077 $0.01161
Opus 5 $0.00039 $0.00580
Sonnet 5 $0.00015 $0.00232
Haiku 4.5 $0.00008 $0.00116

Measured 3d ago against content hash 60cc33de32c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research-types-explanatory 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.

research-agent/skills/research-types/explanatory/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Explanatory Research Sub-Skill

Router-dispatched paradigm sub-skill for Explanatory research. Not directly model-invocable; the research-agent parent loads this sub-skill only when Phase 1 detection classifies the question as Explanatory.

When This Sub-Skill Loads

The parent router dispatches here when the input question contains signals like:

  • "Why does X happen"
  • "Root cause of X"
  • "What caused X" / "What led to X"
  • "Explain why" / "Because"
  • Post-incident investigations, defect investigations, "RCA on X"

Framework Selection

Framework Use When
PECO Population is exposed (not actively choosing) and effect is measured ("why did the population exposed to dependency X experience outcome Y")
None Direct causal investigation; use 5 Whys + Fishbone instead of a formal framework

PICO is rare for Explanatory — Explanatory looks backward at why something happened; PICO looks forward at whether an intervention works.

Output Pattern: Causal Analysis

Use this pattern verbatim for the Findings section (parent skill's Phase 6 Synthesis output):

## Research Type: Explanatory
## Framework: [PECO / None]

## Symptom Definition
[Precise statement of observed effect - what, when, where, severity]

## 5 Whys Chain
Why 1: [Symptom] -> Because [Cause 1] [S1]
Why 2: [Cause 1] -> Because [Cause 2] [S2]
Why 3: [Cause 2] -> Because [Cause 3] [S3]
Why 4: [Cause 3] -> Because [Cause 4]
Why 5: [Cause 4] -> Because [Root Cause]

## Fishbone Categories (Ishikawa)
| Category | Contributing Factors |
|----------|---------------------|
| People | [...] |
| Process | [...] |
| Technology | [...] |
| Environment | [...] |
| Data/Materials | [...] |

## Hypothesis Evidence Map
| Hypothesis | Supporting [Sx] | Opposing [Sx] | GRADE |
|------------|-----------------|---------------|-------|
| H1: [...]  | [S1], [S3]      | [S2]          | ⊕⊕⊕◯  |

## Most Probable Root Cause
[State with GRADE level]

## Counterfactual Test
[What would we expect if this cause were removed? Does evidence support that?]

## Validation Required
[What experiment or observation would confirm the root cause?]

Read the full file on GitHub · 100 lines

Changes

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.

  1. 3d ago First seen · 100 lines · 77 tokens per session scan A 60cc33de32c8

Subscribe to this mod's changes

research-types-explanatory is a skill published in the GitHub repository P47Phoenix/Claude-Plugins (2 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 1,161 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens