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research

research is a skill for Claude Code from juicesharp/rpiv-mono. It costs 85 tokens per session (4,999 once invoked), scanned A, original, MIT.

A structured research workflow for answering questions about a codebase and saving the findings as a research document. A codebase is the collection of source files that make up a software project.

In plain words
What is it for?
Use it to research a codebase from a question or discovery document, coordinate targeted analysis, and write the results to the project's research artifacts.
Why use it?
It helps investigate a project systematically and preserve the evidence so the findings can guide later design or implementation work.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions subagents.

not rated 780repo +30 changed 8d ago A scan SkillSpector: pass 85 tokens original MIT

Good fit Use it to research a codebase from a question or discovery document, coordinate targeted analysis, and write the results to the project's research artifacts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/juicesharp/rpiv-mono/research
View source ↗ juicesharp/rpiv-mono
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.

Any agent
npx skills add juicesharp/rpiv-mono --skill research
Clone the repo
git clone --depth 1 https://github.com/juicesharp/rpiv-mono

Made for: Claude Code.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/research/github.svg)](https://agentmods.dev/skills/juicesharp/rpiv-mono/research)
Your own site
<a href="https://agentmods.dev/skills/juicesharp/rpiv-mono/research"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/research/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.

agentmods 80×15 button for research

Your own site · 80×15
<a href="https://agentmods.dev/skills/juicesharp/rpiv-mono/research"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,999 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
SkillSpector: 1 finding, up to low

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 →

  • low Excessive Agency · line 190
    Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.
    Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
How audits are shown
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 5.1 $0.00085 $0.04999
Opus 5 $0.00043 $0.02499
Sonnet 5 $0.00017 $0.01000
Haiku 4.5 $0.00009 $0.00500

Measured 8d ago against content hash 8b2e1878b54e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

research 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 8d 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.

packages/rpiv-pi/skills/research/SKILL.md · 389 lines

How it starts

The opening of the file, as written. The whole thing — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research

You are tasked with answering structured research questions by spawning targeted analysis agents and synthesizing their findings into a comprehensive research document. This skill internally dispatches the scope-tracer agent to formulate trace-quality research questions, then answers them.

Input

$ARGUMENTS — free-text research prompt, or a .rpiv/artifacts/discover/*.md path to chain from discover.

Metadata

node "${SKILL_DIR}/../_shared/now.mjs"
echo
node "${SKILL_DIR}/../_shared/git-context.mjs"

Copy values verbatim — do not reformat the timezone offset.

Flow

  1. Input → 2. Dispatch agents → 3. Synthesize & checkpoint → 4. Write doc → 5. Present & chain → 6. Follow-ups

The final artifact feeds design or blueprint.

Steps

Step 1: Input Handling

  1. Argument is empty:

    Please provide a free-text research prompt.

    Then wait for input.

  2. Detect chained discover artifact: If the input includes a path matching .rpiv/artifacts/discover/.*\.md, read it FULLY using the Read tool (no limit/offset) before scope-tracer dispatch:

    • Translate each ### [Decision title] block in the FRD's ## Decisions section into a Developer Context entry: **Q (discover: <Decision title>): <Question text>** followed by A: <Chosen text>. Hold these entries in main context — they're recorded in the research artifact's Developer Context section in Step 4 (write document).
    • Use the FRD's ## Recommended Approach text (1-2 sentences naming the architectural shape) as the topic body for the next sub-step's scope-tracer prompt. The full discover artifact path stays in the input so scope-tracer's "read mentioned files first" rule picks up the file naturally for additional context.
    • Carry the FRD's Open Questions forward verbatim into the research artifact's Open Questions section in Step 4.
    • If the input is plain free-text or includes a non-discover path, skip this sub-step and proceed directly to scope-tracer dispatch with the input as the topic.

Read the full file on GitHub · 389 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. 8d ago Changed 8b2e1878b54e
  2. 12d ago First seen · 389 lines · 85 tokens per session scan A 66925eeec580

Subscribe to this mod's changes

research is a skill published in the GitHub repository juicesharp/rpiv-mono (780 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 4,999 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.

Research
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