research-deep

research-deep is a skill for Claude Code, Codex from melodic-software/claude-code-plugins. It costs 131 tokens per session (3,542 once invoked), scanned A, original, MIT.

A dispatcher for carrying out thorough external research in an isolated workspace. It coordinates the research process and produces a cited research file rather than filling the main conversation with all the collected material.

In plain words
What is it for?
Use it for deep investigations that need multiple sources, targeted follow-up, checks against contrary evidence, and a documented research handoff.
Why use it?
Large research tasks can overwhelm the main working context and make findings harder to verify. This keeps the detailed investigation separate while preserving its results and sources.

Skill for Claude CodeCodex

Installs and runs on its own, but its text points at files inside its plugin — anything it tells you to read at a ${CLAUDE_PLUGIN_ROOT} path is only there once the plugin is installed. Installing the plugin gets both.

Part of the discovery plugin — 5 skills, 3 agents shipped together

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/melodic-software/claude-code-plugins/research-deep
Any agent
npx skills add melodic-software/claude-code-plugins --skill research-deep
Clone the repo
git clone --depth 1 https://github.com/melodic-software/claude-code-plugins

Made for: Claude Code, Codex.

Or install discovery, the plugin that ships this one along with the rest of its 5 skills, 3 agents.

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-deep

README.md
[![agentmods](https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/research-deep.svg)](https://agentmods.dev/skills/melodic-software/claude-code-plugins/research-deep)
Your own site
<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/research-deep"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/research-deep.svg" alt="Measured on agentmods" height="20"></a>
Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,542 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 5.1 $0.00131 $0.03542
Opus 5 $0.00066 $0.01771
Sonnet 5 $0.00026 $0.00708
Haiku 4.5 $0.00013 $0.00354

Measured yesterday against content hash 98abbc10ea75, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

research-deep 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 yesterday.

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.

plugins/discovery/skills/research-deep/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pre-computed context

Current branch: !git branch --show-current 2>/dev/null || echo "unknown"

Purpose

/discovery:research-deep is the dispatcher for deep external research, a depth/execution variant of the sibling /discovery:research skill. Same research contract (3-phase discipline, source-tier ratio, recency gate, mandatory falsification, cited RESEARCH.md artifact); heavier execution that keeps the main session's context clean. It selects ONE execution tier from tool availability + task heaviness, then surfaces the same summary contract regardless of tier.

This skill runs inline (main context). It dispatches; the chosen tier provides the context isolation. It must run in main context because that is the only place both of its requirements hold, the Workflow tool, absent from every non-fork subagent, and a dependable Agent spawn, which no subagent is guaranteed to hold: see the Dispatching this skill itself gotcha.

Topic

$ARGUMENTS

If no topic was provided, infer it from the current conversation. Identify the technical claim, decision, or implementation being worked on and research that.

Caveat, a ${CLAUDE_…}-shaped token in a topic may not arrive as you typed it, and this skill carries the highest exposure of the three because a corrupted topic here is copied into every envelope of an N-way fan-out. What was observed, what is documented, what is not, and the per-topic echo-back check: ${CLAUDE_PLUGIN_ROOT}/reference/parent-contract.md ("A different question").

Dispatch decision (multi-topic check, then three tiers)

Multi-topic check. Run FIRST, before any tier. Count the independent sub-topics in the ask (numbered list, enumerated questions, separable subjects that share no claims). N ≥ 2 separable topics → do NOT dispatch an engine on the combined blob. An engine decomposes ONE question into generic research angles; fed a multi-topic blob, every broad agent researches all N topics shallowly. N× the wall-clock and tokens for worse depth. Instead: spawn N parallel discovery:researcher agents (Agent tool, one per topic), each dispatched with the full envelope below. Cap N at roughly a dozen. Past that, narrow the ask with the user before dispatching. Give each agent its own sub-slice. <memory_dir>/<slug>/<topic-slug>/, assigned by this session in the dispatch envelope, never chosen by the worker (two workers choosing independently can choose the same one); the memory root travels as its own envelope field, since a worker handed a nested sub-slice path cannot tell from that path alone which ancestor is the configured root. Each writes the normal RESEARCH.md index, its sidecars, and its own research-checklist.md inside that sub-slice; those filenames are fixed, so N agents pointed at one slice root would overwrite one another's index and ledger rather than producing separable artifacts. This session owns each topic's post-dispatch boundary. Synthesis is the last step, not the only one. Close "The post-dispatch boundary" below for each topic, then synthesize the slice-root RESEARCH.md from the per-topic indexes. Skipping it produces the worst available artifact: a root RESEARCH.md presenting claims as gate-passed when the rows that matter were never graded by anyone. An engine is for a SINGLE contested or deep question that needs falsification rounds and adversarial claim-checking.

Read the full file on GitHub · 124 lines

Files

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.

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. yesterday First seen · 124 lines · 131 tokens per session scan A 98abbc10ea75

Subscribe to this mod's changes

research-deep is a skill published in the GitHub repository melodic-software/claude-code-plugins (15 stars, last pushed today), licensed MIT. It adds 131 tokens to every session and 3,542 once invoked, about $0.0007 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 2026-09-03.

Related

Other skills, from other repositories

parallel-orchestrator

Manage parallel Claude Code workstreams using git worktrees. Use when: splitting large tasks across multiple workers, coordinating parallel development, monitoring worker progress, integrating completed work, analyzing work item documents (code reviews, issue lists). Triggers: parallel, orchestrator, worktrees…

jimmc414/claude-code-plugin-marketplace · 78 tokens

parallel-worker

Execute focused implementation tasks in a parallel workflow. Use when: working on assigned files in a worktree, making checkpoint commits, signaling dependencies or blockers, completing orchestrator-assigned tasks. Triggers: worker, checkpoint, worktree, assigned scope, commit prefix, parallel task.

jimmc414/claude-code-plugin-marketplace · 59 tokens

generate-tree-structure

For tree/maze generation: spanning trees, random mazes, graph coverage. Uses frontier-based exploration with configurable traversal order.

jimmc414/claude-code-plugin-marketplace · 30 tokens

optimize-local-search

For NP-hard optimization: TSP, scheduling, assignment problems. Uses greedy construction + local improvement (2-opt, hill climbing).

jimmc414/claude-code-plugin-marketplace · 31 tokens

solve-constraint-puzzle

For constraint satisfaction: Sudoku, scheduling, N-queens, logic puzzles, SAT-like problems, assignment problems. Uses propagate-then-search pattern.

jimmc414/claude-code-plugin-marketplace · 36 tokens

stack-based-backtrack

For search with undo: explicit decision stack, backtracking when paths fail, depth-first exploration with state restoration.

jimmc414/claude-code-plugin-marketplace · 27 tokens