deep-research

High-stakes research with Analysis of Competing Hypotheses, disconfirmation testing, evidence matrices, and sensitivity checks (~400k token budget) — use when…

Skill by 5uck1ess

IndexedMIT★ 5

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/5uck1ess-deep-research

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About

High-stakes research with Analysis of Competing Hypotheses, disconfirmation testing, evidence matrices, and sensitivity checks (~400k token budget) — use when the user needs to be SURE, not just well-informed. Trigger phrases include "deep research", "deeply investigate", "validate that X", "prove/disprove", "we're making a big decision", "correctness is critical", "I need to trust this answer", or "actively try to disprove this". Worth the extra cost when — the wrong answer has real consequences (architecture commitment, vendor choice, security claim, compliance question, public-facing claim), the user has already seen shallow answers and doesn't trust them, they explicitly ask for rigor or disconfirmation, or they're defending a position to stakeholders. Do NOT use for exploratory "what's out there" questions (use research), routine library comparisons where a single good answer suffices (use research), or quick factual lookups. This is the bias-catching, evidence-calibrating tier — overkill for casual exploration but essential when you can't afford to be wrong.

Use cases: testing, security, research, finance

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

Tools: No tools found
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Install scripts: None found

Network hosts it talks to 1

codeload.github.com · found in install command

Files it touches

~/.claude/skills/deep-research

What's inside

Skill name: deep-research
Description: High-stakes research with Analysis of Competing Hypotheses, disconfirmation testing, evidence matrices, and sensitivity checks (~400k token budget) — use when the user needs to be SURE, not just well-informed. Trigger phrases include "deep research", "deeply investigate", "validate that X", "prove/disprove", "we're making a big decision", "correctness is critical", "I need to trust this answer", or "actively try to disprove this". Worth the extra cost when — the wrong answer has real consequences (architecture commitment, vendor choice, security claim, compliance question, public-facing claim), the user has already seen shallow answers and doesn't trust them, they explicitly ask for rigor or disconfirmation, or they're defending a position to stakeholders. Do NOT use for exploratory "what's out there" questions (use research), routine library comparisons where a single good answer suffices (use research), or quick factual lookups. This is the bias-catching, evidence-calibrating tier — overkill for casual exploration but essential when you can't afford to be wrong.
License: MIT
Instructions: 27 lines in SKILL.md · view on GitHub

Agent Skills spec issues

description: longer than 1024 characters

Files (1)

SKILL.md

SKILL.md (first lines)

# Deep Research

ACH-enhanced deterministic research: clarify → perspectives → decompose → parallel search → extract claims → hypotheses → disconfirm → evidence matrix (with sensitivity check) → self-critique → synthesize.

Costs more tokens (~400k budget) but produces higher-confidence results by actively trying to disprove answers.

## Invoke

Start the workflow via the devkit engine:

Use the `devkit_start` tool with workflow: "deep-research" and input: "{input}".

Then follow each step the engine returns. Call `devkit_advance` after completing each step. The engine controls step order, gates, and loops. Do NOT skip steps.

## Rules

- Perspectives first — ground queries in real viewpoints, not LLM brainstorming
- Disconfirm > confirm — try to KILL hypotheses, not prove them
- Summarize immediately — never carry raw fetched content forward
- Evidence matrix is mandatory — no skipping the structured comparison
- Sensitivity check is mandatory — know how fragile your conclusion is
- Self-critique before output — catch your own biases
- Cite everything — every claim links to its source
- Confidence calibration — HIGH/MEDIUM/LOW based on evidence robustness, not gut feel
- Be honest about uncertainty — "I don't know" with good reasoning beats a confident wrong answer

From 5uck1ess/devkit, MIT.

Signals

Stars: 5
Forks: 1
Open issues: 0
Last commit (this folder): Apr 11, 2026 (6 months ago)
Last release: Oct 21, 2026 (6 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/5uck1ess/devkit
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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deep-research skill. Agent Mart. https://agentmart.dev/skills/5uck1ess-deep-research (last updated 2026-10-24)