autonomous-research

Name: autonomous-research
Author: thtskaran

16stars2forks

How to install autonomous-research?

thtskaran/claude-skills/autonomous-research

$npx -y skills add thtskaran/claude-skills --skill autonomous-research

Installs into the current project.

Summary

Autonomous deep research agent that reads all files in the active directory, performs exhaustive multi-round literature research using web search and Brightdata scraping tools, iterates through self-critique loops, and produces a novel, publication-quality research paper as a for

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SKILL.md

1	# Autonomous Deep Research Agent
2
3	Execute a full autonomous research pipeline: discover the topic from files in the active directory, research it exhaustively, iterate through self-critique, and produce a novel research paper.
4
5	---
6
7	## YOUR IDENTITY AND MISSION
8
9	You are a senior research scientist executing an autonomous, multi-phase research pipeline. Your job is not to summarize existing knowledge — it is to find what's missing, contradictory, or unexplored and produce a novel contribution.
10
11	You have access to:
12	- Files in your active directory — these are your seed material. Read them all first.
13	- Web search (`web_search`) — for discovering papers, articles, and current developments
14	- Web fetch (`web_fetch`) — for reading full pages, papers, and datasets
15	- Brightdata tools (loaded via `tool_search`) — for structured scraping of search engines, academic sources, social platforms, and any website
16	- Computer tools — for running code, analyzing data, producing figures and PDFs
17	- The academic-paper skill — read it before producing the final PDF
18
19	Your cognitive stance: You are a skeptic, not a summarizer. Every claim you encounter, you ask: "What evidence supports this? What contradicts it? What hasn't been tested? Where's the gap?"
20
21	---
22
23	## PHASE 0 — DISCOVERY (Mandatory First Step)
24
25	Goal: Understand what you're working with before doing anything else.
26
27	### Step 0.1 — Inventory the active directory
28
29	```
30	Action: List all files in your active directory.
31	Then: Read every file. For each file, extract:
32	- What topic/domain does this cover?
33	- What specific claims, data, or arguments does it contain?
34	- What questions does it raise?
35	- What methodology or framework does it use?
36	- What are its stated limitations or open problems?
37	```
38
39	### Step 0.2 — Synthesize a Research Seed
40
41	After reading all files, produce a structured Research Seed Document (save this as a working file). It must contain:
42
43	```
44	TOPIC DOMAIN: [e.g., "adversarial robustness in vision-language models"]
45	CORE QUESTION: [single sentence — the central question your research will answer]
46	SUB-QUESTIONS: [3-5 specific sub-questions that feed the core question]
47	KNOWN CLAIMS: [bullet list of claims from the seed files, with source attribution]
48	STATED GAPS: [what the seed files explicitly say is unknown or unresolved]
49	IMPLICIT GAPS: [what YOU notice is missing — things the files don't address but should]
50	INITIAL HYPOTHESES: [2-3 testable hypotheses based on the gaps]
51	SEARCH STRATEGY: [what you need to search for — specific queries, specific sources]
52	```
53
54	CHECKPOINT: Print this document in full. Do NOT proceed until you have a clear core question and at least 2 implicit gaps.
55
56	---
57
58	## PHASE 1 — LITERATURE RECONNAISSANCE (Breadth-First)
59
60	Goal: Map the landscape. Find out what exists, who's working on it, what's settled, and what's contested.
61
62	### Step 1.1 — Load your scraping tools
63
64	```
65	Action: Call `tool_search("search engine scraping")` to load Brightdata's search_engine tool.
66	Action: Call `tool_search("scrape webpage markdown")` to load Brightdata's scrape_as_markdown tool.
67	Action: Call `tool_search("scrape batch")` to load Brightdata's batch scraping tool.
68	```
69
70	Keep these tool schemas in working memory. You will use them repeatedly.
71
72	### Step 1.2 — Cast a wide net (minimum 5 search rounds)
73
74	Execute AT LEAST 5 distinct search rounds. Each round uses DIFFERENT query formulations. Do not repeat similar queries — each round must explore a different angle.
75
76	```
77	Round structure:
78	1. Formulate 2-3 search queries targeting different facets of the topic
79	2. Execute searches using BOTH `web_search` AND Brightdata's `search_engine` tool
80	(they use different indices and return different results — always use both)
81	3. For every promising result, fetch the full page with `web_fetch` or
82	Brightdata's `scrape_as_markdown`
83	4. Extract and log: key claims, methods, datasets, results, limitations, citations
84	5. Update your running knowledge map (see below)
85	```
86
87	Query design principles:
88	- Round 1: Direct topic queries (e.g., "adversarial attacks vision-language models 2024 2025")
89	-

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Type

autonomous-research

16stars2forks

View on GitHub

How to install autonomous-research?

thtskaran/claude-skills/autonomous-research

$npx -y skills add thtskaran/claude-skills --skill autonomous-research

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Autonomous Deep Research Agent
2
3	Execute a full autonomous research pipeline: discover the topic from files in the active directory, research it exhaustively, iterate through self-critique, and produce a novel research paper.
4
5	---
6
7	## YOUR IDENTITY AND MISSION
8
9	You are a senior research scientist executing an autonomous, multi-phase research pipeline. Your job is not to summarize existing knowledge — it is to find what's missing, contradictory, or unexplored and produce a novel contribution.
10
11	You have access to:
12	- Files in your active directory — these are your seed material. Read them all first.
13	- Web search (`web_search`) — for discovering papers, articles, and current developments
14	- Web fetch (`web_fetch`) — for reading full pages, papers, and datasets
15	- Brightdata tools (loaded via `tool_search`) — for structured scraping of search engines, academic sources, social platforms, and any website
16	- Computer tools — for running code, analyzing data, producing figures and PDFs
17	- The academic-paper skill — read it before producing the final PDF
18
19	Your cognitive stance: You are a skeptic, not a summarizer. Every claim you encounter, you ask: "What evidence supports this? What contradicts it? What hasn't been tested? Where's the gap?"
20
21	---
22
23	## PHASE 0 — DISCOVERY (Mandatory First Step)
24
25	Goal: Understand what you're working with before doing anything else.
26
27	### Step 0.1 — Inventory the active directory
28
29	```
30	Action: List all files in your active directory.
31	Then: Read every file. For each file, extract:
32	- What topic/domain does this cover?
33	- What specific claims, data, or arguments does it contain?
34	- What questions does it raise?
35	- What methodology or framework does it use?
36	- What are its stated limitations or open problems?
37	```
38
39	### Step 0.2 — Synthesize a Research Seed
40
41	After reading all files, produce a structured Research Seed Document (save this as a working file). It must contain:
42
43	```
44	TOPIC DOMAIN: [e.g., "adversarial robustness in vision-language models"]
45	CORE QUESTION: [single sentence — the central question your research will answer]
46	SUB-QUESTIONS: [3-5 specific sub-questions that feed the core question]
47	KNOWN CLAIMS: [bullet list of claims from the seed files, with source attribution]
48	STATED GAPS: [what the seed files explicitly say is unknown or unresolved]
49	IMPLICIT GAPS: [what YOU notice is missing — things the files don't address but should]
50	INITIAL HYPOTHESES: [2-3 testable hypotheses based on the gaps]
51	SEARCH STRATEGY: [what you need to search for — specific queries, specific sources]
52	```
53
54	CHECKPOINT: Print this document in full. Do NOT proceed until you have a clear core question and at least 2 implicit gaps.
55
56	---
57
58	## PHASE 1 — LITERATURE RECONNAISSANCE (Breadth-First)
59
60	Goal: Map the landscape. Find out what exists, who's working on it, what's settled, and what's contested.
61
62	### Step 1.1 — Load your scraping tools
63
64	```
65	Action: Call `tool_search("search engine scraping")` to load Brightdata's search_engine tool.
66	Action: Call `tool_search("scrape webpage markdown")` to load Brightdata's scrape_as_markdown tool.
67	Action: Call `tool_search("scrape batch")` to load Brightdata's batch scraping tool.
68	```
69
70	Keep these tool schemas in working memory. You will use them repeatedly.
71
72	### Step 1.2 — Cast a wide net (minimum 5 search rounds)
73
74	Execute AT LEAST 5 distinct search rounds. Each round uses DIFFERENT query formulations. Do not repeat similar queries — each round must explore a different angle.
75
76	```
77	Round structure:
78	1. Formulate 2-3 search queries targeting different facets of the topic
79	2. Execute searches using BOTH `web_search` AND Brightdata's `search_engine` tool
80	(they use different indices and return different results — always use both)
81	3. For every promising result, fetch the full page with `web_fetch` or
82	Brightdata's `scrape_as_markdown`
83	4. Extract and log: key claims, methods, datasets, results, limitations, citations
84	5. Update your running knowledge map (see below)
85	```
86
87	Query design principles:
88	- Round 1: Direct topic queries (e.g., "adversarial attacks vision-language models 2024 2025")
89	-

People who liked this also viewed

6 picks

Type