Borrowing it
Nothing to install: this file belongs to Diptanil-x-42/knowledge-base-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Diptanil-x-42/knowledge-base-mcp/main/.claude/skills/research-capture/SKILL.mdgit clone --depth 1 https://github.com/Diptanil-x-42/knowledge-base-mcpWrote 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.
[](https://agentmods.dev/skills/diptanil-x-42/knowledge-base-mcp/research-capture)<a href="https://agentmods.dev/skills/diptanil-x-42/knowledge-base-mcp/research-capture"><img src="https://agentmods.dev/badge/skills/diptanil-x-42/knowledge-base-mcp/research-capture/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.
<a href="https://agentmods.dev/skills/diptanil-x-42/knowledge-base-mcp/research-capture"><img src="https://agentmods.dev/badge/skills/diptanil-x-42/knowledge-base-mcp/research-capture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00574 |
| Opus 5 | $0.00024 | $0.00287 |
| Sonnet 5 | $0.00010 | $0.00115 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
research-capture 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Capture Workflow
When the user asks you to research, capture, or organize information, follow this workflow using the knowledge-base MCP tools.
Capturing New Research
- Break the information into individual, focused notes
- Use
add_notefor each distinct finding with:- A clear, descriptive title (e.g., "MCP uses JSON-RPC 2.0 for message encoding")
- Detailed content explaining the finding
- Relevant tags for categorization
- After saving, confirm what was captured with note IDs
Reviewing Existing Research
- Start by running
list_tagsto see what topics exist - Use
get_notes_by_tagto pull notes on the requested topic - Use
search_notesfor keyword-based lookups when the exact tag is unknown - Present findings in a structured format
Research Summary Format
When summarizing research on a topic, structure your response as:
Key Findings
- Bullet points of main discoveries
Connections
- How different notes relate to each other
Knowledge Gaps
- What is missing or needs further research
Suggested Next Steps
- What to research or capture next
Tag Conventions
- Use lowercase with hyphens:
machine-learningnotMachine Learning - Use broad category tags:
python,web-dev,ai,devops - Add specificity tags:
fastapi,react-hooks,prompt-engineering - Always include at least one broad and one specific tag per note
Cleanup
- If the user says information is outdated, use
delete_notewith the note ID - Before deleting, confirm the note title and ID with the user
Knowledge Base Health Check
When the user asks to check the health of their knowledge base, or when starting a new research session:
- Run
get_statisticsto get current metrics - Analyze the results and report:
- Coverage: Which tags have the most/fewest notes
- Gaps: Tags with only 1-2 notes that may need more research
- Activity: How recent the latest notes are (stale if older than 2 weeks)
- Depth: Whether average content length suggests detailed or shallow notes
- Suggest specific actions:
- Topics that need more notes based on underrepresented tags
- Tags that could be consolidated (too similar)
- Areas where the user might want to do fresh research
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.
- 9d ago First seen · 68 lines · 48 tokens per session scan A 9ed006ace61d
research-capture is a skill published in the GitHub repository Diptanil-x-42/knowledge-base-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 574 once invoked, about $0.0002 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.
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