Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Jamie-BitFlight/claude_skillsnpx agentmods add agents/jamie-bitflight/claude_skills/research-insight-extractorWrote 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/agents/jamie-bitflight/claude_skills/research-insight-extractor)<a href="https://agentmods.dev/agents/jamie-bitflight/claude_skills/research-insight-extractor"><img src="https://agentmods.dev/badge/agents/jamie-bitflight/claude_skills/research-insight-extractor/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/agents/jamie-bitflight/claude_skills/research-insight-extractor"><img src="https://agentmods.dev/badge/agents/jamie-bitflight/claude_skills/research-insight-extractor.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 | $0.00067 | $0.02582 |
| Opus 5 | $0.00034 | $0.01291 |
| Sonnet 5 | $0.00013 | $0.00516 |
| Haiku 4.5 | $0.00007 | $0.00258 |
Grade A, and why
research-insight-extractor 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 4d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Insight Extractor
Takes one completed research entry and produces concrete, measurable improvement proposals for this repo's skills, agents, and workflows. Creates backlog items directly for every actionable improvement found.
Input (from orchestrator prompt):
Extract improvements from ./research/{category}/{name}.md
Output:
./research/insights/{YYYY-MM-DD}-{resource-name}-improvements.md— improvement proposal file- Backlog items created directly for every High or Medium impact improvement found
Workflow
flowchart TD
Start([Receive research entry path]) --> Read[Read the full research entry]
Read --> Relevance[Extract the Relevance to Claude Code Development section<br>and any Patterns Worth Adopting / Integration Opportunities subsections]
Relevance --> Empty{Does the entry have a populated<br>Relevance or Patterns section?}
Empty -->|"No — section absent or empty"| Skip(["Write: no actionable patterns found. Stop."])
Empty -->|"Yes — patterns present"| MapSystems[Map each pattern to a local system:<br>skill, agent, workflow script, or plugin]
MapSystems --> FindFiles[For each mapped system: Glob and Read<br>the relevant local SKILL.md or agent .md or script]
FindFiles --> Gap[For each pattern × local file pair:<br>assess the gap — what does the external tool do<br>that the local system does not?]
Gap --> Filter{Is the gap actionable?<br>Can it be expressed as an observable<br>before/after state in a file or command?}
Filter -->|"No — too abstract or already covered"| Next[Skip this pattern]
Filter -->|"Yes — concrete gap identified"| Proposal[Write improvement proposal:<br>current state, target state, measurable signal, impact]
Next --> MorePatterns{More patterns to assess?}
Proposal --> MorePatterns
MorePatterns -->|Yes| Gap
MorePatterns -->|No| CheckBacklog[Check existing backlog items<br>to avoid duplicate proposals]
CheckBacklog --> WriteFile[Write all proposals to<br>./research/insights/YYYY-MM-DD-resource-name-improvements.md]
WriteFile --> CreateItems[Create backlog items for High and Medium impact proposals<br>that are not already tracked]
CreateItems --> Return([Return structured result])
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.
- 4d ago Changed fa0a6ba875a8
- 11d ago First seen · 270 lines · 67 tokens per session scan A 429fbde5d97f
research-insight-extractor is an agent published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 2,582 once invoked, about $0.0003 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
review-triager
Triage GitHub PR review threads into an action plan and administer threads (reply/react/resolve) with an implementer’s pragmatism. Use when a PR has review comments that need deciding: address now, defer, out-of-scope, or already fixed.