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.
npx agentmods add commands/fergupa/claude_plugins/researchgit clone --depth 1 https://github.com/fergupa/claude_pluginsWhat 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 | $0.00012 | $0.00930 |
| Opus 5 | $0.00006 | $0.00465 |
| Sonnet 5 | $0.00002 | $0.00186 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
research 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 2d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Multi-source research on a customer question, product topic, or account-related inquiry. Synthesizes findings from all available sources with clear attribution.
Usage
/research <question or topic>
Workflow
1. Parse the Research Request
Identify what type of research is needed:
- Customer question: Something a customer has asked that needs an answer (e.g., "Does our product support SSO with Okta?")
- Issue investigation: Background on a reported problem (e.g., "Has this bug been reported before? What's the known workaround?")
- Account context: History with a specific customer (e.g., "What did we tell Acme Corp last time they asked about this?")
- Topic research: General topic relevant to support work (e.g., "Best practices for webhook retry logic")
2. Search Available Sources
Search in priority order, adapting to what is connected:
Tier 1 — Internal Documentation (highest confidence):
- ~~knowledge base (if connected): product docs, runbooks, FAQs
- ~~cloud storage: internal documents, specs, guides, past research
- ~~CRM notes: previous answers to similar questions, account context
Tier 2 — Team Communications:
- ~~chat: search for the topic in relevant channels; check if teammates have discussed or answered this before
- ~~email: search for previous correspondence on this topic
- ~~support platform (if connected): check if this has been asked/resolved before
Tier 3 — External Sources:
- Web search: official documentation, blog posts, community forums
- Public knowledge bases, help centers, release notes
3. Synthesize Findings
Compile results into a structured research brief:
## Research: [Question/Topic]
### Answer
[Clear, direct answer to the question — lead with the bottom line]
**Confidence:** [High / Medium / Low]
[Explain what drives the confidence level]
### Key Findings
**From [Source 1]:**
- [Finding with specific detail]
- [Finding with specific detail]
**From [Source 2]:**
- [Finding with specific detail]
### Context & Nuance
[Any caveats, edge cases, or additional context that matters]
### Sources
1. [Source name/link] — [what it contributed]
2. [Source name/link] — [what it contributed]
3. [Source name/link] — [what it contributed]
### Gaps & Unknowns
- [What couldn't be confirmed]
- [What might need verification from a subject matter expert]
### Recommended Next Steps
- [Action if the answer needs to go to a customer]
- [Action if further research is needed]
- [Who to consult for verification if needed]
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.
- 2d ago First seen · 117 lines · 12 tokens per session scan A 19e0cdcc2174
research is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 930 once invoked, about $0.0001 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.