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.
git clone --depth 1 https://github.com/synaptiai/synapti-marketplaceWrote 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/commands/synaptiai/synapti-marketplace/quick-research)<a href="https://agentmods.dev/commands/synaptiai/synapti-marketplace/quick-research"><img src="https://agentmods.dev/badge/commands/synaptiai/synapti-marketplace/quick-research/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/commands/synaptiai/synapti-marketplace/quick-research"><img src="https://agentmods.dev/badge/commands/synaptiai/synapti-marketplace/quick-research.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 | $0.00013 | $0.00439 |
| Opus 5 | $0.00006 | $0.00219 |
| Sonnet 5 | $0.00003 | $0.00088 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
quick-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 8d 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.
What it actually says
Quick Research: $ARGUMENTS
Research with source verification (2-3 searches max).
Process
-
Search for relevant sources
-
After each search, note:
- Key facts (High/Medium/Low confidence)
- Any contradictions
-
Deliver:
## Answer
[Direct response]
## Supporting Evidence
- [Fact] ([Source], [confidence])
- [Fact] ([Source], [confidence])
## Confidence: [High/Medium/Low]
[justification]
## Caveats
[limitations]
User Interaction
Use the AskUserQuestion tool when:
- Question is too broad for quick research
- Clarification needed between quick vs deep research
- Low confidence result warrants escalation decision
Example Invocations
Broad question:
User: /quick-research climate change
→ Use AskUserQuestion tool:
Question: "This topic is broad for quick research. What specific question?"
Options:
- "Latest IPCC report findings"
- "Current global temperature trends"
- "Switch to /deep-research for comprehensive coverage"
Low confidence result:
After research: Confidence LOW (conflicting sources)
→ Use AskUserQuestion tool:
Question: "Quick research found conflicting information. How to proceed?"
Options:
- "Accept low-confidence answer with caveats"
- "Escalate to /deep-research for thorough verification"
- "Try different search queries"
Escalation decision:
User: /quick-research [complex multi-part question]
→ Use AskUserQuestion tool:
Question: "This question may need deeper research. Which approach?"
Options:
- "Quick research (2-3 searches, faster)"
- "Deep research (5-10 searches, more thorough)" (Recommended)
- "Start quick, escalate if needed"
Begin now.
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.
- 8d ago First seen · 79 lines · 13 tokens per session scan A bf33dd24758f
quick-research is a command published in the GitHub repository synaptiai/synapti-marketplace (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 439 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
create-worktree
Follow these steps to create a git worktree.
paper-trail-review
Review and arbitrate problematic references (cascade-exhausted, unresolved UID, awaiting OCR). Generates an up-to-date report, walks through cases by category, and applies arbitration decisions to the registry.
paper-trail-decide
Inspect a single reference in detail (identity, PDF status, acquisition history with verdicts, citation context in vault, state transitions) and decide its fate. Offers actionable decisions based on the ref's current state.
paper-trail-new-paper
Start writing an academic paper (IMRaD structure) on a topic, with anti-hallucination citation verification at every step. Builds on existing audited SOTAs.
paper-trail-registry-cleanup
Nettoyage historique du registre des fiches bibliographiques (les 900 fiches existantes). Cible les vieilles fiches mal nommées (0000, untitled) ET les duplicates avec suffixes numériques (foo2020bar2, 234) qui sont des artefacts de runs INGEST passés. Délègue les décisions au sub-agent textbook-resolver. Les merge…
paper-trail-cascade
Acquire PDFs via the 8-source cascade (11 with opt-in extended sources) for a single ref by slug, or a batch filtered by state. Validates page 1 anti-homonymy on each download.