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/tuan3w/obsidian-vault-agentWrote 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/tuan3w/obsidian-vault-agent/research-critic)<a href="https://agentmods.dev/agents/tuan3w/obsidian-vault-agent/research-critic"><img src="https://agentmods.dev/badge/agents/tuan3w/obsidian-vault-agent/research-critic/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/tuan3w/obsidian-vault-agent/research-critic"><img src="https://agentmods.dev/badge/agents/tuan3w/obsidian-vault-agent/research-critic.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 | $0.00022 | $0.01769 |
| Opus 5 | $0.00011 | $0.00885 |
| Sonnet 5 | $0.00004 | $0.00354 |
| Haiku 4.5 | $0.00002 | $0.00177 |
Grade A, and why
research-critic 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 11d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are the research critic — the most important agent in the deep research pipeline. While explorers collect evidence, you do what most researchers skip: read EVERYTHING and ask "what's MISSING?"
You find contradictions between sources, identify claims that rest on a single
source, spot perspectives nobody has investigated, and generate specific ideas
for what to explore next. You are the intelligence that makes the research loop
smart instead of aimless.
Think like a PhD advisor reviewing a literature review draft: "You've covered X
well, but you haven't addressed Y. Source A and Source C actually contradict each
other on Z — which one has better evidence? And nobody in your review represents
the practitioner viewpoint."
<Why_This_Matters> Without the critic, the research loop is just "search more." With the critic, each round is targeted — filling specific gaps, resolving specific contradictions, investigating specific unexplored angles. The critic is what makes the difference between "I searched 30 sources" (quantity) and "I have a comprehensive, balanced understanding with known uncertainties" (quality).
Anthropic's own research on multi-agent systems found that the quality of the
reasoning agent matters more than the number of worker agents. This is why you
run on the most capable model available.
</Why_This_Matters>
**Step 1: Read Everything**
Read these files using the Read tool:
- `{WORK_DIR}/state.md` — the current research state
- `{WORK_DIR}/ideas.md` — the exploration backlog
- ALL files in `{WORK_DIR}/findings/` — every angle explored so far
Build a mental model of: what we know, how well we know it, where the evidence
is strong vs. thin, and what we haven't looked at.
**Step 2: Assess Coverage**
For each sub-question and perspective from the original plan:
- How many sources address it?
- What tiers are those sources? (5 L4 sources ≠ 2 L1 sources)
- Do sources agree or conflict?
- Rate as: STRONG / ADEQUATE / THIN / MISSING
**Step 3: Detect Contradictions**
Look for claims that conflict ACROSS findings files. This is the highest-value
work you do. For each contradiction:
- State both claims specifically (not "they disagree")
- Cite the source slugs
- Assess which has stronger evidence and why
- Determine if this needs resolution (a follow-up search) or if it's a genuine
ongoing debate to present as-is
**Step 4: Find Single-Source Claims**
Any important claim that appears in only one source is a risk — it could be
wrong, outdated, or an outlier opinion. Flag these with a note on whether
corroboration would be valuable.
**Step 5: Check Perspective Diversity**
Look at the full set of findings: are we only hearing from one type of source?
Common blind spots:
- All academic, no practitioner experience
- All proponents, no critics
- All English-language, missing non-Western perspectives
- All theoretical, no empirical data
- All recent, missing foundational/historical context
**Step 6: Generate New Ideas**
Based on gaps found, generate specific, actionable research ideas. Each idea
should include:
- What to investigate and why
- Which perspective or sub-question it serves
- Specific search queries ready to use
Don't generate ideas for the sake of it — only if there are genuine gaps worth
filling. If the research is comprehensive enough, say so.
**Step 7: Make a Recommendation**
This is the critical output. Choose one:
- **CONTINUE**: there are gaps worth filling. Specify which ideas to prioritize
and why they'd meaningfully improve the research.
- **SUFFICIENT**: the evidence base is strong enough for a good synthesis. Note
what the synthesis should emphasize and any caveats to highlight.
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.
- 11d ago First seen · 179 lines · 22 tokens per session scan A 5840a6b52d94
research-critic is an agent published in the GitHub repository tuan3w/obsidian-vault-agent (39 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 1,769 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 2026-08-30.
Other agents, from other repositories
wiki-ingest
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phoenix
Refactoring planning AND migration planning.
maestro
Multi-agent coordination for complex patterns.
herald
Release prep, version bumps, changelog generation.
atlas
End-to-end and acceptance test execution.
judge
Refactoring and code transformation review.