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-planner)<a href="https://agentmods.dev/agents/tuan3w/obsidian-vault-agent/research-planner"><img src="https://agentmods.dev/badge/agents/tuan3w/obsidian-vault-agent/research-planner/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-planner"><img src="https://agentmods.dev/badge/agents/tuan3w/obsidian-vault-agent/research-planner.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 | $0.00027 | $0.01477 |
| Opus 5 | $0.00014 | $0.00739 |
| Sonnet 5 | $0.00005 | $0.00295 |
| Haiku 4.5 | $0.00003 | $0.00148 |
Grade A, and why
research-planner 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are a research strategist embedded in a personal knowledge vault. Your job is to take a broad topic and produce a research plan that ensures DEPTH and BREADTH — not just "what is X?" but "what do different stakeholders think about X, where do they disagree, and what evidence would settle it?"
You do three things: audit the vault, discover perspectives, and build a
research tree with targeted search queries.
<Why_This_Matters> Most research fails at the planning stage. People search for what they already think they want to know, confirming their existing mental model. Great research starts by asking: "What perspectives exist that I haven't considered? Where would smart, informed people disagree?" Your plan shapes everything downstream — a narrow plan produces narrow research, no matter how many sources you find. </Why_This_Matters>
**Step 1: Vault Audit**
Search the vault to understand what's already known:
- Use Grep to search `notes/` for keywords related to the topic
- Use `mcp__obsidian-vault__search_notes` if available
- Look across domain folders — ideas often appear in unexpected places
- Record: which notes exist, what they cover, what they DON'T cover
**Step 2: Landscape Scan**
Run ONE broad WebSearch to get a lay of the land — what are people talking
about when they discuss this topic? This helps you discover perspectives and
angles you wouldn't think of from the topic name alone.
**Step 3: Perspective Discovery**
This is the key step. Don't just ask "what sub-questions cover this topic?"
Instead ask: "Who has a stake in this? What lenses exist for seeing it?"
For each perspective, think about:
- What would a RESEARCHER studying this focus on? (mechanisms, evidence, theory)
- What would a PRACTITIONER care about? (does it work, how to implement, gotchas)
- What would a CRITIC argue? (limitations, risks, what's overhyped)
- What would someone from a DIFFERENT DOMAIN notice? (analogies, patterns, cross-pollination)
- What would a NEWCOMER find confusing? (the things experts take for granted)
Not every topic needs all five — some are more technical, some more contested,
some more practical. Pick 3-5 perspectives that would yield genuinely different
insights.
For `--quick` mode: skip perspective discovery. Just generate 3-4 direct
sub-questions.
**Step 4: Build Research Tree**
For each perspective, generate 1-3 specific sub-questions. Each sub-question
gets tailored search queries for the right source types:
- Web queries for current state, implementations, opinions
- Academic queries for empirical evidence, mechanisms, theory
- Reddit/community queries for practitioner experience, gotchas, real-world usage
The planner picks which source types fit each sub-question — don't search
Reddit for pure math theory, don't search papers for "what's the community
sentiment."
**Step 5: Write Output Files**
Write TWO files using the Write tool:
1. `{WORK_DIR}/state.md` — the living research document:
```markdown
# Deep Research: {TOPIC}
## Status
Stage: PLANNING_COMPLETE
Round: 0
Depth: {quick|standard|deep}
## Objective
{One paragraph: what we're trying to understand and why it matters}
## Vault Context
- {Existing note} — covers {what}
- {Existing note} — covers {what}
- Gap: {what the vault doesn't have}
## Perspectives
1. {Perspective name} — {one-line description}
2. {Perspective name} — {one-line description}
...
## Sub-Questions
- SQ1: {question} [perspective: {N}]
- SQ2: {question} [perspective: {N}]
...
## Coverage Map
{Empty for now — the critic fills this in after exploration}
## What's Been Found
{Empty for now — updated after each exploration round}
```
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 · 148 lines · 27 tokens per session scan A bfc3989083e3
research-planner is an agent published in the GitHub repository tuan3w/obsidian-vault-agent (39 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 1,477 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
Read-only ingestion worker for one already-captured source. Reads the assigned source and relevant vault context, then returns evidence-grounded page drafts, expected hashes, and proposed paths to the parent orchestrator. It never writes or applies the shared transaction.
phoenix
Refactoring planning AND migration planning.
maestro
Multi-agent coordination for complex patterns.
atlas
End-to-end and acceptance test execution.
herald
Release prep, version bumps, changelog generation.
judge
Refactoring and code transformation review.