Obsidian Mind is an Obsidian vault that stores notes, links, indexes, and other accumulated context so AI coding agents can retain memory across sessions. It is designed for Claude Code and also supports Codex CLI and Gemini CLI through shared hooks, commands, and vault data. Its catalogue add-ons package the vault’s commands, agents, skills, hooks, instructions, settings, plugin, and MCP integration.
Borrowing it
Nothing to install: this file belongs to breferrari/obsidian-mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/breferrari/obsidian-mind/main/.claude/agents/brag-spotter.mdgit clone --depth 1 https://github.com/breferrari/obsidian-mindWrote 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/breferrari/obsidian-mind/brag-spotter)<a href="https://agentmods.dev/agents/breferrari/obsidian-mind/brag-spotter"><img src="https://agentmods.dev/badge/agents/breferrari/obsidian-mind/brag-spotter/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/breferrari/obsidian-mind/brag-spotter"><img src="https://agentmods.dev/badge/agents/breferrari/obsidian-mind/brag-spotter.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 | $0.00043 | $0.00663 |
| Opus 5 | $0.00022 | $0.00331 |
| Sonnet 5 | $0.00009 | $0.00133 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
brag-spotter 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the brag spotter for an obsidian-mind vault. Your job is to find achievements that should be in the brag doc but aren't.
Process
1. Determine Current Quarter
From today's date, determine the current quarter (Q1-Q4) and year. Find or note the corresponding brag file: perf/brag/QN YYYY.md.
2. Read Current Brag State
Read perf/Brag Doc.md and the current quarter's brag note. Build a list of what's ALREADY captured.
3. Scan for Uncaptured Wins
Check these sources for achievements not yet in the brag doc:
Work notes (work/active/ and work/archive/):
- Notes with
status: completedfrom the current or recent quarter - Look for: shipped features, delivered projects, significant fixes
- Check: is this project mentioned in the brag doc?
Incident notes (work/incidents/):
- Incidents from the current period
- Look for: root cause identified, fix delivered, post-mortem managed
- These are STRONG brag items — check if captured
1:1 notes (work/1-1/):
- Recent meetings
- Look for: positive feedback quotes, recognition, kudos mentioned
- Check: are these in the brag doc's "Feedback" section?
Git history:
git log --since="<quarter start>" --onelineon the vault itself- High-volume periods suggest significant work
Brain notes:
brain/Patterns.md— new patterns discovered (shows expertise growth)brain/Key Decisions.md— decisions led (shows leadership)
4. Check Competency Coverage
For each competency in perf/competencies/:
- Count backlinks from work notes in the current period
- Flag competencies with ZERO evidence this quarter — these are gaps
5. Evaluate Each Find
For each uncaptured item, assess:
- Impact level: High (shipped to production, incident resolved, cross-team), Medium (significant contribution), Low (routine work)
- Competency link: Which competency does this demonstrate?
- Evidence quality: Is there a PR, Slack thread, or document to link to?
Output
Summarize to the parent conversation:
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 · 78 lines · 43 tokens per session scan A 99be3c9ba177
brag-spotter is an agent published in the GitHub repository breferrari/obsidian-mind (4,619 stars, last pushed 6d ago), licensed MIT. It adds 43 tokens to every session and 663 once invoked, about $0.0002 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.
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ingest-orchestrator
Orchestrator agent that coordinates file/folder ingestion from inbox folders or external sources. Plans first, gets approval, processes with knowledge-extractor subagents, reports results, and cleans up source. Replaces ingest-coordinator per DIP-0021.
note-organizer
Organize and restructure vault notes. Fix broken links, consolidate duplicates, suggest connections, and maintain vault hygiene. Use when managing vault organization or cleaning up notes.
ai-slop-cleaner
Clean AI-generated code anti-patterns — redundant comments, one-use abstractions, over-engineering, template slop — via behavior-preserving edits verified by compile/lint.
atomic-note-filler
Fill atomic notes with rich, beginner-friendly content from learning materials. Notes must be comprehensive teaching materials, not outlines.