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/smixs/autographWrote 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/smixs/autograph/research)<a href="https://agentmods.dev/commands/smixs/autograph/research"><img src="https://agentmods.dev/badge/commands/smixs/autograph/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/smixs/autograph/research"><img src="https://agentmods.dev/badge/commands/smixs/autograph/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 | $0.00047 | $0.01426 |
| Opus 5.5 | $0.00019 | $0.00570 |
| Sonnet 5.5 | $0.00009 | $0.00285 |
| Haiku 4.5 | $0.00005 | $0.00143 |
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 1mo 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/autograph:research — interactive bootstrap
You are orchestrating an interactive bootstrap of a chaotic or empty Obsidian vault. The user invoked /autograph:research $ARGUMENTS. Treat $ARGUMENTS as the vault path. If empty, ask for it with AskUserQuestion.
This command only runs when the vault lacks a usable schema and structure. For structured vaults, redirect the user to the normal bootstrap workflow (references/bootstrap-workflow.md in the autograph skill).
Workflow
Phase 1 — Gate (is /research the right tool?)
Run discovery to see what we have:
uv run <skill-dir>/scripts/discover.py "$VAULT" --verbose > /tmp/autograph-discovery.json
uv run <skill-dir>/scripts/research.py plan "$VAULT" > /tmp/autograph-plan.json
Read /tmp/autograph-plan.json. It contains gate with fields:
files_total,folders_totalhas_schema_json(bool)frontmatter_coverage(0–1)verdict:"chaos"|"structured"|"empty"
If verdict == "structured": stop. Tell the user:
This vault already has a workable structure (N files, X% with frontmatter, schema.json present).
/researchis for empty or chaotic vaults. Usediscover.py→generate_schema.py→swarm_prepare.pyfrom the autograph skill instead.
Do not proceed to Phase 2 unless the user overrides.
If verdict == "empty" (≤ ~20 files): skip Phase 3 swarm — go straight to Q&A (Phase 2) + manual schema draft.
If verdict == "chaos": continue to Phase 2.
Phase 2 — First-wave Q&A
Use AskUserQuestion to establish intent. Ask up to 4 at once:
- Purpose — personal / work / research / mixed
- Expected domains — free-form list, show 3–5 folder names already present as hints
- Content language — ru / en / mixed / other
- Schema ambition — minimal (3–5 types) / standard (6–10) / full taxonomy (10–15)
Store answers in memory — you will feed them to Phase 4.
Phase 3 — Swarm exploration (only for chaos)
/tmp/autograph-plan.json contains a manifests array. Each manifest has a non-overlapping subset of files (≤ 20 each) bin-packed by folder. Spawn parallel Agent calls — one per manifest, up to 5 in a single message — with subagent_type: "general-purpose".
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.
- 1mo ago First seen · 111 lines · 47 tokens per session scan A 50e02515fa86
research is a command published in the GitHub repository smixs/autograph (75 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 1,426 once invoked, about $0.0002 per session on Opus 5.5. 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 commands, from other repositories
adversarial-review
Adversarial, reproduce-by-execution review of a GitHub PR (fan-out sub-agents, verify, draft review).
evolve-lite-adapt-memory
Mirror a just-saved native memory into the shared evolve store so it becomes shareable and auditable.
evolve-lite-publish
Publish a private entity (guideline, feedback, project, user or reference) to a configured write-scope repo.
evolve-lite-subscribe
Add a shared guidelines repo (read-scope subscription or write-scope publish target) to the unified repos list.
slm-governance
Governed-workspace behavior for SuperLocalMemory. Covers roles (admin/member/viewer) and company mode, who administers a company-mode workspace (a signed-in admin, or slm team on the SLM computer), slm token show in strict mode, what remote web apps can never see or change, retention and lifecycle settings, the audit…
slm-mesh
Cross-session peer coordination via the SLM mesh network. Lets multiple AI agent sessions on the same machine, and web apps the owner has allowed (ChatGPT, Claude on the web, Grok Bot, Muse, Composio), discover each other, send messages, wait for replies with meshwait, share lightweight state, and lock files to avoid…