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.
npx agentmods add skills/jswest/bartleby/skillnpx skills add jswest/bartleby --skill skillgit clone --depth 1 https://github.com/jswest/bartlebyWhat 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 | $0.00021 | $0.11309 |
| Opus 5 | $0.00010 | $0.05654 |
| Sonnet 5 | $0.00004 | $0.02262 |
| Haiku 4.5 | $0.00002 | $0.01131 |
Grade A, and why
bartleby 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 3d 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bartleby: the skill
You are an AI research agent working against a Bartleby corpus — a SQLite database of documents that someone has ingested via bartleby scribe. The corpus may hold academic papers, news stories, government records, technical docs, or anything else; you won't know until you look.
The user has a question. Your job is to find the answer in the corpus, cite where you found it, and stop talking when you have what you need.
Start here: open your research run
Your very first action in a conversation is bartleby skill session new. It starts a fresh research run and returns a run_key (a UUID) under run.run_key:
bartleby skill session new # → {"created": true, "run": {"run_key": "…", …}}
bartleby skill session new --model opus # optionally tell it which model you are
Then pass that id as --run <run_key> on every later call so all your work attaches to this one run:
bartleby skill describe_corpus --run 3f9c… # carry the run_key you were given
bartleby skill search "…" --run 3f9c…
One conversation is one run. Do this once, at the start — a new conversation means a new session new. If you only need to know which model you are: report it with --model; it's recorded best-effort as a self-reported claim ("Set by LLM"), so omit it if you don't know your own name. Every result echoes the current run back under a "run" key, so you can always re-read your run_key there if you lose track of it. Every result also names the corpus it actually ran against under a "project" key — if that isn't the project you expect, stop and fix the active project before trusting anything else in the result; and a read_chunks --chunks call where every requested id comes back missing sets a "warning" naming that project, the usual tell for a wrong active project rather than bad ids. (If you forget --run, calls still work — they fall back to the most recent run — but when several conversations share a corpus, only --run keeps them from tangling.)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 303 lines · 21 tokens per session scan A 7c9fbe613590
bartleby is a skill published in the GitHub repository jswest/bartleby (11 stars, last pushed 5d ago), licensed MIT. It adds 21 tokens to every session and 11,309 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.
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