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 skills add ShaishavMaisuria/research-paper-lifecycle-skills --skill paper-profilegit clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-skillsWrote 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/skills/shaishavmaisuria/research-paper-lifecycle-skills/paper-profile)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/paper-profile"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/paper-profile/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/skills/shaishavmaisuria/research-paper-lifecycle-skills/paper-profile"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/paper-profile.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 | $0.00183 | $0.02299 |
| Opus 5 | $0.00092 | $0.01149 |
| Sonnet 5 | $0.00037 | $0.00460 |
| Haiku 4.5 | $0.00018 | $0.00230 |
Grade A, and why
paper-profile 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 12d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Profile
Elicits the author's paper positioning once, stores it in
.paper-memory/profile.yml, and lets every other skill read it so the whole
toolkit behaves context-awarely instead of asking the same questions over and
over. This is the "ask me about my paper" front door: a short, optioned
interview, then a small validated YAML file the rest of the repo consumes.
It is a copilot, not an oracle. The profile records your stated intent (what kind of paper this is, who it's for, how bold you want to be). It does not judge whether the science is good and it never predicts acceptance.
When to use
- "Set up my paper profile" / "ask me about my paper" / "what verticals?"
- Starting a new paper and wanting the toolkit tuned before drafting.
- "Remember my writing style / positioning across skills."
- Any other skill notices
.paper-memory/profile.ymlis missing and you want to create it so that skill can personalize. - Re-run any time the positioning changes (e.g. you drop down a venue tier, or pivot from systems to empirical framing).
Inputs
- The user's paper working directory (where they want
.paper-memory/to live). This is the user's paper repo, not this skills repo. - The user's answers to the interview (you ask; they pick). Nothing else is required — there is no network call and no file the user must pre-create.
- Optional: an existing
.paper-memory/profile.ymlto update instead of starting fresh.
Process
-
Locate the paper directory and the memory dir. Confirm with the user where their paper lives; the profile goes in
<paper-dir>/.paper-memory/. If one already exists, load and show it (profile_io.py show) and offer to update rather than overwrite. -
Show the option menu, then interview. Print the blank template so the user sees the choices, then ask through them. Get the exact vocabulary from the script so you never invent a value:
python3 scripts/profile_io.py schema. Ask in this order, always offering the options and a one-line gloss of each (full descriptions live in references/positioning-axes.md):
What ships with it
4 files 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.
- 12d ago First seen · 172 lines · 183 tokens per session scan A 8f0599b0e9d0
paper-profile is a skill published in the GitHub repository ShaishavMaisuria/research-paper-lifecycle-skills (42 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 183 tokens to every session and 2,299 once invoked, about $0.0009 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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