research-dossier-discipline

research-dossier-discipline is a skill for Claude Code from AURORA-NEURO/aurora-agent. It costs 99 tokens per session (1,330 once invoked), scanned A, original, Apache-2.0.

A guide for writing honest reports about measurements from autonomous or agent-driven research runs. It limits conclusions to the tested synthetic worlds, retained evidence, and properties the evaluation actually measured.

In plain words
What is it for?
Use it to write research summaries, README sections, or dossiers that connect claims to artifacts, describe measured cases, and state what the evaluation cannot distinguish.
Why use it?
It prevents a benchmark result from being presented as a broad scientific fact when the run only supports a narrower observation. Negative findings and limits remain part of the report.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aurora-science plugin — 6 skills shipped together

Good fit Use it to write research summaries, README sections, or dossiers that connect claims to artifacts, describe measured cases, and state what the evaluation cannot distinguish.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aurora-neuro/aurora-agent/research-dossier-discipline
Install

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.

Any agent
npx skills add AURORA-NEURO/aurora-agent --skill research-dossier-discipline
Clone the repo
git clone --depth 1 https://github.com/AURORA-NEURO/aurora-agent

Made for: Claude Code.

Or install aurora-science, the plugin that ships this one along with the rest of its 6 skills.

Wrote 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.

agentmods badge for research-dossier-discipline

README.md
[![agentmods](https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/research-dossier-discipline/github.svg)](https://agentmods.dev/skills/aurora-neuro/aurora-agent/research-dossier-discipline)
Your own site
<a href="https://agentmods.dev/skills/aurora-neuro/aurora-agent/research-dossier-discipline"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/research-dossier-discipline/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.

agentmods 80×15 button for research-dossier-discipline

Your own site · 80×15
<a href="https://agentmods.dev/skills/aurora-neuro/aurora-agent/research-dossier-discipline"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/research-dossier-discipline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,330 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00099 $0.01330
Opus 5.5 $0.00040 $0.00532
Sonnet 5.5 $0.00020 $0.00266
Haiku 4.5 $0.00010 $0.00133

Measured 1mo ago against content hash 77c85fd8ebd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

research-dossier-discipline 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.

plugins/aurora-science/skills/research-dossier-discipline/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Note: the crate paths, documents, and measured numbers below are illustrations from the aurora-agent workspace where these methods were developed and tested. The discipline itself applies to any autonomous or semi-autonomous research write-up.

Research-dossier discipline

An autonomous run can produce real measurements and still produce a dishonest dossier, because the dishonesty lives in the write-up's altitude: measurements made at one level, claims stated at another. This skill fixes the altitude.

What a run may claim

An autonomous measurement run over synthetic worlds may claim, at most:

  • Measurements over the worlds it actually ran — fixtures and seeded generator families — identified precisely enough to regenerate (spec, seed, grid), with numbers pinned to retained artifacts or tests.
  • Observation- and evaluation-level statements: strategy X was admissible in N of M cells of this family; these two methods selected identical fact sets on every measured world; this postcondition failed on these instances.
  • Properties of its own instruments, stated as such: this benchmark cannot discriminate these methods; this tie is overdetermined by this construction property.

Every such claim inherits the scope of its worlds. The workspace's findings say it plainly of their own headline table: "That is partly a property of the benchmark, not of the methods."

What a run must never claim

This block is the honesty frame the workspace applies wherever research output is described. Carry it into the limitations section verbatim wherever your dossier describes research:

This is autonomous measurement science over SYNTHETIC decision worlds (fixtures and seeded generators). It can never claim: biology or medicine, literature or prior-work coverage, external-world observation, or release-level claims from fixture evidence. Oracle review is a human gate.

Unpacked:

  • No biology, no medicine. The evaluation contracts enforce this mechanically — the reproduction check refuses to convert a matched pipeline into a validity claim ("matching a pipeline cannot be promoted into biological validity", docs/EVALUATION_REPRODUCTION_CHECK.md), and the audit projections each end with the boundary that they do not establish biological, causal, or clinical validity. Where research is described at length, include the boundary sentence: "Research and developer infrastructure: it does not diagnose an individual, recommend treatment, triage care, enroll participants, or claim medical-device functionality."
  • No literature coverage. A run that searched nothing read nothing. "Related work" and "novel" are claims about the world's literature, and a dossier built from local fixtures has zero evidence about it. Say "not compared against prior work" rather than implying a survey.
  • No external-world observation. Synthetic worlds are constructed to have properties; finding those properties confirms the generator, not nature.
  • No release-level claims from fixture evidence. "Works on the shipped fixtures" is a regression statement. Readiness, robustness, and generality are claims about worlds not yet seen; the workspace's own tie is the cautionary case — a method that matched the compiler on every world measured, on a family whose construction overdetermined the match.
  • Oracle review is a human gate. Where a pipeline's judge is itself under evaluation, or a verdict feeds a consequential decision, a human review is part of the method, and the dossier records whether it happened — a structural success is never collapsed into approval.

Read the full file on GitHub · 103 lines

Changes

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.

  1. 1mo ago First seen · 103 lines · 99 tokens per session scan A 77c85fd8ebd4

Subscribe to this mod's changes

research-dossier-discipline is a skill published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 99 tokens to every session and 1,330 once invoked, about $0.0004 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 2026-08-31.

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