Run a Cold Review Panel
SkillDev toolsUse when a manuscript you wrote is nearly finished and needs a hostile read before it is submitted, including a mock review, a simulated reviewer panel, a pre-submission review, red-team the paper, asking whether this would get rejected, or checking which reviewer objection the paper cannot survive. Use for your own unsubmitted manuscript; reviewing someone else's submission as an invited referee is a different task.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Run a Cold Review Panel skill
What this skill tells your AI
The instructions your AI receives, as published by gxcaesar/open-research-skills in skills/run-cold-review-panel/SKILL.md and read by ahel’s review.
Read your own manuscript with reviewers that have never seen the project. The value comes entirely from what they do not know: an author's summary of the work repairs, in the reading, exactly the gaps a reviewer would have fallen into.
The panel is inbound. The manuscript is yours and has not been submitted.
Select one mode
| Mode | Responsibility | Read first |
|---|---|---|
isolation-manifest | Construct and prove the reviewers' context, from the filesystem rather than from assertion | references/isolation.md |
lens-assignment | Assign the reading lenses and the model families that carry them | references/lenses.md |
artifact-execution | Have one reviewer actually run the released artifact and report what happened | references/artifact-execution.md |
meta-review | Adjudicate the reports into one decision with prioritised repairs | references/meta-review.md |
Resolve the installed skill and components
Resolve SKILL_DIR as the directory containing this SKILL.md. Every runtime resource
is below that directory; never resolve through a package parent.
Prove the isolation, do not assert it
A reviewer that has absorbed the project's own framing will reproduce it. The panel is worthless unless each reviewer's inputs are known, and knowing them means constructing them.
Build a review directory containing exactly what a reviewer would receive: the manuscript, the figures, the supplementary material, and the artifact as released. Record the listing of that directory, byte sizes included, as the isolation manifest.
A statement from the reviewer that it has no other context is not evidence. A model's report about its own inputs is a description it generates, not an observation it makes. The directory listing is the observation. Where the runtime permits it, start each reviewer in that directory and nowhere else.
Give the lenses different jobs
Five reviewers reading for the same thing produce one review five times. Assign distinct lenses and keep them separate until adjudication.
| Lens | Reads for |
|---|---|
| Claim support | Whether each stated claim is carried by the evidence offered for it |
| Method correctness | Whether the procedure could produce the reported number at all |
| Comparison fairness | Whether the comparators were given a real chance |
| Reproducibility | Whether the released material lets someone else reach the result |
| Significance | Whether the result, if entirely true, matters to the stated audience |
Carry the lenses across more than one model family where that is available. Two families disagree in different places, and the disagreements are where the manuscript is soft.
One reviewer runs the artifact
At least one lens executes the released material rather than reading about it. Reports that a package installs, that a command exists, or that a script "should" reproduce a figure are not findings. What happened when it was run is a finding.
Record the commands and their output verbatim, including the failures. A panel that never touched the artifact cannot speak to reproducibility, and should say so instead of implying otherwise.
Adjudicate, then prioritise
The meta review is not an average. Adjudicate each finding: confirmed, unsupported, or needing evidence the panel did not have. A finding raised by one lens and missed by four is not thereby weak; a finding raised by all five may still be wrong about the paper.
Order the surviving findings by what a real rejection would rest on, and state for each what change would remove it and what that change costs.
Boundaries
This skill produces a rehearsal, not an acceptance prediction. It cannot see the venue's actual reviewer pool, the committee's calibration, or the competing submissions. It also does not edit the manuscript: repairs are handed back to whoever owns the prose.
Signals
- GitHub stars
- 33
- Forks
- 1
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packages (in examples/panel-round/clean.json)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Catalog kind
- skill
- Gateway key
run-cold-review-panel- Source
- github.com/gxcaesar/open-research-skills