Scope Fit (cc-scope-fit)
SkillMediaUse when deciding whether a molecular / translational oncology study belongs in Cancer Cell (Cell Press) before investing in full submission. Diagnoses mechanism + translational-relevance fit; it does not design experiments or edit prose.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Scope Fit (cc-scope-fit) skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Cancer-Cell-Skills/skills/cc-scope-fit/SKILL.md and read by ahel’s review.
When to trigger
- Starting a project and unsure if Cancer Cell is the right venue
- A reviewer or PI says "this feels incremental" or "too descriptive"
- Considering a Cell Press presubmission inquiry
- Deciding between Cancer Cell and a broader-scope or specialty journal
What Cancer Cell wants
Cancer Cell publishes mechanistic, hypothesis-driven cancer biology and translational oncology. The recurring acceptance pattern combines two pillars:
- Mechanistic depth — a defined molecular mechanism (a pathway, regulatory axis, genetic/epigenetic event, or cell-cell interaction), not just a phenotype or a correlation.
- Translational relevance — the mechanism matters for human cancer: it is anchored in patient data, predicts a vulnerability, or motivates a therapeutic / biomarker strategy.
In-scope topics include tumor biology and signaling, cancer genetics / genomics, the tumor microenvironment, immuno-oncology, metastasis, therapy resistance, and clinical-translational studies that carry mechanistic insight.
Fit decision table
| Signal in the manuscript | Fit verdict |
|---|---|
| Clear mechanism + validated in cells, in vivo, AND human/patient data | Strong fit |
| Mechanism + in vivo, human data is associative but supportive | Likely fit — strengthen human anchor |
| Mechanism only in cell lines, no in vivo, no human relevance | Off-fit — go back to cc-study-design |
| Descriptive omics / atlas with no mechanism or vulnerability | Off-fit unless reframed around a mechanism |
| Strong clinical correlation but no mechanism | Off-fit — likely a specialty / clinical journal |
| Methods / tool paper without a cancer-biology discovery | Off-fit — a methods journal |
| Therapeutic claim with only in vitro support | Premature — needs in vivo / human validation |
How to position the contribution
- State the gap in mechanistic understanding, not just "X is understudied."
- Name the orthogonal systems that will close it (cells + in vivo + human).
- Make the translational hook explicit and proportionate to the evidence (mechanism → vulnerability → candidate intervention/biomarker).
- Compare to the closest 2–3 prior papers and say precisely what is new (new mechanism, new node, new context, new in vivo proof).
Presubmission inquiry decision gate
For a borderline paper, reduce the decision to three evidence questions before investing in a full Cancer Cell package:
| Question | Strong answer | Weak answer |
|---|---|---|
| Mechanism | The causal molecular axis is perturbed, rescued, and connected to phenotype. | The axis is inferred from correlation or omics enrichment only. |
| Human anchor | Patient samples, clinical dataset, organoid, or translational model supports relevance. | Only immortalized cell-line evidence or an anecdotal clinical correlation. |
| Therapeutic/biomarker logic | The intervention, vulnerability, or stratification claim follows from the mechanism. | Translational language is aspirational and not tested. |
If one column is weak, the best next move is usually cc-study-design, not cover-letter polish. If all
three are strong, a presubmission inquiry can emphasize the mechanism, the human anchor, and the exact
delta over the nearest Cancer Cell or Cell Press papers.
Checklist
- One sentence states the mechanism (molecule/axis → effect on cancer phenotype)
- Mechanism is validated in ≥2 orthogonal systems, ideally including human/patient data
- Translational relevance is explicit and matched to the evidence level
- Closest prior work is identified; the advance over it is specific
- The study is hypothesis-driven, not purely descriptive
- Therapeutic / biomarker claims are backed by in vivo and/or human data
- If a clinical-trial-style study, mechanism still carries the novelty
Anti-patterns
- A single-system (cell-line-only) story pitched as a major mechanism
- Descriptive single-cell / genomic atlas with no functional mechanism or vulnerability
- "Therapeutic target" framing with no in vivo efficacy or human evidence
- Overclaiming clinical impact from a correlation
- Repackaging an incremental extension of the lab's prior paper without a new mechanistic node
Output format
【Scope verdict】Strong fit / Likely fit / Off-fit
【Mechanism (1 sentence)】...
【Orthogonal validation present】cells / in vivo / human — list which
【Translational hook】... (and whether evidence supports it)
【Gap vs. closest prior work】...
【Next step】Strengthen via cc-study-design / proceed to cc-study-design / reconsider venue
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
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cc-scope-fit- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills