Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› imc-related-work
imc-related-work
GitHub指导IMC论文相关工作撰写，强调基于数据集、视角和工具的定位。涵盖测量文献细分、差异优先描述、对比现有数据集与偏见、正确归属会议（如USENIX/CCS/SIGCOMM）及遵守双盲评审规范。
Trigger Scenarios
撰写IMC论文的related-work部分
审计IMC论文的定位准确性
检查自我引用的双盲合规性
区分IMC与其他顶级会议的前置工作
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill imc-related-work -g -y
SKILL.md
Frontmatter
{
"name": "imc-related-work",
"description": "Use when writing or auditing the related-work and positioning of an ACM IMC paper, covering the measurement literature lanes, positioning against prior datasets and vantage points, delta-first framing, citing the datasets and tools you build on, distinguishing IMC from SIGCOMM\/NSDI\/PAM prior art, and keeping self-citations double-blind."
}
IMC Related Work
Use this to position an IMC paper. In measurement, related work is not just prior claims — it is prior datasets, vantage points, tools, and methodologies. IMC reviewers know the measurement landscape intimately; a positioning that ignores the dataset or platform your finding builds on, or that misattributes measurement work to the wrong venue, reads as unfamiliarity with the field.
Cover the measurement literature lanes
Position against the relevant lanes, not a generic "prior work" pile:
- The same phenomenon, measured before: who measured this, from which vantage points, when, and what did they find? Your delta is usually breadth, recency, a better vantage point, or a correction.
- The datasets and platforms you use: RIPE Atlas, CAIDA data, scan datasets, DNS platforms, top lists, telescopes — cite the paper that introduced the instrument, and note its known biases.
- The methodology you extend or question: if you improve or audit a measurement technique, cite its origin and state what you change.
- Adjacent findings that frame significance: related measurements that make your question matter.
Delta-first positioning
Lead each comparison with the difference, not a summary:
- "Prior work measured X from a single vantage point in 2019; we measure it from N vantage points across M regions over a 12-week window in 2026, revealing a diurnal pattern invisible to a snapshot."
- Make the delta a measurement delta: more/better vantage points, longer window, better ground truth, a corrected input, or a newly reachable dataset — not merely "we also study this."
Position against prior datasets and vantage points
This is IMC-specific and easy to underdo:
- If you reuse a dataset, say which version/vintage and why it still supports your claim.
- If you build a new dataset, contrast its coverage and provenance with existing ones — this is
also the seed of a Community Contribution Award case (
imc-artifact-evaluation). - Acknowledge the biases of instruments you rely on (e.g., top-list instability, telescope coverage gaps); a reviewer who introduced that instrument may be reading you.
Get the venue attribution right
Measurement work is spread across venues, and misattribution signals unfamiliarity:
- Much scanning/tooling landed at USENIX Security or CCS (e.g., ZMap, Censys), not IMC.
- Systems and protocol design with a measurement flavor is often SIGCOMM, NSDI, or CoNEXT.
- Focused active-measurement work often appears at PAM.
- Check dblp before writing "as shown at IMC" — cite the actual venue (see
resources/exemplars/library.mdfor the sibling-confusion guard).
Keep self-citations double-blind
IMC is double-blind, so positioning against your own prior work is a leak risk:
- Refer to your prior work in the third person: "Prior work [12] measured..." not "In our earlier work we...".
- Do not let a chain of self-citations, a reused private vantage point, or a distinctive testbed name deanonymize you.
- Cite your own datasets by their anonymized/public identifier, not an account or lab URL.
Failure patterns
| Pattern | Why it hurts | Fix |
|---|---|---|
| Prior work summarized, delta implied | Reviewer cannot see the contribution | Lead every comparison with the measurement delta |
| Instrument reused without citing its origin/bias | Reads as unaware of the field | Cite the dataset/tool paper and its known biases |
| Measurement misattributed to IMC | Signals unfamiliarity | Verify the venue on dblp; cite correctly |
| Self-citation in first person | Double-blind leak | Third-person; anonymized dataset references |
Output format
[Lane coverage] phenomenon / datasets-platforms / methodology / adjacent framing — all covered?
[Delta] stated as a measurement delta (vantage points/window/ground truth/dataset)? yes/no
[Dataset positioning] instruments cited with vintage + biases? yes/no
[Venue attribution] measurement prior art cited to the correct venue? yes/no
[Anonymity] self-citations third-person, no infrastructure leak? passed/issues
[Edits] <ordered list>
Version History
- 9f86f09 Current 2026-07-19 16:01
