CC super-research
Autonomous research skill for open-ended, high-volume research work — an agent left running for a while (minutes to overnight) that produces honest, comparable, auditable evidence instead of a single one-shot answer. Covers eight modes selected by the request: (1) experiment loop — iteratively edit code, run, measure a metric, keep or revert (baseline → hypothesize → run → keep/revert loop; use for "optimize X", "tune hyperparameters", "run experiments overnight", "autonomously improve this model", "hill-climb a metric", "自动实验"); (2) topic survey / 主题调研 — collect and synthesize sources on a question (use for "survey the literature on X", "research topic Y", "调研 Z", "literature review", "deep research", "what's the state of the art in", "gather evidence about"); (3) quantitative analysis / 量化分析 — reproducible, hypothesis-first data analysis with schema audit, effect sizes, and caveats (use for "analyze this dataset", "量化分析", "test whether X correlates with Y", "compute the effect of", "investigate this data"); (4) benchmark comparison / 对比评测 — pick among N candidates under a fair, fixed matrix (use for "compare X vs Y", "which library/model/prompt is best for us", "benchmark these options", "选型", "对比评测"); (5) root-cause investigation / 根因排查 — hypothesis-driven, two-way-reversal debugging of regressions, flakes, and perf drops (use for "why is X broken", "root cause this", "debug the regression", "why is it flaky", "排查", "定位", "复盘"); (6) ablation study / 消融实验 — leave-one-out attribution of a system's components against a measured noise floor (use for "ablate X", "which parts of Y matter", "attribution study", "消融实验", "is component Z pulling its weight"); (7) paper reproduction / 复现论文 — implement a paper's method as a working repo with logged ambiguities (use for "复现这篇论文", "paper to code", "implement this method", "reproduce the main table of X"); (8) paper writing + citation audit / 写论文 & 引用校验 — draft or polish an academic paper and verify every citation against real
Autonomous research skill for open-ended, high-volume research work — an agent left running for a while (minutes to overnight) that produces honest…
As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Shorten the description to 1024 characters.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 2447 chars, limit 1024 - warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Autonomous research skill for open-ended, high-volume research wor… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
description-budgetdescription takes 2447 of the ~15000-char shared budget for all skills
Process rating: all ten parameters 61/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 19 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2175 tokens
- 100Progress reporting. Reports progress
- low No test case covers injection arriving through data
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 2447: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 36 example trigger phrases
- +4Structure: 5 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.