cold-start
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Full crystallization strategy for users who have no research direction at all. Covers actor profiling, landscape reconnaissance, direction narrowing, obstacle analysis, goal decomposition, and north-star synthesis. Use when the user's first message reveals zero specificity about what they want to research.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- ZwaarContrast/skillstheir own version, first seen Nov 1, 2026
- garrytan/gbraintheir own version, first seen Oct 17, 2026
- Pearl1531/Archetype-talktheir own version, first seen Oct 13, 2026
- serithemage/serverless-openclawtheir own version, first seen Oct 8, 2026
- kunalsuri/ai-fication-kittheir own version, first seen Jul 11, 2026
- vivy-yi/xiaohongshu-skillstheir own version, first seen Jan 22, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- garrytan/gbrain★ 32ksame name
- vivy-yi/xiaohongshu-skills★ 455same name
- serithemage/serverless-openclaw★ 196same name
- kunalsuri/ai-fication-kit★ 3same name
- Pearl1531/Archetype-talk★ 2same name
- ZwaarContrast/skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository32 other files it gives its agents.
- actor-profilingSkill
- and-or-decomposeSkill
- ask-constraintsSkill
- ask-decomposition-validationSkill
- ask-intentionalitySkill
- ask-obstacle-acceptanceSkill
- assess-obstacle-severitySkill
- broad-paper-searchSkill
- broad-web-searchSkill
- clarify-resourcesSkill
- crystallize-north-starSkill
- deep-web-searchSkill
- direction-narrowingSkill
- explore-resumeSkill
- feasibility-checkSkill
- final-validationSkill
- formulate-top-goalSkill
- generate-candidate-fieldsSkill
- generate-research-briefSkill
- goal-decompositionSkill
- hot-startSkill
- identify-obstaclesSkill
- landscape-reconnaissanceSkill
- landscape-synthesisSkill
- north-star-crystallizationSkill
- north-star-synthesisSkill
- obstacle-analysisSkill
- present-and-askSkill
- present-candidatesSkill
- propose-mitigationsSkill
- validate-leavesSkill
- warm-startSkill
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 9 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
9
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
All SOPs in this strategy follow these rules:
- Distinctive: line 15
Prefer multiple choice when possible — easier to answer
- Distinctive: line 16
Always allow "unsure" / "TBD" as legitimate answers
- Distinctive: line 17
Always ask WHY — not just "what do you want" but "why do you want it"
- Distinctive: line 18
After user answers: confirm understanding before continuing
- Distinctive: line 19
If user's answer reveals new information: immediately follow up
- Distinctive: line 20
If user declines to answer (privacy): accept, note that downstream work becomes broader/more iterative
- Distinctive: line 58
When to iterate and where to return to
- Distinctive: line 59
When enough information exists to move forward
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Cold Start Strategy
The user knows nothing — they want to publish at a top venue but have no idea what to research.
Questioning Protocol
All SOPs in this strategy follow these rules:
- One question at a time — never overwhelm with multiple questions
- Prefer multiple choice when possible — easier to answer
- Always allow "unsure" / "TBD" as legitimate answers
- Always ask WHY — not just "what do you want" but "why do you want it"
- After user answers: confirm understanding before continuing
- If user's answer reveals new information: immediately follow up
- If user declines to answer (privacy): accept, note that downstream work becomes broader/more iterative
Available Tactics
| Tactic | Purpose |
| actor-profiling | Understand who the user is |
| landscape-reconnaissance | Broad, shallow field exploration |
| direction-narrowing | Focus within chosen field(s) |
| obstacle-analysis | Identify and mitigate barriers |
| goal-decomposition | KAOS-style AND/OR goal structuring |
| north-star-synthesis | Converge into North Star + ResearchBrief |
Default Flow (reference only)
actor-profiling → landscape-reconnaissance → direction-narrowing
→ obstacle-analysis → goal-decomposition → north-star-synthesisThis is a reference, not a mandate. You decide the actual execution path.
Iteration Points
- From obstacle-analysis: may return to landscape-reconnaissance, direction-narrowing, or obstacle-analysis itself
- From goal-decomposition: may return to landscape-reconnaissance, direction-narrowing, obstacle-analysis, or goal-decomposition itself
How to Use This Strategy
You are the general. This strategy gives you:
- A default flow as starting reference
- Available tactics with their purposes
- Iteration points where backtracking makes sense
What you decide:
- Whether to execute a tactic fully or partially
- Whether to skip a tactic entirely
- Whether to invoke individual SOPs directly (bypassing tactic framing)
- When to iterate and where to return to
- When enough information exists to move forward
The only non-negotiable: the process ends with north-star-synthesis producing a North Star + ResearchBrief that the user confirms.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

