warm-start
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Simplified crystallization strategy for users who have a general research direction (e.g., "I'm interested in LLM reasoning") but lack specificity. Simplifies actor profiling and landscape reconnaissance, then proceeds through direction narrowing, obstacle analysis, goal decomposition, and north-star synthesis. Use when the user's first message reveals a general area but not a specific problem.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- creationisttest-git/startup_studiotheir own version, first seen Sep 12, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- creationisttest-git/startup_studio★ 0same 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
- cold-startSkill
- 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
The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 9 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈9
Distinctive · estimate
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 64
When to iterate and where to return to
- Distinctive: line 65
When enough information exists to move forward
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Warm Start Strategy
The user has a general direction — they know the field or area but not the specific problem.
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 (simplified) → landscape-reconnaissance (simplified or skipped)
→ direction-narrowing → obstacle-analysis → goal-decomposition → north-star-synthesisThis is a reference, not a mandate. How to simplify, how much to simplify, whether to skip entirely — these are your decisions. This strategy suggests simplification as the default posture, but you judge based on what the user's initial message reveals.
Simplification Guidance
- actor-profiling: The user's stated direction already reveals partial context. Focus on resources, constraints, and intentionality rather than exhaustive background exploration.
- landscape-reconnaissance: The user already knows the general field. You may skip broad scanning and go directly to direction-narrowing, or do a targeted scan of the specific sub-area they mentioned.
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
- Simplification guidance for the warm-start context
- 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?
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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
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Now do this for your own.
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