hot-start
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Minimal crystallization strategy for users who already have a specific research topic or problem (e.g., "I want to improve CoT faithfulness in LLMs") and need structuring into a formal North Star. Heavily simplifies or skips exploration tactics, focusing on obstacle analysis, goal decomposition, and synthesis. Use when the user's first message reveals a specific, actionable research direction.
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
- 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. 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 67
When to iterate and where to return to
- Distinctive: line 68
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
Hot Start Strategy
The user already knows their direction. Your job is to structure it, not explore alternatives.
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 (heavily simplified) → landscape-reconnaissance (skipped or minimal)
→ direction-narrowing (heavily simplified) → obstacle-analysis (simplified)
→ goal-decomposition → north-star-synthesisThis is a reference, not a mandate. The user already knows their direction. landscape-reconnaissance and direction-narrowing may only need a few searches for context — or may be skipped entirely if the user's topic is already well-defined.
Simplification Guidance
- actor-profiling: Focus only on resources and constraints relevant to the stated topic. Skip broad background exploration.
- landscape-reconnaissance: Usually skippable. Only invoke if you need context about the field to properly structure the user's goal.
- direction-narrowing: Usually skippable. The user has already narrowed. Only invoke if their stated topic is still too broad for a single North Star.
- obstacle-analysis: Focus on the specific obstacles to their stated direction, not hypothetical alternatives.
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 hot-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?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- 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.

