long-horizon-prompting
Writes launch prompts for long-running autonomous agents and parallel orchestrations: exact success predicates, non-counting outcomes, persistence and stop rules and audit gates.
- Kind
- Skill
- Works with
- Any agent
- Source
- Agent Skills for Context Engineering @ 9ff0c99108b3
- Path
- skills/long-horizon-prompting/SKILL.md
- Jobs
- Documentation, Planning
- Topics
- Prompt engineering, Agents & MCP
Credit
- Made by
- muratcankoylan
- Original
- github.com/muratcankoylan/Agent-Skills-for-Context-Engineerin…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it from a plugin marketplace.
/plugin install context-engineering@context-engineering-marketplace
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/long-horizon-prompting/ (the skill folder)
- 5 file(s) the skill refers to
- Runs scripts
- None found
- Uses the network
- None found
- Needs secrets
- None found
- Source revision
- 9ff0c99108b3
- Licence
- MIT
- Last activity
- 12 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# Long-Horizon Prompting This skill covers the design of the prompt that launches an agent expected to work autonomously for hours or days, alone or as an orchestrator managing many parallel workers. The central technique is the pseudo-formal task brief: a specification written with the rigor of formal verification but expressed linguistically, because most hard problems have no machine-checkable success condition. The exemplar is the published prompt behind GPT-5.6 Sol Ultra's candidate proof of the Cycle Double Cover Conjecture, produced by a 64-subagent orchestration (claim-long-horizon-cdc-run). The prompt structure generalizes far beyond mathematics: any domain where success can be stated precisely and failure modes can be enumerated can use the same brief anatomy. In orchestrated runs the same discipline applies at every handoff: when a model optimized its own multi-agent workflow text in Sakana AI's MASS work, task information moved out of worker roles and into output contracts and call order (claim-long-horizon-contract-information-shift). …