Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add docxology/template --skill long-horizon-promptinggit clone --depth 1 https://github.com/docxology/templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/docxology/template/long-horizon-prompting)<a href="https://agentmods.dev/skills/docxology/template/long-horizon-prompting"><img src="https://agentmods.dev/badge/skills/docxology/template/long-horizon-prompting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/docxology/template/long-horizon-prompting"><img src="https://agentmods.dev/badge/skills/docxology/template/long-horizon-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00154 | $0.05065 |
| Opus 5 | $0.00077 | $0.02533 |
| Sonnet 5 | $0.00031 | $0.01013 |
| Haiku 4.5 | $0.00015 | $0.00507 |
Grade A, and why
long-horizon-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to long-horizon-prompting — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
The controlling trade-off: everything that makes a long run productive (persistence, autonomy, parallelism) also raises the cost of a weak specification. A short interactive prompt fails cheaply; a long-horizon brief with a loophole burns hours of compute producing an answer-shaped artifact that does not solve the problem.
When to Activate
Activate this skill when:
- Writing or reviewing the prompt for a long-running autonomous run before launching it
- Converting a vague hard problem ("solve X", "figure out why Y happens") into an explicit brief with a success predicate and non-counting outcomes
- Writing the root or orchestrator prompt that manages many parallel workers on an open-ended search problem
- Adding persistence instructions, stop conditions, effort floors, or return gates to an agent prompt
- Diagnosing a failed long run whose failure traces to the brief: premature return, an answer-shaped near miss, all workers converging on one approach, or fabricated completion claims
- Building a pre-launch review step that enhances and evaluates prompts before expensive agent time is committed
Do not activate this skill for adjacent work owned by other skills:
- Agent topology, supervisor versus swarm choice, handoff protocols, and coordination mechanics:
multi-agent-patterns. That skill owns the architecture; this skill owns the words that steer it. - Runtime control surfaces, locked evaluators, rollback, durable logs, and approval boundaries around an autonomous loop:
harness-engineering. Constraints that must survive optimization pressure belong in the harness, not the prompt. - Building the evaluator, regression suite, or deterministic quality gates a run is scored by:
evaluation. - LLM-as-judge design, rubrics, pairwise comparison, and bias mitigation:
advanced-evaluation. - Compaction, note-taking, and cross-session memory mechanics for surviving context limits:
context-compression,memory-systems,filesystem-context. - Loops that modify their own harness or prompts:
self-improvement-loops. - Remote sandboxes and background execution infrastructure:
hosted-agents.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 275 lines · 154 tokens per session scan A 75f8c53453fb
long-horizon-prompting is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 154 tokens to every session and 5,065 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to long-horizon-prompting, differing in 0 lines, and is treated as a copy.
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llm-application-dev
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firebase-ai
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coding-agents-prompt-authoring
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fixing-prompt
Prompt: Prompt Refinement and Optimization.