Long-Horizon Prompting Website

A dependency-free educational website and before/after lab for the long-horizon-prompting skill. It explains the method, provides a copyable builder template and checklist, maps the research evidence and caveats, and shows four complete prompt rewrites.

This example was built by applying the skill to itself: each "after" is a complete launch prompt produced with the skill's brief-writing workflow, and the scoring uses the rubric from task-brief-template.md.

Website

This is a Long-Horizon Prompting project site hosted from this monorepo. The on-page brand is Long-Horizon Prompting; GitHub Pages serves it under the repository path:

https://muratcankoylan.github.io/Agent-Skills-for-Context-Engineering/

Pages:

• index.html: topic overview, brief anatomy, system boundaries, and routes into the site
• guide.html: eight-step method, prompt/harness split, copyable template, checklist, and gotchas
• lab.html: complete prompt comparisons, downloads, copy controls, rubric, and residual risks
• references.html: research, vendor guidance, CDC case notes, evidence labels, and caveats

Website prompts expand fully. The screenshot renderer adds a capture-only body class so release PNGs remain bounded without changing the site.

Publish from this repository

1. In this monorepo: Settings → Pages → Build and deployment → Source = GitHub Actions.
2. Merge to main (or run Deploy long-horizon prompt lab via workflow_dispatch).
3. Open https://muratcankoylan.github.io/Agent-Skills-for-Context-Engineering/

Deployment is defined in .github/workflows/deploy-prompt-lab.yml. The deploy job runs only on main.

What is here

report/            CDC case-study artifacts, fetched local with provenance
  cdc_prompt.pdf     OpenAI's published GPT-5.6 Sol Ultra Cycle Double Cover prompt
  cdc_proof.pdf      The candidate proof (unreviewed; see caveats)
  cdc_prompt.txt     Text extraction (for grep/diff)
  cdc_proof.txt
  PROVENANCE.md      Source URLs, SHA-256, retrieval date, caveats
scripts/
  fetch_report.sh    Re-fetch the report PDFs and re-extract text
  verify_report.py   Assert the skill's annotated CDC reference matches the published prompt
  install_skill.sh   Install the skill into .cursor/skills and validate it
  build_lab.py       Source of truth: prompt pairs + rubric scores -> data/ and ui/data.js
  validate_site.py   Deterministic pages, metadata, link, reference, sitemap, and prompt checks
  capture.mjs        Render the UI and capture the release screenshots
data/prompt-pairs.json  Machine-readable pairs and scores
ui/                Static multi-page site and generated prompt downloads
screenshots/       Generated PNGs for sharing

The four use cases

Each "before" is a genuine prompt-engineered launch prompt (expert persona, task context, chain-of-thought, explicit output format, persistence). None is a strawman. Each "after" is a complete, copy-ready prompt that applies the pseudo-formal task-brief method; it is not a template or an outline.

Use case	Domain	Topology	Failure the brief pre-blocks
Autonomous model-improvement run	ML training	Single agent	"Beat the number" invites train-on-eval and single-seed noise wins
Parallel approximation-ratio proof	Algorithms / theory	64-worker orchestration	Frequent-sync consensus is a diversity-collapse recipe
Long-horizon distributed-systems RCA	Concurrency	Multi-session single agent	"Find the root cause" has no checkable success predicate
Autonomous security audit / red-team	AppSec	Parallel workers	Persistence + checklist inflates unconfirmed findings

The dominant edit differs by case (contamination guards for ML, diversity policy for the proof, a reproduction predicate for RCA, non-counting outcomes for the audit), which is the point: the skill is a checklist of independent failure modes, not one trick.

Reproduce

# From the repo root. Requires python3 with pypdf and Pillow, and Node with Chrome available.
python3 -m pip install pypdf Pillow skills-ref

# 1. Bring the exemplar report local and verify the skill reference is faithful to it
examples/long-horizon-prompt-lab/scripts/fetch_report.sh
python3 examples/long-horizon-prompt-lab/scripts/verify_report.py

# 2. Install the skill into this project's .cursor/skills and validate it
examples/long-horizon-prompt-lab/scripts/install_skill.sh

# 3. Build, validate, serve, or capture the site
cd examples/long-horizon-prompt-lab
npm ci
npm test
npm run serve
# In another terminal: npm run capture

Open http://localhost:8000/. Prompt Lab cases use URL hashes such as lab.html#security-audit. The site remains file-safe for local inspection, while HTTP is recommended for clipboard behavior and production parity.

How to read the scorecard

Each dimension is scored 0 (absent), 1 (present but gameable), or 2 (adversary-proof), straight from the skill's rubric. n/a marks dimensions that do not apply (the diversity policy is irrelevant to a single-agent run). The total is over applicable dimensions only.

Honesty and limits

• The scores use the skill's own rubric, so a high "after" score means the brief fully applies the skill's checklist, measured by that checklist. This is a structural comparison of specification quality, not an outcome benchmark. It shows the briefs are harder to satisfy with a near miss; it does not claim any particular run succeeds.
• Every pair carries a residual risk the brief cannot remove (harness-enforced budgets, lenient proof verification, solvability framing on ill-posed problems, prompt-advisory scope limits). These are shown in the UI, not hidden.
• The CDC candidate proof in report/ had no peer review or formalization at publication. The validated artifact of interest is the prompt structure, not the theorem. See report/PROVENANCE.md.
• .cursor/ is gitignored in this repo, so the installed skill copy is not committed; scripts/install_skill.sh is the reproducible record of the install.

Skills demonstrated

long-horizon-prompting (primary). Adjacent skills referenced by the briefs' division of labor: harness-engineering (runtime-enforced budgets and permissions), multi-agent-patterns (topology behind the orchestration policy), advanced-evaluation (adversarial audit design).