This library is not a research paper and does not claim that one prompt design solves agent reliability. It translates well-known prompt-engineering patterns, cognitive-review controls, and systems-engineering discipline into reusable prompts, skills, and acceptance contracts.
The site resolves each asset's curated research labels through research-sources.json. That registry is the normalized citation source for the links shown outside each copyable agent body.
Accurate product claim
The assets are research-informed, not magic. They are designed to improve structure, evidence discipline, reviewability, and failure disclosure. They do not guarantee correct output by themselves.
Important correction to common prompt advice
Do not design serious agent workflows around revealing hidden chain-of-thought. Use CoT-safe public reasoning artefacts instead:
- task restatement;
- assumptions table;
- branch comparison;
- decision log;
- inspected files and UI states;
- command output;
- verification table;
- failure disclosure;
- final status.
That gives reviewers useful reasoning structure without requiring private reasoning traces.
Research patterns mapped to this library
| Pattern | Source idea | Repository translation |
|---|---|---|
| Chain-of-Thought | Stepwise intermediate reasoning can improve complex reasoning. | Use public decision records, not hidden chain-of-thought. |
| Tree-of-Thoughts | Explore and evaluate multiple reasoning paths. | Require branch comparison for ambiguous/risky choices. |
| ReAct | Interleave reasoning and action with environmental observations. | Observe files/tests/UI before editing; observe results after actions. |
| Least-to-Most | Decompose hard tasks into easier sub-problems solved in sequence. | Force atomic implementation, debugging, and review steps. |
| Self-Consistency | Compare multiple reasoning paths before settling on an answer. | Require rival hypothesis or alternative route checks. |
| Self-Refine / Reflexion | Use feedback and failed attempts to improve later attempts. | Treat failed checks as inputs for revision, not as embarrassment. |
| Process supervision | Evaluate the route to an answer, not just the final result. | Require evidence ladders, command logs, and traceable statuses. |
| Constitutional / contract-based critique | Use explicit principles for critique and revision. | Use acceptance contracts with rejection wording. |
| Systems-engineering traceability | Map requirements to verification evidence. | Require claim → requirement → evidence → status mapping. |
Behavioural controls used across the library
- Checklist discipline — repeated, explicit checks reduce missed steps during complex work.
- Cognitive forcing — the assets force assumptions, uncertainty, scope, and evidence to be stated before final claims.
- Premortem analysis — agents must imagine failure before closing the task, which exposes risks that optimistic implementation often misses.
- Adversarial review — agents must search for evidence against their own preferred conclusion.
- Evidence calibration — confidence must be tied to inspected files, runtime behaviour, command results, and manual checks.
- Separation of duties — implementation, review, specialist review, and release judgement are intentionally separated.
- Acceptance contracts — contracts are phrased as rejection rules so incomplete work is easier to stop.
- Traceability — every material output claim must be tied to a requirement, artefact, evidence source, and status.
- Branch comparison — risky tasks must compare more than one path before committing to a route.
- Failure visibility — failed checks, skipped checks, blocked tools, and uninspected behaviour are first-class outputs.
Standards and documentation mapped into the assets
- Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”: https://arxiv.org/abs/2201.11903
- Yao et al., “Tree of Thoughts: Deliberate Problem Solving with Large Language Models”: https://arxiv.org/abs/2305.10601
- Yao et al., “ReAct: Synergizing Reasoning and Acting in Language Models”: https://arxiv.org/abs/2210.03629
- Zhou et al., “Least-to-Most Prompting Enables Complex Reasoning in Large Language Models”: https://arxiv.org/abs/2205.10625
- Wang et al., “Self-Consistency Improves Chain of Thought Reasoning in Language Models”: https://arxiv.org/abs/2203.11171
- Shinn et al., “Reflexion: Language Agents with Verbal Reinforcement Learning”: https://arxiv.org/abs/2303.11366
- Madaan et al., “Self-Refine: Iterative Refinement with Self-Feedback”: https://arxiv.org/abs/2303.17651
- Lightman et al., “Let’s Verify Step by Step”: https://arxiv.org/abs/2305.20050
- Bai et al., “Constitutional AI: Harmlessness from AI Feedback”: https://arxiv.org/abs/2212.08073
- Mitchell, Russo, and Pennington, “Back to the future: Temporal perspective in the explanation of events”: https://doi.org/10.1002/bdm.3960020103
- Evans and Stanovich, “Dual-Process Theories of Higher Cognition: Advancing the Debate”: https://doi.org/10.1177/1745691612460685
- ISO/IEC/IEEE 15288 systems and software engineering lifecycle framing: https://www.iso.org/standard/81702.html
- WCAG 2.2 accessibility success criteria and supporting guidance: https://www.w3.org/TR/WCAG22/
- NIST SSDF secure software development practices: https://csrc.nist.gov/pubs/sp/800/218/final
- OWASP Cheat Sheet Series practical security guidance: https://cheatsheetseries.owasp.org/
- NIST AI RMF and Generative AI Profile: https://www.nist.gov/itl/ai-risk-management-framework
- NIST ARIA pilot evaluation report: https://www.nist.gov/publications/assessing-risks-and-impacts-ai-aria-pilot-evaluation-report
- OWASP Top 10 for LLM Applications 2025: https://genai.owasp.org/llm-top-10/
- Google People + AI Guidebook: https://pair.withgoogle.com/guidebook-v2/
- Microsoft Guidelines for Human-AI Interaction: https://www.microsoft.com/en-us/research/publication/guidelines-for-human-ai-interaction/
- OpenAI prompt engineering best practices: https://help.openai.com/en/articles/10032626-prompt-engineering-best-practices-for-chatgpt
- Schulhoff et al., “The Prompt Report: A Systematic Survey of Prompt Engineering Techniques”: https://arxiv.org/abs/2406.06608
Human-AI quality layer
Use HUMAN-AI-QUALITY-STANDARD.md for the current source-mapped quality bar covering sociotechnical framing, mental-model calibration, trust calibration, automation-bias resistance, human handoff, untrusted-content boundaries, output validation, accessibility, and failure visibility.
Important limitation
The assets improve structure, consistency, and evidence discipline. They do not guarantee correct output by themselves. A reviewer must still inspect the actual code, UI, tests, commands, and limitations.
Required research vocabulary cross-reference
This document participates in the same control vocabulary used across the library: CoT-safe public reasoning, Tree-of-Thoughts branch evaluation, ReAct observation loops, Least-to-most decomposition, Self-consistency checks, Self-refinement after failed checks, Process supervision, and traceability.