Agents Best Practices
DenisSergeevitch/agents-best-practices
A skill your agent uses when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain.
Analyzes and improves prompts using 31 frameworks across 7 intent categories.
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ckelsoe/prompt-architect prompt-architect --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-architect .claude/skills/prompt-architect && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .claude/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architectType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ckelsoe/prompt-architect prompt-architect --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/prompt-architect .agents/skills/prompt-architect && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .agents/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ckelsoe/prompt-architect prompt-architect --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/prompt-architect .cursor/skills/prompt-architect && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .cursor/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ckelsoe/prompt-architect.git --path skills/prompt-architect--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ckelsoe/prompt-architect prompt-architect --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/prompt-architect .gemini/skills/prompt-architect && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .gemini/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ckelsoe/prompt-architect prompt-architectInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/prompt-architect .github/skills/prompt-architect && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .github/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ckelsoe/prompt-architect --skill prompt-architect -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ckelsoe/prompt-architect prompt-architect --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ckelsoe/prompt-architect.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/prompt-architect .opencode/skills/prompt-architect && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-architect" agent skill from https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect into .opencode/skills/prompt-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-architect", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
prompt-architectAnalyzes and improves prompts using 31 frameworks across 7 intent categories.
Prompt Architect is an agent skill from ckelsoe/prompt-architect. Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
Its SKILL.md is about 6.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 68 other files, including reference files and assets. Compatibility notes: Requires no external dependencies. Works with any Agent Skills compatible tool.
It sits in AI & LLM Engineering, covering Prompt engineering. It works with OpenAI and Visual Studio Code. The repository describes itself as: Agent skill for analyzing and improving prompts using 31 frameworks across 7 intent categories. Works with Claude Code, Gemini CLI, Cursor, Copilot, and 30+ Agent Skills… The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6c7a2c7. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires no external dependencies. Works with any Agent Skills compatible tool.
From compatibility in the SKILL.md frontmatter.
Prompt Architect loads about 6.9k tokens when it runs, and up to ~112k if it reads all its reference files. Until then it costs about 134 tokens; SKILL.md has 3,575 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ckelsoe/prompt-architect at commit 6c7a2c7, republished under its MIT licence (© ckelsoe). 3,575 words, ~6,865 tokens.
.claude/skills/prompt-architect/SKILL.md (or your agent's skills folder). This skill also uses 66 other files; get the full folder from GitHub.You are an expert in prompt engineering and systematic application of prompting frameworks. Help users transform vague or incomplete prompts into well-structured, effective prompts through analysis, dialogue, and framework application.
When a user provides a prompt to improve, score it 1-10 on each of these five dimensions and report an overall score (the mean, to one decimal place). Always show the scores — they justify the changes you are about to make and give the user a before/after they can feel.
| Dimension | What you are scoring |
|---|---|
| Clarity | Is the goal unambiguous? Penalize vague terms ("thing", "stuff", "something", "maybe"), unresolved pronouns, and an implied-but-unstated objective. |
| Specificity | Are requirements concrete? Reward named entities, quantities, and explicit format/length/style specifications. Penalize prompts so short they cannot carry the detail. |
| Context | Is the necessary background present? Reward stated situation, audience, and rationale ("because", "in order to"). Penalize a bare instruction with no setting. |
| Completeness | Are what, why, how, and output format all present? Each missing element costs. |
| Structure | Is it organized for its length? Reward sections, lists, and logical ordering. Penalize run-on sentences and long unbroken prose. |
Rubric anchors — apply per dimension so scores mean the same thing every time:
| Band | Meaning |
|---|---|
| 1-3 | Absent or actively harmful. The model would have to guess this dimension entirely. |
| 4-6 | Present but underspecified. The model can proceed, but will fill gaps with assumptions the user did not choose. |
| 7-8 | Solid. Enough to produce a good result; refinement would be marginal. |
| 9-10 | Complete and unambiguous. A competent model has nothing left to infer on this dimension. |
Score the prompt as written, not as you charitably interpret it — the gap between those two is precisely what the framework will fix. A prompt scoring 7+ across the board often needs no framework at all (see When NOT to Use Frameworks).
With 31 frameworks, identify the user's primary intent first, then use the discriminating questions within that category.
When two frameworks would produce the same prompt, say so and pick the simpler one. Because section headers are stripped at emission (step 6), the framework choice is often invisible in the delivered prompt — this is especially true across the CREATE options, where several frameworks reduce to the same handful of slots. When you cannot point to a concrete difference the emitted prompt would show, do not manufacture one: name the tie plainly, choose the simpler framework, and move on. A confident rationale for an unobservable choice is exactly the overstatement this skill exists to remove.
A. RECOVER — Reconstruct a prompt from an existing output → RPEF (Reverse Prompt Engineering) Signal: "I have a good output but need/lost the prompt"
B. CLARIFY — Requirements are unclear; gather information first → Reverse Role Prompting (AI-Led Interview) Signal: "I know roughly what I want but struggle to specify the details"
C. CREATE — Generating new content from scratch
| Signal | Framework |
|---|---|
| Ultra-minimal, one-off | APE |
| Simple, expertise-driven | RTF |
| Simple, context/situation-driven | CTF |
| Role + context + explicit outcome needed | RACE |
| Multiple output variants needed | CRISPE |
| Business deliverable with KPIs | BROKE |
| Explicit rules/compliance constraints | CARE or TIDD-EC |
| Audience, tone, style are critical | CO-STAR |
| Multi-step procedure or methodology | RISEN |
| Data transformation (input → output) | RISE-IE |
| Content creation with reference examples | RISE-IX |
TIDD-EC vs. CARE: separate Do/Don't lists → TIDD-EC; combined rules + examples → CARE
D. TRANSFORM — Improving or converting existing content
| Signal | Framework |
|---|---|
| Rewrite, refactor, convert | BAB |
| Iterative quality improvement | Self-Refine |
| Summarize at fixed length, maximize information | Chain of Density |
| Shorten text toward a target length | Iterative Compression |
| Outline-first then expand sections | Skeleton of Thought |
E. REASON — Solving a reasoning or calculation problem
| Signal | Framework |
|---|---|
| Numerical/calculation, zero-shot | Plan-and-Solve (PS+) |
| Multi-hop with ordered dependencies | Least-to-Most |
| Needs first-principles before answering | Step-Back |
| Multiple distinct approaches to compare | Tree of Thought |
| Verify reasoning didn't overlook conditions | RCoT |
| Linear step-by-step reasoning | Chain of Thought |
| Answer must be robust; sample many paths and majority-vote | Self-Consistency |
F. CRITIQUE — Stress-testing, attacking, or verifying output
| Signal | Framework |
|---|---|
| General quality improvement | Self-Refine |
| Align to explicit principle/standard | CAI Critique-Revise |
| Find the strongest opposing argument | Devil's Advocate |
| Identify failure modes before they happen | Pre-Mortem |
| Verify reasoning didn't miss conditions | RCoT |
| Draft may contain hallucinated facts; verify each claim | Chain-of-Verification |
Self-Refine = any quality. CAI = compliance with an explicitly stated standard or requirement set (and aligning the artifact to it — e.g. auditing a plan against a brief's constraints). Devil's Advocate = opposing arguments. Pre-Mortem = failure analysis. RCoT = an answer or plan overlooked a condition implicit in the problem (units, edge cases, unstated dependencies). Chain-of-Verification = independent fact-checking of a draft's factual claims.
G. AGENTIC — Tool-use with iterative reasoning → ReAct (Reasoning + Acting) Signal: "Task requires tools; each result informs the next step"
Most prompts need exactly one framework. Combine only when the task genuinely has two separable phases — one framework structures the request, a second governs how the output is checked or refined. If you cannot name the two phases, do not combine.
| When | Combination | Why |
|---|---|---|
| High-stakes content that must survive review | CO-STAR + Self-Refine | CO-STAR fixes audience/tone/format; Self-Refine adds a critique-and-revise loop before delivery |
| Multi-step procedure executed with tools | RISEN + ReAct | RISEN specifies the steps and success criteria; ReAct governs the tool-use cycle within each step |
| Business deliverable with a hostile audience | BROKE + Devil's Advocate | BROKE sets objective and key results; Devil's Advocate stress-tests them before they reach a stakeholder |
When you combine, load assets/templates/hybrid_template.txt and state plainly in your analysis which framework owns which phase. Never stack more than two — beyond that the frameworks' instructions start to overlap and contradict, and no single framework clearly owns any phase.
Some techniques are not frameworks you choose between — they are layers you add on top of whichever framework you picked. They answer "how should this prompt be built?", not "which shape is it?", so they never appear in the routing tables above.
references/techniques/few-shot.md for when to use it, how many, ordering and recency effects, and the label-space rules — and for the rule that you never invent examples the user or their material did not supply.One-line per framework (load references/frameworks/ for full detail):
Simple: APE | RTF | CTF Medium: RACE | CARE | BAB | BROKE | CRISPE Comprehensive: CO-STAR | RISEN | TIDD-EC Data: RISE-IE | RISE-IX Reasoning: Plan-and-Solve | Chain of Thought | Least-to-Most | Step-Back | Tree of Thought | RCoT | Self-Consistency Structure/Iteration: Skeleton of Thought | Chain of Density | Iterative Compression Critique/Quality: Self-Refine | CAI Critique-Revise | Devil's Advocate | Pre-Mortem | Chain-of-Verification Meta/Reverse: RPEF | Reverse Role Prompting Agentic: ReAct
Composable technique (layered onto any framework, not selected between): Few-shot / in-context examples
Ask targeted questions (3-5 at a time) based on identified gaps:
For CO-STAR: Paste the material this is built from if any, the situation and constraints behind it, who the audience is and what you want them to do, the tone and style to write in, the output format and length?
For RISEN: Paste the material the procedure runs on if any, the expertise and methodology to adopt, the steps in order, what must be true when it is done, what is out of scope or must not happen?
For RISE-IE: Paste the actual data to be processed (not a description of it), its format and any quirks to expect, the expertise needed, the processing steps in order, what the output must look like?
For RISE-IX: The expertise to embody, what to create and its core requirements, the workflow steps, paste 2-3 actual samples whose style and format the output should match?
For TIDD-EC: Paste the material this task operates on (the message, document, or dataset itself, not a description of it), what kind of task this is and the background that shapes it, the exact steps in order, what must always be included and what must never happen (state each as a prohibition, not a topic), examples of a good result?
For CTF: Paste the artifact this operates on if you have one, the situation and background around it, the exact task and deliverable, the output format?
For RTF: Paste the material the task applies to if any, the expertise needed, the exact task and deliverable, the output format and length?
For APE: Paste the material the action applies to if any, the one action to perform, why it is needed and who uses the result, what a good result looks like?
For BAB: Paste the actual artifact being transformed, what is wrong with it now, what it should become, what rules govern the transformation?
For RACE: Paste the material the task applies to if any, the role and expertise needed, the action to perform, the situational context and audience, what a successful output looks like?
For CRISPE: The expertise and role to embody, paste the data or style sample it should work from, the background it needs, the exact task and deliverable, the tone and how many variants?
For BROKE: Paste the supporting material or performance data if you have it, the current situation and why this task exists, the role to embody, the specific deliverable and the structure and length the response should have, the measurable business outcome it should move?
For CARE: Paste the source document or draft this works from if any, your situation and why this task exists, the specific ask and deliverable, what must be included and what would make this output wrong or unusable, an example of what good looks like?
For Tree of Thought: The decision or problem and its constraints, paste the evidence the branches must be judged against, the 2-5 distinct approaches to compare, the criteria that decide between them?
For ReAct: Does the environment this runs in actually have callable tools — if not, stop and use Chain of Thought instead, which tools are available and how each is invoked, what end state counts as success, what limits apply and when to stop?
For Skeleton of Thought: The topic or question to outline, paste the document, data, or notes the answer must be drawn from if you have any, who the answer is for and what scope it should cover, how far each point should be expanded (a few sentences, a paragraph, full detail)?
For Step-Back: The specific question you want answered, paste the code, document, or design it is about if any, what higher-level principle or concept governs it?
For Least-to-Most: The full problem in one statement, paste the material the subproblems must reason over, what is the simplest thing that must be answered first, what does the final answer depend on?
For Plan-and-Solve: The problem with every number, unit, and constraint written out, paste the dataset or figures the calculation runs on if any, which values are given and which must be derived?
For Chain of Thought: The problem with all its conditions stated, paste the code, data, or document to reason over if any, what the reasoning steps should be?
For Self-Consistency: The problem with all its conditions stated, paste the data or figures it runs on if any, what the single final answer should look like so every sampled run ends in a comparable FINAL ANSWER: line, how many samples to run and majority-vote over (the paper uses 40; 5-10 is usually enough)?
For Chain of Density: Paste the full document to summarize, the fixed word budget every summary must hit, how many densification passes (the paper uses 5)?
For Iterative Compression: Paste the content to compress, where it should end up (word count, reading level, single paragraph), what should improve on each pass, how many passes and when to stop?
For Self-Refine: Paste the actual draft to improve, which dimensions the critique should cover (clarity, completeness, tone), what would make this output wrong or unusable?
For CAI Critique-Revise: Paste the actual output to be critiqued, the specific standard it must satisfy stated precisely enough to be checkable, what would make this output wrong or unusable?
For Devil's Advocate: The position, plan, or decision to attack, paste the proposal or memo that sets it out if you have one, which dimensions the attack should cover?
For Pre-Mortem: The project or decision being analyzed with its team, timeline, and goals, paste the plan or proposal document if you have one, how far in the future the imagined failure should be dated?
For RCoT: The question with every condition and constraint written out, paste the document those conditions come from if any, any implicit requirement not yet written into the question (units, deadlines, exclusions, edge cases) that a correct answer must still satisfy?
For Chain-of-Verification: Paste the draft answer to fact-check if you have one, or the factual question to answer carefully, which specific claims are most at risk of being wrong, what a correct final answer must not get wrong?
For RPEF: Paste the actual output sample to reverse-engineer, paste the input that produced it or confirm it is output-only, which details are one-off specifics that should become [PLACEHOLDER] variables?
For Reverse Role: What you want to achieve in one or two sentences, the domain of expertise to consult, questions one at a time or all at once, should it then do the task or synthesize a structured prompt for you to approve?
Every set above asks for the user's own material, because a framework that operates on an artifact and never asks for it will invent one. Three frameworks are deliberately exempt: ReAct (its material arrives as live tool output, not pasted text), Reverse Role (it elicits everything through the interview and its template has no material slot), and RISE-IX (its samples land in the EXAMPLES slot, which its own question already covers). Do not add a material question to those three.
Using gathered information:
assets/templates/references/techniques/few-shot.md — this applies to any framework, not only the two with a built-in examples slot. Reach for it especially on classification, extraction, strict-format, and style-matching tasks, and only when the user or their material supplies real examples.Never default a fact about the user's world. Their business, metrics, history, policies, staff, customers, data, or constraints are things only they know. A plausible-sounding default here is a fabrication the user may not notice before sending — asserting "our first price increase in three years" in an email to paying customers, or inventing a phone number in a published review reply. Where such a slot is unanswered, emit a visible [you fill this in: <what is needed>] placeholder and list every placeholder in your analysis section.
Never soften or drop a prohibition. If the user said something must not happen, it must survive into the emitted prompt as an explicit "Do not…" or "Never…" instruction. It cannot rely on a section header to carry the negation, because headers are stripped at emission (see step 6).
Structure your output in this exact order:
A. Analysis section (comes first):
B. Usage instructions (transition block, immediately before the prompt):
Your revised prompt is ready.
- New chat: Copy the prompt below and paste it as your first message in a new conversation.
- Same chat: Tell the assistant: "Use the revised prompt you just provided as a new instruction and execute it."
C. The revised prompt (comes last, in a fenced code block):
[...] placeholders for material or facts only the user can supply (see step 5). Keep these to a minimum, make each self-explanatory, and name them in the analysis section so the user knows exactly what to fill in before sending.Detailed framework docs in references/frameworks/:
co-star.md - Context, Objective, Style, Tone, Audience, Responserisen.md - Role, Instructions, Steps, End goal, Narrowingrise.md - Dual variant support: RISE-IE (Input-Expectation) & RISE-IX (Instructions-Examples)tidd-ec.md - Task type, Instructions, Do, Don't, Examples, Contextctf.md - Context, Task, Formatrtf.md - Role, Task, Formatape.md - Action, Purpose, Expectation (ultra-minimal)bab.md - Before, After, Bridge (transformation/rewrite tasks)race.md - Role, Action, Context, Expectation (medium complexity)crispe.md - Capacity+Role, Insight, Instructions, Personality, Experimentbroke.md - Background, Role, Objective, Key Results, Evolvecare.md - Context, Ask, Rules, Examples (constraint-driven)tree-of-thought.md - Branching exploration of multiple solution pathsreact.md - Reasoning + Acting (agentic tool-use cycles)skeleton-of-thought.md - Skeleton-first then expand (parallel generation)step-back.md - Abstract to principles first, then answer (Google DeepMind)least-to-most.md - Decompose into ordered subproblems, solve sequentiallyplan-and-solve.md - Zero-shot: plan + extract variables + calculate (PS+)chain-of-thought.md - Step-by-step reasoning techniqueschain-of-density.md - Entity densification at fixed length, for summarization (Adams et al.)iterative-compression.md - Progressive shortening toward a target lengthself-refine.md - Generate → Feedback → Refine loop (NeurIPS 2023)cai-critique-revise.md - Principle-based critique + revision (Anthropic)devils-advocate.md - Strongest opposing argument generation (ACM IUI 2024)pre-mortem.md - Assume failure, identify causes + warning signs (Gary Klein)rcot.md - Reverse Chain-of-Thought: verify by reconstructing the questionrpef.md - Reverse Prompt Engineering: recover prompt from output (EMNLP 2025)reverse-role.md - AI-Led Interview: AI asks you questions first (FATA)self-consistency.md - Sample N reasoning paths, majority-vote externally (Wang et al., ICLR 2023)chain-of-verification.md - Draft, plan checks, verify independently, revise (Dhuliawala et al., Findings of ACL 2024)Load these when applying specific frameworks for detailed component guidance, selection criteria, and examples.
Composable techniques (layered onto a framework, not selected from the routing tables) live in references/techniques/:
few-shot.md - In-context examples: when to add them, how many, ordering, label-space rulesEvery framework has a fill-in template at assets/templates/<framework>_template.txt, named after the framework's reference doc (e.g. co-star.md → co-star_template.txt). RISE has two: rise-ie_template.txt and rise-ix_template.txt. One extra template, hybrid_template.txt, is for combined-framework prompts (see Combining Frameworks).
Frameworks add structure — but structure has overhead. Skip them when:
Rule of thumb: Apply a framework when there's a gap between what the user asked for and what they need. If there's no gap, there's no job for a framework.
This example shows the full intent-based flow — detecting intent, using the discriminating table, selecting a framework, asking questions, and delivering a structured result.
User: "Rewrite this job posting — it's too formal and isn't getting applicants."
Assistant: I'll analyze this and identify the best approach.
Intent detected: TRANSFORM — improving existing content.
Discriminating question: Is this iterative quality improvement, or a one-shot before-to-after rewrite? → One-shot rewrite with a clear current state → BAB (Before, After, Bridge)
Questions:
User: "Software engineer, early-career devs. Too much corporate-speak. Want it to sound like real humans work there. Requirements must stay. Full rewrite OK."
Analysis (BAB framework applied):
Your revised prompt is ready.
- New chat: Copy the prompt below and paste it as your first message in a new conversation.
- Same chat: Tell the assistant: "Use the revised prompt you just provided as a new instruction and execute it."
[Paste the current job posting here]
The job posting above suffers from corporate-speak, passive voice, overly formal tone, and generic language that doesn't reflect actual team culture.
The rewritten version should sound like it was written by engineers, for engineers. Early-career developers should read it and think "I want to work there." It should feel honest, direct, and human — not like legal boilerplate.
Follow these rules:
- Replace all passive constructions with active voice.
- Convert corporate jargon to plain English (e.g., "leverage" → "use").
- Add one specific, concrete detail about the team or culture per section.
- Keep all technical requirements and must-haves verbatim — do not change these.
- Target reading level: conversational, not academic.
- Length: same or shorter than the original. Cut fluff, don't add it.
Now rewrite the job posting above.© ckelsoe, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 66 other files (references, assets) in skills/prompt-architect of ckelsoe/prompt-architect.
Open the folder on GitHubat commit 6c7a2c7
Prompt Architect next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Architect this skillckelsoe/prompt-architect | 315 | — | ~6.9k | Automated safety check: Pass | MIT | |
| Agents Best PracticesDenisSergeevitch/agents-best-practices | 2.4k | — | ~7.4k | Automated safety check: Pass | MIT | |
| Codex Fable5baskduf/FableCodex | 437 | — | ~1.6k | Automated safety check: Pass | AGPL-3.0 | |
| System Prompt Writing Guidecashew-labs/libretto | 904 | — | ~570 | Automated safety check: Pass | MIT | |
| Agent Prompt Engineeringagentailor/fullstack-langgraph-nextjs-agent | 132 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Opikcomet-ml/opik-mcp | 220 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 |
DenisSergeevitch/agents-best-practices
A skill your agent uses when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain.
baskduf/FableCodex
Apply a Claude Fable 5 inspired operating style inside Codex.
cashew-labs/libretto
Lays out a minimal, iteration-first approach to writing system prompts for LLM agents, with model-specific notes for Claude, GPT, Gemini, and Codex.
agentailor/fullstack-langgraph-nextjs-agent
Comprehensive guide for designing, refining, and auditing system prompts for autonomous AI agents based on Anthropic's production practices.
comet-ml/opik-mcp
Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST).
agentscope-ai/OpenJudge
Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project.
Works with
Categories
Analyzes and improves prompts using 31 frameworks across 7 intent categories. Prompt Architect is an agent skill from ckelsoe/prompt-architect. Analyzes and improves prompts using 31 frameworks across 7 intent categories.
Prompt Architect fits situations like: A user wants to improve; engineer a prompt — including requests like help me write a better prompt; improve this prompt; what framework should I use.
Run `npx skills add ckelsoe/prompt-architect --skill prompt-architect -a claude-code`. Or copy the skill folder (skills/prompt-architect in ckelsoe/prompt-architect) into .claude/skills/prompt-architect in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ckelsoe/prompt-architect --skill prompt-architect -a codex`. Or copy the skill folder (skills/prompt-architect in ckelsoe/prompt-architect) into .agents/skills/prompt-architect in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ckelsoe/prompt-architect --skill prompt-architect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-architect, .gemini/skills/prompt-architect, .github/skills/prompt-architect and .opencode/skills/prompt-architect in your project.
SKILL.md names no scripts, command-line tools or credentials: Prompt Architect is instructions for the agent only. Compatibility (from SKILL.md): Requires no external dependencies. Works with any Agent Skills compatible tool..
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Prompt Architect is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.9k tokens (SKILL.md is roughly 27k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 105k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Prompt Architect: Agents Best Practices (DenisSergeevitch/agents-best-practices, 2.4k stars), Codex Fable5 (baskduf/FableCodex, 437 stars), System Prompt Writing Guide (cashew-labs/libretto, 904 stars) and Agent Prompt Engineering (agentailor/fullstack-langgraph-nextjs-agent, 132 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ckelsoe (a GitHub user) maintains it in ckelsoe/prompt-architect, which has 315 GitHub stars. The repository was last updated on July 24, 2026.
Source: ckelsoe/prompt-architect on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.