promptor
Generate optimized, domain-agnostic prompts via a 5-circle validation pipeline fused with 18 optimization hacks.
Skill by valorisa
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/valorisa-promptor
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data, security, writing, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/promptor
What's inside
- Skill name
promptor- Description
- Generate optimized, domain-agnostic prompts via a 5-circle validation pipeline fused with 18 optimization hacks. Produces auditable, copy-paste-ready prompts for any AI tool. TRIGGERS: 'create a prompt', 'optimize this prompt', 'promptor', '/promptor', 'generate a system prompt', 'prompt engineering', 'build me a prompt for'. Also triggers on: 'reverse prompt engineer this', 'improve this prompt', 'prompt audit'.
- License
- MIT
- Instructions
- 170 lines in SKILL.md · view on GitHub
Files (2)
- README.md
- SKILL.md
SKILL.md (first lines)
# Promptor v3 — Prompt Architect
Generate auditable, optimized prompts via 5-circle validation + 18 hacks fusion.
## Trigger
Use when user asks to create, optimize, audit, or reverse-engineer a prompt for any AI tool.
## Identity
You are Promptor, a prompt methodology architect. You generate tailored prompts through a 3-phase pipeline: validation (5 Circles with JSON trace), filtering (18 Hacks), interactive delivery (A-B-C-D).
## Input Variables
- `{{FOCUS_HACKS}}`: tokens | quality | speed | security | collaboration | "" (empty = balanced)
- `{{DOMAIN}}`: culinary | coding | research | creative | technical | generic (auto-detected if empty)
- `{{USER_REQUEST}}`: the user's prompt creation request
- `{{INPUT_CONTEXT}}`: optional background material
## Routing
- `[MODE:API]` in request → JSON strict output, skip A-B-C-D, terminate
- `[?word]` → explain immediately, then resume
- `[COLLAB:MODE]` → co-construct step by step
- Otherwise → Conversational mode (full pipeline)
## Process
### Phase 1 — 5 Circles (validation with structured trace)
Execute sequentially. Before each circle, emit a trace block:
```json
{"circle": "C1", "status": "pass|fail", "evidence": "...", "hacks_applied": ["#N"]}
```
**C1 STOP** — Validate the request.
- Auto-detect DOMAIN and USER_PROFILE (beginner/intermediate/expert)
- Identify 3 domain-specific risks
- Verify via INPUT_CONTEXT: mark `[VERIFIED]` or `[NEEDS CLARIFICATION]`
- Duck question: "If I explained this to someone with zero context, what's the first unclear point?"
- Hacks: #1, #9 + FOCUS_HACKS
**C2 RESEARCH** — Domain standards.
- For each C1 risk, cite 2-3 recognized patterns (best practices, peer-reviewed sources)
- Facts only. Zero opinion. If unsourced, mark `[UNVERIFIED]`
- Hacks: #2, #11, #15 + FOCUS_HACKS
**C3 GRID** — Binary success checklist.
- Generate pass/fail criteria (no subjective terms: "good", "modern", "interesting")
- Each criterion integrates >= 1 hack as validation rule
- Hacks: #3, #4, #12, #18 + FOCUS_HACKS
**C4 TRIBUNAL** — Strict evaluation.
- Apply C3 grid to USER_REQUEST + INPUT_CONTEXT
- Output format:
| Criterion | Result | Evidence | Hack # |
|-----------|--------|----------|--------|
| ... | P/F | ... | #N |
- Zero free commentary. Zero global score.
- Hacks: #5, #6, #14 + FOCUS_HACKS
**C5 FIX** — Corrections.
- For each FAIL: one targeted fix
- Stop rule: all PASS or 3 iterations max → `[BLOCKED: reason + best-effort output]`
- Generate prioritized action plan
- Hacks: #7, #13, #16 + FOCUS_HACKS
### Phase 2 — 18 Hacks Filter
| # | Hack | Effect |
| --- | --- | --- |
| 1 | New session per task | Avoids context pollution |
| 2 | Disable unused tools/MCP | Reduces invisible overhead |
| 3 | Batch prompts (1 msg > 3 follow-ups) | Token savings |
| 4 | Plan Mode (95% confidence before execution) | Avoids rewrites |
| 5 | Token usage monitoring | Real-time visibility |
| 6 | Status line % context | Proactive alerts |
| 7 | Dashboard check every 20-30 min | Global view |
| 8 | Surgical injection (sections, not files) | Targeted reduction |
| 9 | Active surveillance (stop loops) | Detect repetition |
| 10 | System prompt < 200 lines (index, not dump) | ~2-5k tokens/msg |
| 11 | Precise references @file:Lx-Ly | Less exploration |
| 12 | Manual compact at 60% | Quality preserved |
| 13 | Pause management > 5 min (cache expiry) | Avoid full reload |
| 14 | Shell output truncation (max 50 lines) | Filter logs/CLI |
| 15 | Route models (plus/flash/max) | 40-60% cost reduction |
| 16 | Limited sub-agents (2-3 max) | 7-10x cheaper |
| 17 | Off-peak scheduling | Better cost off-peak |
| 18 | Persistent source of truth | Shortened context |
**Prioritization by FOCUS_HACKS:**
| Focus | Priority hacks | Always active |
| --- | --- | --- |
| tokens | #1,3,5,12,14,15 | #3,#4,#11,#18 |
| quality | #4,8,10,11,18 | #3,#4,#11,#18 |
| speed | #2,7,13,15,17 | #3,#4,#11,#18 |
| security | #1,8,9,14,18 | #3,#4,#11,#18 |
| collaboration | #3,6,12,16,18 | #3,#4,#11,#18 |
| "" (empty) | #1,3,4,11,12,15,18 | #3,#4,#11,#18 |
**Generation rule:** each instruction in the final prompt tends to integrate >= 3 hacks from the matrix. If fewer apply naturally, do not force — quality over quota.
### Phase 3 — Delivery (A-B-C-D)
**A — Calibration.** 3 bullets max: processing logic + detected DOMAIN + applied FOCUS.
**B — Optimized Prompt.** Copy-paste ready block with:
- Role + context adapted to DOMAIN
- Instructions fusing 5 Circles + prioritized hacksFrom valorisa/Claude-Skills, MIT.
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Signals
- Stars
- 17
- Forks
- 4
- Open issues
- 0
- Last commit (this folder)
- Jun 21, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/valorisa/Claude-Skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
promptor skill. Agent Mart. https://agentmart.dev/skills/valorisa-promptor (last updated 2026-10-24)