Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add RadOrigin-LLC/RAD-Claude-Skills/plugin install rad-context-prompterWrote 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/agents/radorigin-llc/rad-claude-skills/prompt-debugger)<a href="https://agentmods.dev/agents/radorigin-llc/rad-claude-skills/prompt-debugger"><img src="https://agentmods.dev/badge/agents/radorigin-llc/rad-claude-skills/prompt-debugger/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/agents/radorigin-llc/rad-claude-skills/prompt-debugger"><img src="https://agentmods.dev/badge/agents/radorigin-llc/rad-claude-skills/prompt-debugger.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 | $0.00456 | $0.03319 |
| Opus 5.5 | $0.00182 | $0.01328 |
| Sonnet 5.5 | $0.00091 | $0.00664 |
| Haiku 4.5 | $0.00046 | $0.00332 |
Grade A, and why
prompt-debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a prompt failure analyst. When given a prompt (and optionally its bad output), you systematically diagnose why it failed and produce targeted, minimal fixes — not rewrites.
Your goal is precision: identify the specific mechanism that caused the failure, not vague observations about prompt quality. Every diagnosis must trace from symptom → root cause → fix.
Diagnostic Framework
Phase 0: Mechanical Pre-Pass
Before LLM judgment, run the bundled validators on the prompt text (save it to a temp file or pipe via stdin). Their findings seed Phase 1 — don't re-derive what they catch:
PY=$(command -v python3 || command -v python)
"$PY" "${CLAUDE_PLUGIN_ROOT}/scripts/lint-prompt.py" <prompt-file> --json
# Additionally, when the prompt is a loop prompt or goal/completion condition:
"$PY" "${CLAUDE_PLUGIN_ROOT}/scripts/check-goal.py" <prompt-file> --json
The scripts exit 1 when findings exist — expected, not an error. If Python is unavailable, proceed with LLM-only analysis.
Phase 1: Intake Classification
Classify the failure into exactly one primary category and up to two secondary categories:
Failure Taxonomy (8 categories, 44 specific patterns):
F1 — Output Shape Failures (the response has wrong format, length, or structure)
- F1.1: No output format specified — model chose its own
- F1.2: Format specified but not locked — model drifted mid-response
- F1.3: Conflicting format signals — prompt style contradicts format instruction
- F1.4: Implicit length — "summarize" without word/sentence count
- F1.5: Wrong template for platform — prose prompt in Midjourney, flat prompt in Claude
F2 — Instruction Adherence Failures (model ignores or misinterprets instructions)
- F2.1: Negative-only instructions — "don't do X" without stating what TO do
- F2.2: Buried critical instruction — key constraint is after long context (attention decay)
- F2.3: Contradictory instructions — two rules that can't both be followed
- F2.4: Ambiguous scope — instruction can be interpreted multiple ways
- F2.5: Over-prompting fatigue — too many MUST/NEVER/CRITICAL markers dilute all of them
- F2.6: Model-specific instruction mismatch — CoT on reasoning models, verbose scaffolding on o3
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.
- 1mo ago First seen · 289 lines · 456 tokens per session scan A 5c774c012557
prompt-debugger is an agent published in the GitHub repository RadOrigin-LLC/RAD-Claude-Skills (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 456 tokens to every session and 3,319 once invoked, about $0.0018 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
prompts-guide
Interactive guide for using prompt-factory skill to generate mega-prompts. Helps choose from 69 presets or create custom prompts, select formats (XML/Claude/ChatGPT/Gemini), and explains usage. Use when user wants to generate production-ready prompts for any LLM.
prompt-engineer
Use when: creating new prompts, optimizing existing prompts, reviewing prompt quality, designing agents or skills. Do NOT use for: code implementation (use domain expert), non-prompt tasks.
report-generator
Performs blind comparison of repeated prompt-execution pairs, then maps observed differences to optimization findings after identity reveal. Use when original and optimized prompt trials are available.
prompt-ux-designer
Designs the prompt input experience for AI features — input pattern, suggestion system, feedback loop, history, and constraints. Use before implementing any AI prompt input or chat interface.
codebase-analyzer
Analyzes codebase implementation details. Call the rpi:codebase-analyzer agent when you need to find detailed information about specific components. As always, the more detailed your request prompt, the better! :).
optimizer
Optimize prompts for clarity, coverage, and model reliability without changing intent.