PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsWrote 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/rules/mohitagw15856/pm-claude-skills/prompt-debugging)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/prompt-debugging"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/prompt-debugging/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/rules/mohitagw15856/pm-claude-skills/prompt-debugging"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/prompt-debugging.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 | $0.00127 | $0.01016 |
| Opus 5 | $0.00063 | $0.00508 |
| Sonnet 5 | $0.00025 | $0.00203 |
| Haiku 4.5 | $0.00013 | $0.00102 |
Grade A, and why
prompt-debugging 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 5d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Debugging
When a prompt misbehaves, most people randomly reword it until something sticks — slow, and it doesn't teach you anything. Prompts fail in diagnosable ways: ambiguity, missing context, a format the model can't follow, or instructions that contradict each other. This finds the actual failure, applies the targeted fix, and names the principle — so you fix it once and stop hitting the same wall.
What This Skill Produces
- A diagnosis — the specific failure mode: ambiguous ask, missing context, unspecified output format, conflicting instructions, buried key instruction, or too much at once
- The targeted fix — the change that addresses that failure, not a superstitious reword
- A corrected prompt — rewritten to fix the diagnosed problem, with the change explained
- A generalization check — testing that the fix works across cases, not just the one example (the trap of overfitting to a single output)
- The principle — the underlying rule (be specific, show the format, resolve conflicts, front-load the key instruction) so you recognize it next time
- When it's the model, not the prompt — the honest call when the task is beyond what prompting fixes
Required Inputs
Ask for these if not provided:
- The prompt — the actual text that's misbehaving
- What it's doing wrong — ignoring an instruction, wrong format, inconsistent, off-topic
- What you want — the correct output, ideally with an example
- The pattern — does it fail always or sometimes (points at ambiguity vs. a hard miss)
Framework: Diagnose Before You Reword
- Name the failure mode. Match the symptom to a cause — ignored instructions often mean it's buried or conflicting; inconsistent output usually means ambiguity; wrong shape means the format wasn't specified.
- Fix that cause specifically. Ambiguous → add specificity; missing context → add it; no format → show the exact format; conflict → resolve it; buried → move the key instruction up front.
- Show, don't just tell. For format and quality problems, an example of the desired output fixes more than paragraphs of description.
- Check it generalizes. Re-test on several cases — a fix that only works on your one example is overfitting, not a fix.
- Extract the principle. Name the rule behind the fix so the next prompt starts right.
- Know when to stop. If the task genuinely exceeds the model or needs tools/context it can't have, say so instead of endless rewording.
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.
- 5d ago First seen · 70 lines · 127 tokens per session scan A 187691f98197
prompt-debugging is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,341 stars, last pushed yesterday), licensed MIT. It adds 127 tokens to every session and 1,016 once invoked, about $0.0006 per session on Opus 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 2026-09-03.
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