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.
npx skills add aakashg/pm-claude-skills --skill prompt-engineergit clone --depth 1 https://github.com/aakashg/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/skills/aakashg/pm-claude-skills/prompt-engineer)<a href="https://agentmods.dev/skills/aakashg/pm-claude-skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/aakashg/pm-claude-skills/prompt-engineer/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/skills/aakashg/pm-claude-skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/aakashg/pm-claude-skills/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 68 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00055 | $0.02048 |
| Opus 5.5 | $0.00022 | $0.00819 |
| Sonnet 5.5 | $0.00011 | $0.00410 |
| Haiku 4.5 | $0.00006 | $0.00205 |
Grade A, and why
prompt-engineer 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Diagnose a prompt, rewrite it, and show exactly what changed and why.
Step 0 — Read first
| Source | Path | What to extract |
|---|---|---|
| The prompt | whatever the user pasted | Actual wording — never paraphrase before diagnosing |
| Failing output | the output they got, if provided | The failure mode; this determines the fix |
| Project context | CLAUDE.md |
Audience, product, banned words, output preferences |
| Technique reference | references/techniques.md |
Full before/after examples for each technique |
If the user pasted a prompt but no failing output, ask for one example of what it produced. Diagnosing from the prompt alone guesses at the failure mode.
Constraints
Mandatory.
- Always show before and after. The user must see the diff, not just the result.
- Explain every change by the problem it solves, not the technique name alone.
- Preserve the user's intent. Improve how they ask, never what they are asking for.
- Right-size the fix. A 10-line prompt that works beats a 50-line prompt that confuses.
- Never add chain-of-thought to a simple generative task like "write a tweet."
- Never write "be thorough and comprehensive." Name exactly what to cover.
- Never add a role that does not match the task.
- Never add a few-shot example below the quality bar you expect back. Bad examples teach bad patterns.
- If the prompt is longer than its expected output on an analytical task, it is too long. Cut it.
Existence check
Before rewriting, verify:
- The prompt itself — the literal text, not a description of it.
- The goal — what the user wants the output to do or be used for.
- The failure — what the current output gets wrong, ideally with a sample.
If two of three are missing, do not rewrite. Ask for exactly those. Rewriting a prompt without knowing how it fails produces a longer prompt, not a better one.
Step 1 — Diagnose
Score the prompt across these dimensions. Name which ones fail.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 190 lines · 55 tokens per session scan A 5d23903cffff
prompt-engineer is a skill published in the GitHub repository aakashg/pm-claude-skills (114 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 2,048 once invoked, about $0.0002 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 2026-08-30.
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