prompt-enhancement
Skill4 starsChanged 5 months agoMITView on GitHub
Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Prefer testable instructions over personality-heavy wording.
- Distinctive: line 19
Add output format only when structure improves usability.
- Distinctive: line 20
Add examples sparingly; use examples that demonstrate edge cases, not just happy paths.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Prompt Enhancement
Operating Rules
- Preserve the user's intent; do not over-constrain creative or exploratory tasks.
- Separate objective, context, inputs, constraints, output format, and verification criteria.
- Make implicit assumptions explicit when they affect outcome quality.
- Prefer testable instructions over personality-heavy wording.
Workflow
- Diagnose the prompt for missing context, ambiguous goals, conflicting constraints, and weak success criteria.
- Rewrite into a direct task with ordered steps and clear boundaries.
- Add output format only when structure improves usability.
- Add examples sparingly; use examples that demonstrate edge cases, not just happy paths.
- Include a short rationale when the user needs to understand tradeoffs.
Prompt Template
Objective: <what outcome is required>
Context: <facts, files, audience, constraints>
Inputs: <data the model should use>
Process: <important reasoning or execution steps>
Output: <format, length, tone, artifacts>
Validation: <how to check correctness>
Boundaries: <what to avoid or ask before doing>Other versions3 projects use a version of this.
Word for word · 1
- Garyson26/Trident-SecOps-Skills★ 1100% same
Edited · 0
None found.
Wrote their own version · 2
- jodykwong/Prompt-Enhancement★ 2same name
- viasrijan/crazy-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository45 other files it gives its agents.
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- gemini-tool-orchestratorSkill
- go-programmingSkill
- kubernetes-securitySkill
- malware-reverse-engineeringSkill
- multilingualSkill
- offensive-securitySkill
- prompt-enhancementSkill
- python-programmingSkill
- ai-redteamSkill
- api-security-automationSkill
- assembly-programmingSkill
- bug-bounty-workflowSkill
- claude-mythos-emulationSkill
- cloud-security-automationSkill
- cybersecurity-partnerSkill
- detection-engineeringSkill
- devsecopsSkill
- exploit-developmentSkill
- forensics-triageSkill
- gemini-tool-orchestratorSkill
- go-programmingSkill
- kubernetes-securitySkill
- malware-reverse-engineeringSkill
- multilingualSkill
- offensive-securitySkill
- osint-recon-automationSkill
- purple-team-automationSkill
- python-programmingSkill
- smart-contract-auditSkill
- soc-operationsSkill
- threat-intel-fusionSkill
- smart-contract-auditSkill
- soc-operationsSkill
- threat-intel-fusionSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.