agentera/agently-prompt-management
legacy/v1/skills/agently-prompt-management/SKILL.md
Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
$ install --global
skillsauthnpx skillsauth add agentera/agently-skills agently-prompt-managementInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
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Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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CrowdStrikeAdvanced threat intelligence
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OWASP Dep-Check
50%
Last scanned: May 17, 2026, 3:55 AM260.9s2 files scanned
SKILL.md
- name:
- agently-prompt-management
- description:
- Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
Agently Prompt Management
Use this skill when the core problem is how prompt state should be structured before one request or request family runs.
Native-First Rules
- prefer
input(...),instruct(...),info(...), andoutput(...)over concatenated prompt strings - move reusable prompt structure into prompt config or YAML instead of ad hoc literals
- keep runtime variables as
${...}placeholders in prompt files and inject them through mappings at load time - keep task-specific request contracts in prompt config, and keep only widely reused persona setup in small code-side factories
- when the output contract is stable and shared across a request family, keep it in prompt config such as
.request.outputinstead of rebuilding it ad hoc in Python - keep prompt composition separate from transport and orchestration
- use config files as an editable bridge when UI or product teams need to adjust prompt-driven behavior without rewriting workflow code
Anti-Patterns
- do not flatten business context into one opaque string unless the task is trivial
- do not rebuild prompt templates through ad hoc
.format(...)or string concatenation when prompt mappings already fit - do not scatter stable prompt or output contracts across multiple Python helpers when one prompt config can own them
- do not use prompt config files as a substitute for workflow state
Read Next
references/overview.md
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