skill fire debugger
by Shippers
Instantly diagnose and fix why your AI agent skills aren't triggering when they should.
New: Skill bounties are live. Post a request, fund the bounty, and creators compete for 7 days to build it -> See open bounties
THE AGENSI STORE
8 skills found
by Shippers
Instantly diagnose and fix why your AI agent skills aren't triggering when they should.
Lint your AGENTS.md (or CLAUDE.md and .cursorrules) for the problems that make a coding agent misbehave. Flags contradictory rules, references to files and commands that no longer exist, overly broad or unsafe instructions, missing sections (build, test, run, conventions), duplicate rules, and the case where you have competing rule files that should be consolidated into one AGENTS.md.
Turn a raw meeting transcript (.vtt, .srt, .txt, or pasted) into clean markdown meeting minutes: a task table with owners and due dates, the decisions actually made, open questions, and a skim-friendly summary. Separates real commitments from the 'we should probably' suggestions.
Lint the function-calling tool definitions your agent exposes. Flags tools with no description, parameters missing a description or a type, overlapping or near-duplicate tools, too many tools for reliable selection, an unsafe tool exposed without a guard, required parameters missing from the schema, and free-form parameters that should be bounded with an enum. Cleaner tool schemas mean an agent that picks the right tool.
Generate an llms.txt for your site and validate an existing one against the spec. The generator turns your sitemap.xml or docs folder into a clean, sectioned llms.txt with one-line descriptions. The validator flags a missing H1 title, a missing summary blockquote, malformed link entries, links with no description, relative URLs that should be absolute, and a referenced llms-full.txt that is not present.
'Should work now' is the most expensive sentence in software — it means the change was reasoned about, not observed, and the next person to touch this code will pay for that assumption
by Nex AI
A 3-step Dutch payment-reminder cadence for an overdue invoice: a friendly nudge, a firm follow-up, and a calm final notice, with a server-side compliance footer and a stop-on-payment rule. Polite where it should be, firm where it must be, never aggressive. Composes on the Nex invoice-bot pattern.
Classify a consequential people-facing action as suitable for automation, revision, delay, human review, human delivery, or no automation.