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Agent skills
developer-tools

Topic: developer-tools

13,276 skills in this topic.

rlm-mode Detect requests for recursive decomposition and large-scale operations that benefit from RLM processing
jmagly/aiwg 107
rlm-batch
jmagly/aiwg 107
fanout
jmagly/aiwg 107
chunk
jmagly/aiwg 107
prose-validate Validate an OpenProse program file against Prose contract grammar without executing it — checks frontmatter, contract structure, service references, and strategy syntax
jmagly/aiwg 107
prose-setup Clone or update the OpenProse repository to ensure AIWG prose tools hook into the latest version of the specification and examples
jmagly/aiwg 107
prose-run
jmagly/aiwg 107
prose-reader Read and parse an OpenProse program file, extracting its contract (requires, ensures, strategies, errors, invariants) and services into a structured representation
jmagly/aiwg 107
prose-install Install OpenProse for AIWG use when no existing installation is found. Tries npx skills add first, falls back to git clone, then saves the resolved path to .aiwg/config.json.
jmagly/aiwg 107
prose-detect Locate an existing OpenProse installation using a prioritized signal chain — env var, AIWG config, AIWG-local install, project plugin manifest, user home directory, or global CLI. Returns the resolved PROSE_ROOT path. Does not install OpenProse; triggers prose-setup if no installation is found.
jmagly/aiwg 107
forme-manifest
jmagly/aiwg 107
prompt-engineer Production prompt engineering — write, iterate, and refine prompts with built-in eval loop feedback
jmagly/aiwg 107
productionize Production readiness review — strip prototype scaffolding, harden code, validate cost model, generate prod/ artifacts
jmagly/aiwg 107
pipeline-status Show status overview of all LLM inference pipelines in the current project
jmagly/aiwg 107
pipeline-design Interactive LLM inference pipeline design — elicits requirements, recommends pattern, scaffolds production-ready artifacts
jmagly/aiwg 107
pattern-selector Recommends the right LLM pipeline pattern for a use case — simple chain, embedded agent, state machine, RAG, eval loop, or dynamic prompt
jmagly/aiwg 107
eval-loop Configure and run the isolated eval loop pattern — generate, evaluate, refine until pass threshold met
jmagly/aiwg 107
cost-optimizer Analyze LLM pipeline costs and generate concrete optimization recommendations with savings estimates
jmagly/aiwg 107
iteration-control
jmagly/aiwg 107
source-unifier Merge multiple documentation sources (docs, GitHub, PDF) with conflict detection. Use when combining docs + code for complete skill coverage.
jmagly/aiwg 107
pdf-extractor Extract text, tables, and images from PDF files. Use when converting PDF documentation, manuals, or reports to searchable text.
jmagly/aiwg 107
llms-txt-support Detect and use llms.txt files for LLM-optimized documentation. Use when checking if a site has LLM-ready docs before scraping.
jmagly/aiwg 107
doc-splitter Split large documentation (10K+ pages) into focused sub-skills with intelligent routing. Use for massive doc sites like Godot, AWS, or MSDN.
jmagly/aiwg 107
doc-scraper Scrape documentation websites into organized reference files. Use when converting docs sites to searchable references or building Claude skills.
jmagly/aiwg 107