docxology
UserProduction-grade scaffold for reproducible computational research — 10-stage DAG pipelines, ≥90% test coverage, cryptographic provenance (SHA-256 + steganographic PDF watermarking), multi-project workspace, AI-agent-ready with AGENTS.md / SKILL.md throughout.
Categories
Indexed Skills (49)
telemetry
Unified pipeline telemetry — collects per-stage performance metrics (CPU, memory, I/O) and diagnostic events into structured reports (JSON, text).
template-academic-paper-reviewer
Read-only manuscript review, methodology review, re-review, and calibration workflow for template projects. USE WHEN the user asks for peer review, referee-style critique, methods/statistics review, or verification that revisions addressed prior comments.
template-academic-paper
Template-native manuscript planning, outline, drafting, revision, formatting, citation check, and AI-use disclosure routing. USE WHEN the user asks to write, outline, revise, format, or prepare a paper inside the Research Project Template.
template-academic-pipeline
Research-to-publication orchestrator for template projects: research, write, verify, review, revise, reproduce, validate, and finalize. USE WHEN the user wants the whole paper workflow or enters midstream with an existing paper or reviewer comments.
template-agentic-use
Agentic-use and skill-routing workflow for the Research Project Template. USE WHEN discovering relevant skills, making template easier for agents to navigate, auditing docs/prompts skill coverage, checking .cursor/skill_manifest.json, evaluating external skills, reviewing external agentic operating models such as Steward OS, AutoResearch CLI, LEANN, or Agent Skills for Context Engineering, or improving agent onboarding/routing without changing project behavior.
template-code-development
Standards-compliant code for the Research Project Template — infrastructure or project src/, thin orchestrators, type hints, no mocks. USE WHEN implementing an algorithm, utility, analysis method, optimizer, new module in projects/*/src or infrastructure/, or user says add code following template architecture — even without docs/prompts. Not for end-to-end feature spanning manuscript + pipeline (use template-feature-addition).
template-comprehensive-assessment
Full checkout audit for the Research Project Template — tests, architecture, docs, manuscript, pipeline. USE WHEN user asks for comprehensive assessment, full repo review, health check across projects, audit everything, or pre-merge sanity sweep for template exemplars — even without naming docs/prompts or a skill. Not for single failing stage only.
template-deep-research
Template-native research intake, literature search, source verification, synthesis, fact-checking, and systematic-review planning. USE WHEN the user asks to research a topic, build a literature corpus, fact-check claims, prepare a PRISMA-style review, or clarify a research question before manuscript work.
template-manuscript-claim-verification
Triple-pass verification of every manuscript claim against code, data, refs, and renderer; repair prose while staying renderable. USE WHEN pre-submission, pre-Zenodo, pre-arXiv, abstract numbers disagree with CSV, citations do not support sentences, or user asks to triple-check / verify every claim — even without docs/prompts. Not for casual PDF summary.
template-manuscript-creation
Scaffold a research manuscript and project layout from a research brief — sections, config.yaml, src/, scripts/, tests. USE WHEN starting a new paper, new projects/ tree, manuscript from topic description, or aligning with template_code_project exemplar — even without copy-paste prompts.
template-manuscript-cross-references
Audit or author registry-driven manuscript cross-refs — labels.yaml, [[FIG:]], [[THMREF:]], [[VAR:]] tokens. USE WHEN fixing figure/equation/theorem numbering, orphan registry keys, hard-coded "Theorem 7.3" in prose, or [[MISSING:]] injection failures — even for Pandoc projects that also use a YAML registry.
template-pipeline-debugging
Systematic pipeline DAG failure triage for the Research Project Template. USE WHEN ./run.sh or execute_pipeline.py fails, a stage stalls (setup, tests, analysis, render, validate, LLM, copy), pytest/coverage gate fails mid-pipeline, PDF render or validate breaks, Project Analysis finishes too fast with no figures, or user says pipeline debug, stage failed, resume checkpoint, core-only triage — even without naming this skill or docs/prompts.
template-reproducibility-audit
Deterministic reproducibility audit — fixed seeds, regenerate-from-clean, double-run diff before Zenodo/arXiv/release. USE WHEN outputs drift between runs, "worked on my machine", need regenerate-from-clean proof, or pre-release reproducibility check — even without naming docs/prompts.
template-workflows
Router and workflow hub for the Research Project Template — code, tests, manuscripts, pipeline, validation, docs, and reproducibility. USE WHEN the user wants to audit the repo, debug ./run.sh or execute_pipeline.py, add a feature, write tests, scaffold a manuscript, verify claims, fix cross-references, run validation gates, refactor, or prepare a Zenodo release, discover relevant template skills, improve agent routing, review external agentic operating models or retrieval references such as Steward OS, AutoResearch CLI, or LEANN, research a topic, write/review/revise a paper, or run a research-to-publication workflow — even if they never say "prompt", "skill", or docs/prompts. Also USE WHEN intent is ambiguous ("something wrong with my manuscript", "full health check", "fix the pipeline") — pick ONE child skill from the routing table and follow it. Do NOT use for generic homework, unrelated app security scans, or casual PDF summarization.
template-startup
Full installation and validation workflow for a fresh clone of the Research Project Template. Installs dependencies, runs pre-commit hooks, executes the core pipeline against template_code_project, validates outputs, and reports a structured PASS / FAIL for each checkpoint. USE WHEN the user says "start", "set up the repo", "install and validate", "does everything work", "first time setup", or any variant of bootstrapping a fresh or unknown checkout.
continual-learning
Orchestrate continual learning by delegating transcript mining to agents-memory-updater and writing durable memory to local JSON (never root AGENTS.md Learned sections). USE WHEN continual learning, mine prior chats, maintain agent memory, or the stop hook triggers this skill.
template-documentation-creation
Author or refresh AGENTS.md and README.md for template directories — accurate commands, Mermaid where helpful, link _generated/active_projects.md. USE WHEN folder needs AGENTS, README audit, doc contract fix, or signposting after code change — even without documentation_creation prompt.
template-feature-addition
End-to-end feature work across src/, scripts/, tests/, manuscript, and docs for the Research Project Template. USE WHEN adding a pipeline-visible feature, new analysis stage, manuscript-facing output, or cross-layer integration — even without feature_addition prompt.
template-infrastructure-module
Add or extend generic infrastructure/ packages — Layer 1 modules with SKILL.md, AGENTS.md, tests in tests/infra_tests/, 60% coverage. USE WHEN new reusable package under infrastructure/, extending validation/rendering/llm, or skill manifest entry — even without infrastructure_module prompt.
template-literature-synthesis
LLM prompt blocks for per-paper and corpus literature synthesis after a search pipeline. USE WHEN synthesizing arXiv/Crossref results, writing per-paper notes, thematic clusters, gap analysis from a literature corpus, or wiring template_search_project synthesis — even without naming infrastructure.llm templates.
template-methods-orchestration
Repo-wide methods orchestration workflow for the Research Project Template. USE WHEN the user asks to add, audit, improve, or validate methods, methodology, method contracts, stage-to-method wiring, artifact/evidence provenance, or orchestration across template projects.
template-publication-audit
End-to-end publication-readiness audit for public template exemplars — tests, no-mocks, methods contracts, claims, evidence, figures, references, rendered outputs, and deterministic regeneration. USE WHEN preparing a template or project for release, DOI deposit, peer review, or a publication sign-off.
template-refactoring
Clean-break refactors with migration for the Research Project Template — move logic to src/, split modules, rename APIs with test updates. USE WHEN restructuring code, extracting modules, removing duplication, or migration without behavior change.
template-test-creation
Create pytest suites under the no-mocks policy — real data, temp files, subprocess, pytest-httpserver. USE WHEN adding tests, raising coverage, testing new src/ module, or user forbids mocks.
template-validation-quality
Run validation CLI, prerender, markdown/PDF/integrity gates, and QA workflows for the Research Project Template. USE WHEN validate manuscript, check PDF for ?? refs, prerender gate, link checker, output integrity, or pre-commit validation — even without validation_quality prompt.
advanced-evaluation
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
bdi-mental-states
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
context-compression
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
context-degradation
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
context-fundamentals
This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills: debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.
evaluation
This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.
filesystem-context
This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.
harness-engineering
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
hosted-agents
This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.
latent-briefing
This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
long-horizon-prompting
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
memory-systems
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
multi-agent-patterns
This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.
project-development
This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.
self-improvement-loops
This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.
tool-design
This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development; route deciding whether to introduce sub-agents to multi-agent-patterns.
infrastructure-autoresearch
Skill for deterministic AutoResearch readiness planning. Use when adding, validating, or documenting opt-in autoresearch.yaml controls, stage-gate readiness, evidence-grounded claims, artifact readiness reports, or AutoResearchClaw-inspired workflow checks in template projects.
infrastructure-core
Skill for the core infrastructure module providing logging, configuration, exception handling, progress tracking, checkpoints, retry logic, pipeline execution, performance monitoring, security, file operations, and multi-project orchestration. Use when setting up logging, loading config, handling errors, running pipelines, or monitoring performance.
infrastructure-doctor
Skill for the doctor infrastructure module providing repository-level diagnostics and safe, reversible automated repair. Use when the local checkout is acting up, before opening a bug, when onboarding a fresh clone, or as a periodic health probe. Every mutation is backed up under .doctor/backups/ and journalled in .doctor/actions.jsonl so any change can be undone byte-for-byte.
infrastructure-documentation
Skill for the documentation infrastructure module providing figure management, image handling, markdown integration, and API glossary generation. Use when managing research figures, inserting images into manuscripts, auto-numbering figures, or generating API documentation.
infrastructure-benchmark
Deterministic benchmark harnesses for public template exemplars. Use when scoring generated project outputs against benchmark manifests, refreshing the default template smoke manifest, checking publication-readiness rubrics, or adding bounded no-network readiness checks for public template outputs.
infrastructure-config
Repository-scoped configuration for the template—environment variable templates and default secure/steganography YAML. Use when editing .env patterns, secure_config defaults, or cross-referencing project manuscript config overrides.
infrastructure-docker
Container build and compose assets for the research template. Use when reproducing CI or cloud environments with Docker, following docs/CLOUD_DEPLOY.md, or wiring Ollama sidecars via docker-compose.
gauss-open-gauss-cli
Open Gauss in this workspace is the math-inc/OpenGauss gauss CLI for Lean session tooling—not Huawei OpenGauss DBMS. See docs/reference/opengauss-naming.md; for the public Lean exemplar see projects/templates/template_active_inference/lean/.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.