A 267-skill research graph in pure markdown — 51 research operations built from 216 single-purpose steps, composed in any order with explicit backtracking. One npx install, no runtime, no MCP bindings. The AI is the researcher; you set the direction.
OpenTelemetry tracing for DeepSeek Harness (dsh): turns each agent turn into a GenAI span tree — steps, LLM calls with TTFT, tool executions, token usage — exported over standard OTLP to Jaeger, Grafana Tempo, SigNoz, Langfuse, or any compatible backend.
ADHD behavioral coaching skill for DeepSeek Harness. Guides readers through task execution - breaks tasks into micro-steps, manages overwhelm, provides launch rituals, calibrates time estimates, and recovers from self-blame.
dsh-pm is the ChunSun × DeepSeek Harness reference plugin: an AI-native project-delivery loop driven by ChunSun. Requirements / Runs / Steps / acceptance scenarios & cases / work-memory, a session delivery panel, and 28 chunsun_* model tools — with the platform as the single source of truth. MIT.
让 Agent 查询课程能力、清单知识点和本地学习者档案，辅助判断下一步学习。 | Let agents query curriculum capabilities, checklist knowledge, and local learner profiles to guide next steps.
Solidify DSH session history step by step: rewrite tool results in place to the facts the model actually kept, so long conversations stop re-sending noise every turn. Costs tokens and time: a ~770-character contract in every request, plus one extra full-context request and round-trip per step when stepSummary is on.
Auditable mathematical proving for DeepSeek Harness: a local stdio MCP server (23 tools), a bundled workflow skill, and the /prove and /audit-proof commands. proven / refuted / inconclusive are never conflated, and unproven steps are reported, not hidden.
