writing-specs
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/writing-specsYou are generating a Research Spec — a document that is simultaneously human-readable and machine-executable. Another CC session will later read this spec and execute it step by step.
SKILL.md
.github/skills/writing-specsView on GitHub ↗
--- name: writing-specs description: Generate a complete, executable Research Spec from North Star + user input. Strategy-level skill that orchestrates questioning, outline, and spec writing. execution: sequential used-by: de-anthropocentric-research-engine --- # Writing Specs You are generating a Research Spec — a document that is simultaneously human-readable and machine-executable. Another CC session will later read this spec and execute it step by step. ## Prerequisites Before invoking this skill, the following MUST exist: - A confirmed North Star statement - A structured ResearchBrief - Both preserved in a context-checkpoint file ## Flow ### Step 1: Read Research Catalog Read `skills/research-catalog/SKILL.md` in its entirety. Internalize: - All available campaigns and their strategies - The dependency relationships between campaigns - Pre-conditions for each campaign ### Step 2: Structured Questioning Invoke these 3 SOPs sequentially. Each asks 2-3 focused questions: 1. `scope-clarification` — research boundaries, depth vs breadth 2. `campaign-selection` — which campaigns to include/emphasize/skip 3. `constraint-elicitation` — time budget, existing knowledge, hard constraints ### Step 3: Pipeline Outline Synthesize the North Star, ResearchBrief, and user answers into a 5-10 line outline: ``` Stage 1: [campaign] ([strategies]) — [topic/focus] Stage 2: [campaign] ([strategies]) — [topic/focus] ... Stage N: experiment-execution (experiment-design) — [topic] ``` Present this outline to the user. Wait for confirmation. User may adjust stages, reorder, add, or remove. ### Step 4: Write Full Spec Expand the confirmed outline into a complete Research Spec. Follow this schema exactly: #### Spec Header ``` # Research Spec: <Topic> > Generated: YYYY-MM-DD > North Star: <one sentence> > Scope: <N> stages, estimated <M> sessions > Source: de-anthropocentric-research-engine ``` #### Global Sections - Global Context Protocol (context-init/checkpoint rules) - Global Execution Rules (±10% deviation, backtrack confirmation) - Global Backtrack Conditions #### Per-Stage Structure For EACH stage, write ALL of these fields: - **Objective**: What this stage accomplishes - **Expected Input**: What context is available from prior stages - **Focus Areas**: Specific aspects to emphasize - **Recommended Combination**: campaign → strategy-A, strategy-B - **Completion Criteria**: Quantified threshold (numeric or objectively verifiable) - **Backtrack Condition**: if [condition], → Stage N (requires user confirmation) - **Execution Steps**: Checkbox items (context-init, each strategy, checkpoints) #### Granularity Rules - Name specific strategies (not tactics/SOPs — those are CC's choice at execution time) - Describe what topic/aspect each strategy addresses - Specify what prior context is available and how to use it - Quantify ALL completion criteria (no vague "sufficient" or "adequate") - Focus Areas tell CC what to prioritize within the campaign's scope ### Step 5: Spec Self-Review Invoke `spec-self-review` SOP. This is MANDATORY and cannot be skipped. ### Step 6: User Review Present the completed spec to the user for review. Wait for approval or change requests. If changes requested, revise and re-run self-review. ## Output Save the spec to: `docs/de-anthropocentric/specs/YYYY-MM-DD-<topic>-spec.md` Inform the user: "Spec complete. To execute, invoke `executing-specs` with the path to this spec file."
More from yogsoth-ai/de-anthropocentric-research-engine
- abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
- ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
- ablation-component-mappingMap system architecture to ablatable units for ablation studies
- ablation-designDesign ablation studies to isolate component contributions in ML systems
- ablation-executionRemove components one by one from a system, record the response/impact of each removal.
- abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
- abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
- abstraction-ladderPerform bisociation at multiple abstraction levels
- abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
- abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.