AgentSkillsCN

research-task

运用组合式工作流程规划开展研究任务。首先对任务进行清晰界定,从研究策略库中选取合适方法,执行研究并自我评估输出质量。

SKILL.md
--- frontmatter
name: research-task
description: Execute a research task using compositional workflow planning. Characterizes the task, selects an approach from a library of research strategies, executes it, and self-evaluates output quality.
argument-hint: [research question or topic]
user-invocable: true
allowed-tools: Read, Glob, Grep, Bash, WebFetch, WebSearch

Research this topic: $ARGUMENTS

Workflow

Phase 1: Task Characterization

Rate the research task on each dimension (1-5):

Dimension1 (Low)5 (High)
ScopePoint question (single fact)Frontier mapping (state of entire field)
Domain structureSingle field, established methodsDistant cross-domain analogy
Evidence typeEmpirical data, measurementsTheoretical arguments, first-principles
Time horizonWhat's true right nowWhat could become true (speculative)
FidelityBallpark / directionalRigorous / publication-grade

Phase 1b: Workflow Library Query

Query the MAP-Elites workflow library for strategies matching this task's coordinates:

bash
sqlite3 research-workflows/workflows.db "SELECT id, name, archetype, fitness_score FROM workflows WHERE status IN ('active', 'seed') ORDER BY fitness_score DESC LIMIT 10;"

For more targeted retrieval, filter by behavioral region:

bash
sqlite3 research-workflows/workflows.db "SELECT w.id, w.name, ws.name as step_name, ws.description, ws.rationale FROM workflows w JOIN workflow_steps ws ON w.id = ws.workflow_id WHERE w.archetype = '<archetype>' AND w.status IN ('active', 'seed') ORDER BY w.fitness_score DESC, ws.step_order;"

Use retrieved workflows to inform Phase 2 strategy selection. Prefer workflows with high fitness scores and usage counts.

Phase 2: Strategy Selection

Based on the characterization and library query, select the most appropriate research archetype:

Exploratory (high scope, moderate fidelity)

  • Quick landscape scan — 15-minute overview of a new area
  • Deep literature review — comprehensive survey of a mature field
  • Trend detection — what's gaining momentum
  • White space identification — what isn't being worked on that should be

Confirmatory (low scope, high fidelity)

  • Fact-checking pipeline — verify specific claims with primary sources
  • Consensus mapping — what do experts agree/disagree on
  • Replication check — has this finding held up under scrutiny

Analytical (moderate scope, high evidence)

  • Compare and contrast — systematic comparison of N approaches
  • Root cause analysis — why did X happen / why doesn't X work
  • Cost-benefit analysis — should we do X given tradeoffs
  • Forecasting — given current trends, what's likely

Generative (high domain structure, moderate time horizon)

  • Cross-domain transfer — find solutions from field B for problems in field A
  • First-principles derivation — reason from fundamentals, not literature
  • Synthesis — combine N existing ideas into a novel framework
  • Adversarial stress-test — find the strongest objections to X

Applied (low time horizon, high fidelity)

  • Technical feasibility assessment — can X be built with current tools
  • Build-vs-buy analysis — build or use existing solution
  • Implementation planning — how to execute X given constraints

Phase 3: Compositional Planning

Don't just pick one strategy. Identify which sub-techniques from multiple strategies suit this specific task:

  1. Select 2-3 relevant archetypes from Phase 2
  2. For each, identify the steps that apply to this task
  3. Note why each step exists (what it optimizes for)
  4. Compose a novel workflow combining the best elements
  5. Record which elements were borrowed from which archetype

Phase 4: Execution

Execute the composed workflow using available tools:

  • WebSearch — broad web queries
  • Exa (web_search_exa, web_search_advanced_exa) — semantic/neural search, cross-domain retrieval
  • WebFetch — deep read of specific URLs
  • Firecrawl (firecrawl_scrape, firecrawl_search) — structured web scraping
  • GitHub — repository search, code search, trending
  • Read/Glob/Grep — local codebase and documentation

Phase 5: Self-Evaluation

Score your output on each dimension (0-1):

  1. Coherence — Does it tell a consistent story? Internal contradictions?
  2. Grounding — Are claims supported by retrieved evidence?
  3. Compression — Can key findings be stated concisely? (If not, thesis may be unclear)
  4. Surprise — Did anything non-obvious surface? (Confirming the known has low value)
  5. Actionability — Does the output enable a decision or next step?

Report the scores honestly. Low scores on surprise or actionability are useful signals — they indicate the research question may need reframing.

Phase 5b: Library Update

Log this execution and update the workflow library:

bash
# Log the execution
sqlite3 research-workflows/workflows.db "INSERT INTO executions (task_description, task_scope, task_domain_structure, task_evidence_type, task_time_horizon, task_fidelity, score_coherence, score_grounding, score_compression, score_surprise, score_actionability, score_composite) VALUES ('<description>', <scope>, <domain>, <evidence>, <horizon>, <fidelity>, <coherence>, <grounding>, <compression>, <surprise>, <actionability>, <composite>);"

If composite score exceeds the current occupant of this behavioral region, the composed workflow becomes a candidate for library insertion.

Phase 6: Output

code
## Research: [Topic]

### Characterization
Scope: [1-5] | Domain: [1-5] | Evidence: [1-5] | Horizon: [1-5] | Fidelity: [1-5]

### Strategy
[Which archetypes were combined and why]

### Findings
[Structured findings — use headers, tables, or lists as appropriate]

### Quality Assessment
Coherence: [0-1] | Grounding: [0-1] | Compression: [0-1] | Surprise: [0-1] | Actionability: [0-1]

### Key Takeaway
[Single paragraph — the compressed thesis]

### Recommended Next Steps
[What to do with these findings]