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research

Skill8 starsChanged 2 months agoMITView on GitHub

Investigate a technical question against high-trust primary sources and capture findings as a cited Markdown note. Use when gathering API facts, reading documentation, verifying library capabilities, or delegating reading legwork — even if the user says \"look into this library\". Do NOT use for writing production feature code.

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 164 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Mandatory Attribution & Line-Level Citations: Every technical claim, API signature, or version constraint must link directly to its primary …

    line 15
  2. Distinctive:

    Background Agent Execution: Run intensive reading, scraping, and repository audits in an isolated background subagent to preserve the …

    line 16
  3. Very specific:

    Key Point: Verify the exact version compatibility against the project's package.json, pyproject.toml, or Cargo.toml.

    line 45
  4. Very specific:

    Action: Write the synthesis to docs/research/<slug>.md (or project standard path):

    line 52

“Common” is measured: kept by at least 6 in 10 of the 164 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Research

Investigate technical questions, library capabilities, and architectural facts against authoritative primary sources, capturing verifiable findings in a cited Markdown research note.

Core Invariants

  1. Primary Sources Exclusively: Ground all findings in official documentation, source code, formal specifications, or first-party release notes; never rely on unverified blog posts or secondary summaries.
  2. Mandatory Attribution & Line-Level Citations: Every technical claim, API signature, or version constraint must link directly to its primary source URI or repo file path.
  3. Background Agent Execution: Run intensive reading, scraping, and repository audits in an isolated background subagent to preserve the primary agent's working context.
  4. Structured Decision Markdown Output: Synthesize research into a permanent markdown file matching project conventions (e.g. docs/research/<slug>.md or .scratch/research/<slug>.md).
  5. Separation of Fact vs. Opinion: Explicitly separate verified architectural facts from subjective engineering recommendations.

Architecture & Map of Content (MOC)

[ Technical Question / Library Query ] ──► [ Spawn Research Subagent ] ──► [ Primary Source Retrieval ] ──► [ Cited Synthesis Report ]
ComponentResponsibilityOutput Target
Primary Source AuditorRead official docs, Github repos, specsWeb search & URL content tools
Research NoteStructured findings with quotes & citationsdocs/research/<topic>.md
Verification GateValidate API signatures against runtime/versionDirect test snippets

Step-by-Step Procedure (TWI)

Step 1: Formulate Research Hypothesis & Boundary

  • Action: Define the core technical questions, necessary library versions, and compatibility requirements.
  • Key Point: Distinguish between hard technical limits (e.g. rate limits, memory footprint) and ergonomic trade-offs.
  • Why: Unbounded research spirals into excessive token consumption without answering the core decision question.

Step 2: Query Authoritative Primary Sources

  • Action: Fetch primary documentation, GitHub repositories, RFCs, and API references using search and web tools.
  • Key Point: Verify the exact version compatibility against the project's package.json, pyproject.toml, or Cargo.toml.
  • Inline Checklist:
    • Source is first-party / authoritative
    • Version matches project environment
    • Exact API signatures and failure modes captured

Step 3: Author Cited Research Note

  • Action: Write the synthesis to docs/research/<slug>.md (or project standard path):
    • Summary: High-level verdict and recommended direction.
    • Key Findings: Concrete technical facts with direct markdown links.
    • Code Samples: Minimal, validated usage examples.
    • Trade-offs & Gotchas: Edge cases, performance bottlenecks, and limitations.
  • Why: Permanent research notes preserve institutional context and prevent repeating research across engineering cycles.

Anti-Rationalization Guardrails

Tempting RationalizationBinding RuleEngineering Rationale
"I remember how this library works from training data, so no need to look up current docs."Mandatory primary-source lookup for all library facts.Training memory hallucinates deprecated API signatures and misses recent breaking changes.
"Summarize from a third-party tutorial or forum post."Trace claims back to the authoritative primary source.Third-party tutorials often propagate anti-patterns and outdated workarounds.
"Inline the full research text into chat without writing a file."Always commit findings to a durable research note.In-chat findings vanish across context resets; markdown files provide persistent documentation.

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