research-systematically

research-systematically is a skill for Claude Code, Codex from Drizzy07x/Skillquiver. It costs 40 tokens per session (1,412 once invoked), scanned A, original, MIT.

A process for researching uncertain technical questions using original sources and documentation that matches the software version. It keeps a written record of the question, planned methods, evidence, failed approaches, and conclusion.

In plain words
What is it for?
Use it for comparisons, experiments, benchmarks, investigations, and checking external software documentation. It is also suited to questions where findings must be reproducible and supported by evidence.
Why use it?
It reduces the risk of presenting guesses or outdated library, SDK, command-line tool, or API details as facts.

Skill for Claude CodeCodex

Part of the skillquiver plugin — 24 skills shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/drizzy07x/skillquiver/research-systematically
Any agent
npx skills add Drizzy07x/Skillquiver --skill research-systematically
Clone the repo
git clone --depth 1 https://github.com/Drizzy07x/Skillquiver

Made for: Claude Code, Codex.

Or install skillquiver, the plugin that ships this one along with the rest of its 24 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for research-systematically

README.md
[![agentmods](https://agentmods.dev/badge/skills/drizzy07x/skillquiver/research-systematically.svg)](https://agentmods.dev/skills/drizzy07x/skillquiver/research-systematically)
Your own site
<a href="https://agentmods.dev/skills/drizzy07x/skillquiver/research-systematically"><img src="https://agentmods.dev/badge/skills/drizzy07x/skillquiver/research-systematically.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,412 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00040 $0.01412
Opus 5 $0.00020 $0.00706
Sonnet 5 $0.00008 $0.00282
Haiku 4.5 $0.00004 $0.00141

Measured 3d ago against content hash 1819c62d3021, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research-systematically scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/research-systematically/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research Systematically

Turn an uncertain question into a reproducible research record without presenting exploration as confirmation, and ground every external-API claim in current, version-matched documentation instead of recollection.

Keep a research record

Maintain one plain file (markdown or JSON, in a scratch directory or outside the repo) holding: the research question, the frozen plan, an evidence log, dead ends, pivots, and the final verdict. Append to the evidence log; never rewrite or delete earlier entries. The pre-registration section is written once and left untouched after results exist.

1. Freeze the question

Before collecting any result evidence, write down:

  • The question, stated once.
  • Hypotheses, each with a concrete prediction and a falsifier — what observation would prove it wrong.
  • Methods and planned experiments, each with a stable ID.
  • Stopping rules: what ends the research besides an answer.

Do not edit this section after results are known. Record deviations, failed approaches, and pivots as new entries instead of rewriting the original plan.

2. Establish local versions before consulting docs

When the research or implementation touches an external library, framework, SDK, CLI, or cloud API:

  • Inspect the manifest or lockfile first. The repository, not memory, says which release is installed.
  • Never guess a version the repository can provide.
  • Local code, callers, and tests stay authoritative for project behavior; external docs describe the external contract only.

3. Retrieve version-matched documentation

Prefer freshness over recollection: an API surface that may have drifted is confirmed against a current source, not recalled.

  • If a documentation-lookup MCP tool (such as Context7) is available: resolve the library ID, pick the closest name with suitable coverage and reputation, prefer an ID matching the locally installed version, and query one concrete topic.
  • Otherwise use the vendor's authoritative documentation directly and note which channel was used.
  • At most three documentation queries per task. Split unrelated topics into separate queries.
  • Never send credentials, private source, customer data, or complete error dumps in a query.
  • For each lookup, log: library, version matched, exact query or topic, source URL, and retrieval date. Treat a lookup as stale once the installed dependency version changes or the entry is older than about a day — re-retrieve rather than reuse.

Read the full file on GitHub · 102 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 102 lines · 40 tokens per session scan A 1819c62d3021

Subscribe to this mod's changes

research-systematically is a skill published in the GitHub repository Drizzy07x/Skillquiver (2 stars, last pushed 11d ago), licensed MIT. It adds 40 tokens to every session and 1,412 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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