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Holmes Skill

An agent skill that reads a photo the way Sherlock Holmes reads a stranger (福尔摩斯): observe first, deduce second, every conclusion cites its evidence and you grade it. Works…

Install

$npx skills add Oldcircle/holmes-skill
Claude Code
claude mcp add holmes-skill -- npx skills add Oldcircle/holmes-skill
Cursor / Claude Desktop (mcp.json)
{
  "mcpServers": {
    "holmes-skill": {
      "command": "npx",
      "args": [
        "skills",
        "add",
        "Oldcircle/holmes-skill"
      ]
    }
  }
}

Install command taken from the project's README or registry entry. Always review code before running it.

🔍 Holmes

An agent skill that reads a photo the way Sherlock Holmes reads a stranger, and shows its work.

Works with

…and any other agent that reads SKILL.md and can see images.

A real desk photo. It wrote 16 observations and sealed the list before reasoning. Six conclusions followed, each citing its evidence. The desk's owner graded them: 5 hits, 1 miss, 0 made-up observations.


Quick start

npx skills add Oldcircle/holmes-skill

Pick your agents when prompted. Then give your agent a photo of your desk, your room or yourself and say:

Read this like Sherlock Holmes.

The agent lists what it can see, seals that list, and only then starts to reason. Every conclusion names the observations it stands on and offers another explanation for the same marks. You mark each one hit, half or miss. Prefer to copy the folder yourself? See Installation.

Why Holmes

  • It looks first and thinks second. Everything it can see goes on a numbered list, and the list is sealed before the trace library is opened. A model that already knows it is looking for a runner will find running shoes that are not there.
  • Every conclusion shows its evidence. Each one cites observation numbers, gives a chain of at most three steps, a calibrated confidence, and an alternative explanation. A conclusion that needs something off the list is dropped.
  • You grade it. Hit, half or miss for each conclusion, and a flag for any observation that is not in the photo. It reports two numbers: the hit rate, and the false-observation rate, which matters more.
  • It commits. At least one conclusion has to combine two or more unrelated observations, and one or two have to be bold bets stated without hedging.
  • Marks, never looks. Reading a person, it records what their habits left behind: pressure marks, tan lines, wear, calluses, which wrist the watch is on. Faces, bodies, age, ethnicity and health stay off the list.
  • No API key, no server. The skill is one Markdown file and a trace library. The model your agent already uses does the looking.
  • One skill, every agent. A standard Agent Skill, so the same folder works in Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.

Two real photos

Both photos were read with this skill and graded line by line by the person they belong to.

Photo Observations Made up Conclusions Hit Miss
A desk 16 0 6 5 1
A selfie, read as a person 13 (9 of them about the room) 0 5 5 0

The desk

Before any reasoning it wrote 16 observations. Two of them look like filler: a red can standing on the mouse pad, and the desk edge is unusually thick, with a metal frame and a hanging cable underneath.

The second one became a conclusion: "This is an electric standing desk." An ordinary desk has no reason to be plugged in. The owner confirmed it.

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