whoami

whoami is a skill for Claude Code from jazz1x/honne. It costs 56 tokens per session (1,991 once invoked), scanned A, original, MIT.

A self-observation skill that scans local language-model transcripts and turns evidence about your work into a seven-part personal profile.

In plain words
What is it for?
Use it to create a profile of your work habits and recurring patterns from one project or from all projects.
Why use it?
It reduces the effort of reviewing many transcripts yourself and separates observed patterns from a manually written self-description.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: names the AskUserQuestion tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the honne plugin — 5 skills, 1 hook shipped together

Good fit Use it to create a profile of your work habits and recurring…

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add jazz1x/honne
Claude Code
/plugin install honne

Made for: Claude Code.

Or install honne, the plugin that ships this one along with the rest of its 5 skills, 1 hook.

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 whoami

README.md
[![agentmods](https://agentmods.dev/badge/skills/jazz1x/honne/whoami.svg)](https://agentmods.dev/skills/jazz1x/honne/whoami)
Your own site
<a href="https://agentmods.dev/skills/jazz1x/honne/whoami"><img src="https://agentmods.dev/badge/skills/jazz1x/honne/whoami.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,991 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00056 $0.01991
Opus 5 $0.00028 $0.00996
Sonnet 5 $0.00011 $0.00398
Haiku 4.5 $0.00006 $0.00199

Measured 6d ago against content hash c1149db5eda8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

whoami 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 6d 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/whoami/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

honne — 7-Axis Self-Observation

When invoked, execute Step 1 through Step 6 in order immediately. Do not summarize the skill or ask what the user wants — invocation itself is the request. Start by asking the Step 1 question.

Step 1: Scope + Locale HITL

Invoke AskUserQuestion tool with two questions in a single call:

(a) Scope:

  • question: "Scan scope?"
  • options: [{"label":"repo","description":"current project only"},{"label":"global","description":"all projects"}]

(b) Locale:

  • question: "Locale?"
  • options: [{"label":"ko","description":"한국어"},{"label":"en","description":"English"},{"label":"jp","description":"日本語"}]

Set SCOPE and LOCALE from the two replies. Do not use plain-text Q&A — arrow-key selection only.

Step 2: Scan

Run: bash "${CLAUDE_PLUGIN_ROOT}/scripts/honne" scan --scope "$SCOPE" --cache ".honne/cache/scan.json" Capture RUN_ID from result: RUN_ID=$(python3 -c 'import json; print(json.load(open(".honne/cache/scan.json"))["run_id"])') Non-zero exit → output stdout+stderr verbatim to user, stop. Do not interpret exit codes.

Step 3: Rejection reframe filter (skip candidate)

For each axis, run: bash "${CLAUDE_PLUGIN_ROOT}/scripts/honne" query --base-dir ".honne" --tag "<axis>" --type rejection --scope "$SCOPE" Before Step 4 records each axis, pipe the candidate through bash "${CLAUDE_PLUGIN_ROOT}/scripts/honne" axis validate --text "$candidate" --locale "$LOCALE" --skip-if-overlaps "$rejection_text" — exit 3 = overlap, skip and log "reframed". 모든 변수는 큰따옴표 인용 필수(공백·특수문자 안전). LLM 호출 없음.

Recording rejections: If the user explicitly says "n" or rejects a candidate claim for any axis, record it as a rejection so Step 3 can filter it in future runs:

bash "${CLAUDE_PLUGIN_ROOT}/scripts/honne" record claim \
  --type rejection --axis "$axis" --scope "$SCOPE" \
  --claim "$CANDIDATE" --run-id "$RUN_ID" \
  --out ".honne/assets/rejections.jsonl"

Read the full file on GitHub · 172 lines

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. 6d ago First seen · 172 lines · 56 tokens per session scan A c1149db5eda8

Subscribe to this mod's changes

whoami is a skill published in the GitHub repository jazz1x/honne (3 stars, last pushed 3mo ago), licensed MIT. It adds 56 tokens to every session and 1,991 once invoked, about $0.0003 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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