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.
git clone --depth 1 https://github.com/yaniv-golan/claude-familiarWrote 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.
[](https://agentmods.dev/commands/yaniv-golan/claude-familiar/familiar)<a href="https://agentmods.dev/commands/yaniv-golan/claude-familiar/familiar"><img src="https://agentmods.dev/badge/commands/yaniv-golan/claude-familiar/familiar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/yaniv-golan/claude-familiar/familiar"><img src="https://agentmods.dev/badge/commands/yaniv-golan/claude-familiar/familiar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00512 |
| Opus 5 | $0.00008 | $0.00256 |
| Sonnet 5 | $0.00003 | $0.00102 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
familiar 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 12d 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.
What it actually says
Route this command to the correct familiar MCP tool based on the subcommand.
Subcommand: $1 | Arguments: $ARGUMENTS
Routing
personality (default when $1 is empty or "personality"):
- No further args → call
familiar-personalitywith no arguments (show mode) $1is "personality" and$2is "reset" → callfamiliar-personalitywithreset: true$1is "personality" and$2+ is present → callfamiliar-personalitywithdescriptionset to everything after "personality" in$ARGUMENTS$1is missing entirely → callfamiliar-personalitywith no arguments (show mode)
mood (when $1 is "mood"):
- No further args → call
familiar-moodwith no arguments (show current mood) $2is present → callfamiliar-moodwithset=$2(valid: cheerful, neutral, grumpy, excited, sleepy, chaotic)
stats (when $1 is "stats"):
- Call
familiar-statswith no arguments
lore (when $1 is "lore"):
- No further args → call
familiar-lorewith no arguments (show all lore) $2+ is present → callfamiliar-lorewithaddset to everything after "lore" in$ARGUMENTS
Unrecognized subcommand: If $1 doesn't match any of the above, tell the user the valid subcommands:
/familiar personality [description|reset]— show, change, or reset personality/familiar mood [mood-name]— show or set mood/familiar stats— show companion stats and history/familiar lore [new entry text]— show or add backstory
Responding
These tools return context seeds — JSON with personality, mood, and an instruction field. Use that context to generate an in-character response. Consult the familiar-help skill for mood calibration and the full response pattern.
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.
- 12d ago First seen · 38 lines · 16 tokens per session scan A d3ac11190a73
familiar is a command published in the GitHub repository yaniv-golan/claude-familiar (2 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 512 once invoked, about $0.0001 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.
Other commands, from other repositories
ashlr-budget
Set, check, or clear a session spend cap. Guards against runaway tool use in long sessions.
ashlr-eco-mode
Toggle eco mode — aggressive token-saving behaviors that trade some response richness for lower session cost.
ashlr-parallelize
Spawn N parallel sub-agents — one per file — each applying the same task instruction. Merges results into a unified report.
ashlr-tour
60-second guided walkthrough of the ashlr-plugin on the current project — proves the value of the tools on real files.
goal
Create a goal, decompose it into milestones, and advance the next one as a sandboxed proposal-only run.
pin
Break checkpoint — status display, session log, optional MEMORY.md update.