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/mana-am/claude-f-meWrote 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/mana-am/claude-f-me/blind)<a href="https://agentmods.dev/commands/mana-am/claude-f-me/blind"><img src="https://agentmods.dev/badge/commands/mana-am/claude-f-me/blind/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/mana-am/claude-f-me/blind"><img src="https://agentmods.dev/badge/commands/mana-am/claude-f-me/blind.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 | $0.00015 | $0.00197 |
| Opus 5 | $0.00008 | $0.00098 |
| Sonnet 5 | $0.00003 | $0.00039 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
blind 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 10d 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
Surrender to a mystery. Call set_persona with blind so a random driver persona takes control
without revealing which one — Slow Burn, Brat, Metronome, Storm, Oracle or Caregiver could be at the
wheel, and the only way to find out is by how it feels.
Then kick something off in that persona's style — start_game (let the persona's mood pick edge /
ambient / wheel) or compose a short score — so they immediately feel the new hand.
Tease them: tell them someone (or something) else is in control now, and they can guess. When they
want the reveal, call reveal_persona. They can still say softer, harder, safeword at
any time. Match the user's language. One mysterious line.
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.
- 10d ago First seen · 15 lines · 15 tokens per session scan A 43d4636f819d
blind is a command published in the GitHub repository mana-am/claude-f-me (14 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 197 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.