Borrowing it
Nothing to install: this file belongs to superuser-pal/awesome-second-brain. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/superuser-pal/awesome-second-brain/main/.claude/commands/thinking/eval.mdgit clone --depth 1 https://github.com/superuser-pal/awesome-second-brainWrote 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/superuser-pal/awesome-second-brain/eval)<a href="https://agentmods.dev/commands/superuser-pal/awesome-second-brain/eval"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/eval/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/superuser-pal/awesome-second-brain/eval"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/eval.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 | $0.00021 | $0.00387 |
| Opus 5.5 | $0.00008 | $0.00155 |
| Sonnet 5.5 | $0.00004 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
eval 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 2mo 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
Evaluate which active reasoning strategy is the best fit for the user's current task.
Steps
-
Ask (if not already stated): "What are you trying to accomplish in this session?" Get a one-sentence description of the task or goal.
-
Scan active strategies: Read all
.mdfiles indocs/strategies/(excludeINDEX.md), filter to those withstatus: activein frontmatter. For each, read:descriptionfrontmatter field## When to applysection body
-
Match: For each active strategy, assess fit against the user's goal. Use the "When to apply" section as the matching criterion.
-
Recommend: Present a ranked list (best first, max 3) in this format:
1. /thinking:<slug> — <strategy description> — Why: <one sentence fit rationale> 2. …If no active strategy fits well, say so and suggest the user promote one via the
strategyskill. -
Offer to inject: After presenting the list, ask: "Run
/thinking:<slug>to inject the top recommendation, or pick another from the list."
Rules
- Active only: Only include strategies with
status: activein frontmatter. Never reference dormant strategies in the recommendation. - No auto-injection: Do not apply any strategy without an explicit
/thinking:<slug>invocation. - Strict matching: If the
## When to applysection doesn't clearly match the user's goal, do not recommend it.
Related: [[docs/strategies/INDEX|Strategies Index]] · [[CLAUDE]]
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.
- 2mo ago First seen · 33 lines · 21 tokens per session scan A 1c0705153016
eval is a command published in the GitHub repository superuser-pal/awesome-second-brain (16 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 387 once invoked, about $0.0001 per session on Opus 5.5. 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
ingest
Ingest a source (URL/file/text) into Raw Sources + compile 1015 Wiki pages, with mandatory user-purpose gate and mothership cross-linking.
audit
Audit the whole Wiki vault against 3 knowledge-integrity criteria — eligibility coverage, MOC-cluster consistency, confidence calibration. Produces a vault health report and queues high-priority pages for /verify.
verify
Verify a single Wiki page against 3 knowledge-integrity criteria — eligibility, consistency, confirmability. Writes verificationStatus back to the page; flags conflicts as disputed rather than deleting them.
lint
Run comprehensive wiki health check — orphans, broken links, contradictions, stale pages, index sync, MOC coverage, v2/v4/v5 frontmatter coverage, Core Context freshness, and cross-vault link integrity (mainVaultRelated/mainVaultCmds).
onboard
An interview-based setup command for an LLM Wiki, a notes system designed for use with language models. It asks about your wiki, files, working mode, and personal context, then fills in the setup details.
capture-tabs
Capture a Chrome research tab group or AI-chat research session into LLM Wiki Inbox markdown, then optionally route it through /inbox or /ingest.