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/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/dashboard)<a href="https://agentmods.dev/commands/naimkatiman/continuous-improvement/dashboard"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/dashboard/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/naimkatiman/continuous-improvement/dashboard"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/dashboard.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 | $0.00014 | $0.00691 |
| Opus 5.5 | $0.00006 | $0.00276 |
| Sonnet 5.5 | $0.00003 | $0.00138 |
| Haiku 4.5 | $0.00001 | $0.00069 |
Grade A, and why
dashboard 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instinct Dashboard
Generate a visual dashboard for this project's continuous-improvement status.
Instructions
- Find project hash: Run
git rev-parse --show-toplevel 2>/dev/null, then SHA-256 first 12 chars - Read observations: Count lines in
~/.claude/instincts/<hash>/observations.jsonl - Read instincts: Load all
*.yamlfiles from project dir +global/ - Read instinct packs: Check if any packs from
instinct-packs/have been loaded
Display Format
╔══════════════════════════════════════════════════════════════╗
║ continuous-improvement Dashboard ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ Project: <name> Level: <CAPTURE|ANALYZE|...> ║
║ Sessions: ~<obs/10> Mode: <beginner|expert> ║
║ ║
║ ┌─ Observations ────────────────────────────────────────┐ ║
║ │ Total: <n> Unprocessed: <n> Last: <date> │ ║
║ └───────────────────────────────────────────────────────┘ ║
║ ║
║ ┌─ Instincts ───────────────────────────────────────────┐ ║
║ │ Total: <n> │ ║
║ │ ████████░░ Auto-apply (0.7+): <n> │ ║
║ │ █████░░░░░ Suggest (0.5-0.69): <n> │ ║
║ │ ██░░░░░░░░ Silent (< 0.5): <n> │ ║
║ │ Global: <n> Project: <n> │ ║
║ └───────────────────────────────────────────────────────┘ ║
║ ║
║ ┌─ Top Instincts ───────────────────────────────────────┐ ║
║ │ <list top 5 instincts by confidence with bars> │ ║
║ └───────────────────────────────────────────────────────┘ ║
║ ║
║ ┌─ Health ──────────────────────────────────────────────┐ ║
║ │ Stale (30+ days): <n> Decaying: <n> │ ║
║ │ Recently reinforced: <n> │ ║
║ └───────────────────────────────────────────────────────┘ ║
║ ║
╚══════════════════════════════════════════════════════════════╝
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.
- 1mo ago First seen · 57 lines · 14 tokens per session scan A 286cdd102180
dashboard is a command published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 691 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 2026-08-31.
Other commands, from other repositories
reflect-promote
Promote learnings from skills to global CLAUDE.md.
rem_help
Each section is printed verbatim by --help for the command it names. The agreed design is issue #1656; change a page there and here together.
memory
Read-side memory operations on the knowledge graph: search past decisions and patterns, load session context, view decision timelines, render Mermaid graph visualizations. Subcommands: search, load, history, viz, status. Use when finding or reviewing what memory already holds. For storing new knowledge instead, invoke…
compact-prep
Ask the agent to prepare for conversation compaction by updating any relevant state and providing guidance for the compaction agent and to kick off the session there after.
fire-reflect
After any failure (debug resolution, test failure, approach rotation, stalled loop), capture what was tried, why it failed, and what actually worked as a persistent reflection. Future sessions search these before investigating.
sync
Mid-session context refresh command. Keeps context fresh and consistent during work.