Milestr Dashboard

📊

test-agent heartbeat tokens per completed LLM session

321289 tokens ↑

Source: state.db — cron_3c4b5e1c2b99_20260720_062942 · Jul 20 13:38Z

Updated 7/20/2026, 2:15:08 PM

📈

test-agent heartbeat tokens (3-session rolling avg)

145680 tokens →

Source: state.db — 3 completed sessions [cron_3c4b5e1c2b99_20260720_062942=321289, ...042450=71999, ...022145=43751]

Updated 7/20/2026, 2:15:08 PM

📐

Loop output verbosity ratio (chars/tiktoken)

3.24 chars/tiktoken →

Source: 28-loop average from milestr/docs/K3-baseline.json (chars per tiktoken in loop output file)

Updated 7/19/2026, 1:22:00 AM

📐

Loop output verbosity (chars/tiktoken in .md file)

3.24 chars/tiktoken →

Source: 28-loop average from milestr/docs/K3-baseline.json

Updated 7/19/2026, 1:22:00 AM

⚠️

Heartbeat runs since last token-reduction action

1 runs ↑

Source: Counted from memory/loops/*.md — last concrete action was I5 state.db migration (Jul 19)

Updated 7/19/2026, 10:13:33 PM

Initiatives & Tasks

📋 I1 Instrument token measurement for agent invocations done 100% ROOT

📋 I2 Establish baseline token usage across agents done 100% ROOT

📋 I3 Identify and optimize high-token prompts and tool usage done 93% ROOT

📋 I4 Implement token-saving practices and monitor impact done 91% ROOT

📋 I5 Migrate K1 to state.db ground-truth measurement done 100% ROOT

📋 I6 Normalize K1: tokens-per-loop-output-byte ratio done 100% ROOT

🔧 I5.1 Build state-db-query.py for ground-truth K1 done 100% I5

🔌 I5.2 Wire state-db-query.py into loop report generator done 100% I5

✅ I5.3 5-loop K1 migration validation (old tiktoken vs new state.db) done 100% I5

📝 I6.1 Document K3 metric semantics + drift threshold done 100% I6

📊 I6.2 Build 30-loop K3 ratio baseline (chars/tiktoken) done 100% I6

🤔 I6.3 Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric? done 100% I6

🪝 A1 Deploy global post_api_request hook for per-agent token tracking done 100% ROOT

🔍 A2 Identify top-3 token-burning agents via agent_tokens.json done 100% ROOT

⏱️ A3 Reduce heartbeat frequency from 60m to 15m or disable done 100% ROOT

✂️ A4 Audit + trim system prompt files for token waste done 100% ROOT

🤖 A5 Implement script-only heartbeat (no LLM) for maintenance monitoring done 100% ROOT

🛡️ A6 Fix stale KPI problem: K1 source session ID + timestamp required done 100% ROOT

📊 A7 Find and fix the most recent token reduction with before/after evidence done 100% ROOT

not started (0)

analyzing (0)

ongoing (0)

done (20)

🧪

Constantly reduce the token usage of the Hermes system

ROOT |

📋

Instrument token measurement for agent invocations

I1 | ROOT

📋

Establish baseline token usage across agents

I2 | ROOT

📋

Identify and optimize high-token prompts and tool usage

I3 | ROOT

📋

Implement token-saving practices and monitor impact

I4 | ROOT

📋

Migrate K1 to state.db ground-truth measurement

I5 | ROOT

📋

Normalize K1: tokens-per-loop-output-byte ratio

I6 | ROOT

🔧

Build state-db-query.py for ground-truth K1

I5.1 | I5

🔌

Wire state-db-query.py into loop report generator

I5.2 | I5

✅

5-loop K1 migration validation (old tiktoken vs new state.db)

I5.3 | I5

📝

Document K3 metric semantics + drift threshold

I6.1 | I6

📊

Build 30-loop K3 ratio baseline (chars/tiktoken)

I6.2 | I6

🤔

Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric?

I6.3 | I6

🪝

Deploy global post_api_request hook for per-agent token tracking

A1 | ROOT

🔍

Identify top-3 token-burning agents via agent_tokens.json

A2 | ROOT

⏱️

Reduce heartbeat frequency from 60m to 15m or disable

A3 | ROOT

✂️

Audit + trim system prompt files for token waste

A4 | ROOT

🤖

Implement script-only heartbeat (no LLM) for maintenance monitoring

A5 | ROOT

🛡️

Fix stale KPI problem: K1 source session ID + timestamp required

A6 | ROOT

📊

Find and fix the most recent token reduction with before/after evidence

A7 | ROOT

blocked (0)