An append-only record of skill-improvement runs, with searches, summary counts, progress reports, and trend charts. Append-only means earlier entries are preserved rather than edited or deleted.

Use it to save run results, find earlier runs by keyword, count KEEP and DISCARD outcomes, and chart scores across experiments.

It keeps experiment history trustworthy and makes progress visible over time. It also reports when there are no completed runs instead of creating an empty chart.

os-lab-log — Skill for Claude Code by richfrem/agent-plugins-skills

Install

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.

Any agent

npx skills add richfrem/agent-plugins-skills --skill os-lab-log

Clone the repo

git clone --depth 1 https://github.com/richfrem/agent-plugins-skills

Made for: Claude Code.

Or install agent-agentic-os, the plugin that ships this one along with the rest of its 14 skills, 2 agents, 3 hooks.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/os-lab-log/github.svg)](https://agentmods.dev/skills/richfrem/agent-plugins-skills/os-lab-log)

Your own site

<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/os-lab-log"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/os-lab-log/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/os-lab-log"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/os-lab-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 205 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,156 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00205	$0.01156
Opus 5.5	$0.00082	$0.00462
Sonnet 5.5	$0.00041	$0.00231
Haiku 4.5	$0.00020	$0.00116

Measured yesterday against content hash 169f9a1050ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

os-lab-log 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 yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/experiment_log.py, scripts/generate_report.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/agent-agentic-os/skills/os-lab-log/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OS Lab Log

One skill for the lab's record keeping: write a run to the log, search the log, summarize it, and chart it.

Constraints
Source types
Quick start
Workflow
Verification
References

Constraints

The log is append-only. Never edit or delete prior entries in context/experiment-log/.
Check result_type in an entry header before parsing it. survey entries are mixed.
Write the entry file and update index.md in the same step.
Reports are read-only views: write charts and summaries only to context/memory/reports/, using timestamped filenames.
Primary data for charts is context/experiment-log/index.md (numeric rows) and per-skill evals/results.tsv; treat context/memory/improvement-ledger.md as a legacy fallback.
If there are no numeric entries or completed cycles, report "No cycles completed yet" and do not render an empty chart.

Source types and result kinds

`--source-type`	`result_type`	Holds
`orchestrator`	numeric	Best score, baseline, delta, KEEP/DISCARD counts (a loop or scorer run)
`tester`	qualitative	Scenario pass/fail results
`verifier`	qualitative	PASS/PARTIAL/FAIL counts and handoff block validity
`planner`	qualitative	Workstreams and identified gaps
`survey`	mixed	Friction counts and north-star metrics

Quick start

# Aggregate counts across all logged runs
python3 scripts/experiment_log.py summary

# Progress chart and text summary for the workspace
python3 scripts/generate_report.py --project-dir . --plugin-dir <path-to-agent-agentic-os>

Workflow

Pick the mode.
- append: persist a completed run.
- query <term>: search logged runs by keyword (session id, target, FAIL, numeric).
- summary: aggregate counts by source type.
- report: chart improvement across cycles (optionally --skill <name>).
Append right after a run, before temp/ is cleared:
```
python3 scripts/experiment_log.py append --source-type <type> --report <path> \
  --session-id <id> --target <target> --triggered-by <caller>
```
survey needs no --report; every other type requires it. Fill in the Actions Taken placeholder afterwards.
Confirm the entry appears in context/experiment-log/index.md with a valid header.
Report: read numeric rows from the index and per-skill results.tsv, then run generate_report.py. It writes progress_<timestamp>.png and summary_<timestamp>.md to context/memory/reports/. Show the chart path, quote the highlights, and offer a per-skill drill-down with --skill.
Interpret with chart-reading-guide.md: gray dots are DISCARD, green dots are KEEP, the green step line is the running best.

Read the full file on GitHub · 93 lines

Files

What ships with it

11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

yesterday First seen · 93 lines · 205 tokens per session scan A 169f9a1050ff

Subscribe to this mod's changes

os-lab-log is a skill published in the GitHub repository richfrem/agent-plugins-skills (7 stars, last pushed 2d ago), licensed MIT. It adds 205 tokens to every session and 1,156 once invoked, about $0.0008 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 2026-10-16.

Analytics and metrics Building agents agent-evaluation agent-orchestration agentic-control-plane agentic-os agentic-workflows ai-agent-governance

os-lab-log

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

OS Lab Log

Contents

Constraints

Source types and result kinds

Quick start

Workflow

What ships with it

What this file has done since we first saw it

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