How do I install the Os Improvement Report skill?

Run npx skillmds@latest add richfrem/os-improvement-report in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Os Improvement Report safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Os Improvement Report?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Os Improvement Report free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Os Improvement Report?

richfrem (@richfrem) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Os Improvement Report by richfrem — Agent Skill

Name: Os Improvement Report
Rating: 4.5 (4 reviews)
Author: richfrem

Os Improvement Report

Trigger with "show me the improvement chart", "how are we improving", "progress report", "graph the eval scores", "show cycle of improvement", "what's the trend", "are we getting better". Produces a visual/text summary of how the agentic loop is improving across cycles. Do NOT use this to run the learning loop or evaluate a specific skill change.

richfrem 284a194 18 files · 7.2 KB Updated Sep 10, 2026

File contents

Loop Progress Report

Visual and text reporting on the agentic loop improvement cycle — across any plugin that maintains an improvement-ledger.md and results.tsv per skill.

The reference output is the autoresearch progress chart: green KEEP dots on a timeline, gray DISCARD dots, running-best step line, annotations showing what each improvement was. This skill produces the same chart for agentic-os and exploration-cycle-plugin improvement cycles.

Dependencies (Python 3.8+, pandas, matplotlib) are in references/detailed-reference.md.

What It Reads

Source	Priority	Content
`context/experiment-log/index.md`	Primary	All logged runs; filter `result_type: numeric` for KEEP/DISCARD/score data from orchestrator runs
`context/memory/improvement-ledger.md`	Legacy fallback	Eval score progression written by os-improvement-loop Stage 4.7; used if experiment log has no numeric entries
`.agents/skills/*/evals/results.tsv`	Supplement	Per-skill detailed eval score history

The experiment log is the unified source of truth for numeric results. The improvement ledger is a legacy format maintained for backward compatibility with older loop runs.

What It Produces

Output	Description
`context/memory/reports/progress_YYYYMMDD_HHMM.png`	Progress chart: KEEP/DISCARD timeline, running-best step line, change annotations
`context/memory/reports/summary_YYYYMMDD_HHMM.md`	Text summary: baseline vs best, top hits by delta, survey effectiveness, north star trend

Execution Flow

Read experiment log for numeric entries — run experiment_log.py summary, filter context/experiment-log/index.md for Result Type: numeric rows, and parse each linked file's KEEP/DISCARD verdict string. Fall through to Phase 1 if no numeric entries exist. Full parsing detail in references/detailed-reference.md.
Check legacy data availability (fallback only) — if context/memory/improvement-ledger.md is missing or its Section 1 table is empty, tell the user no cycles have completed yet rather than running the report on an empty ledger.
Run the report — invoke generate_report.py --project-dir ... --plugin-dir ... (optionally --skill <name>). Exits 0 and prints the chart path + text summary.
Surface the output — report the chart path, print the text summary inline, and ask whether to open the chart image or show per-skill detail.
Cross-plugin reporting (optional) — if tracking both agent-agentic-os and exploration-cycle-plugin, run the report once per plugin's project dir and concatenate the text summaries. Full commands in references/detailed-reference.md.

How to read the resulting chart, and how any other plugin can plug into this report via the three-section ledger format, are in references/detailed-reference.md.

References

improvement-ledger-spec.md — ledger format, writing protocol, initialization
chart-reading-guide.md — how to interpret KEEP/DISCARD dots, step line, and text summary fields
os-improvement-loop SKILL — Stage 4.7 writes to the ledger
test-scenarios-seed.md — 50 pre-designed test hypotheses
post_run_survey.md — survey template (Section 2 trace sources)

---name: os-improvement-reportdescription: Trigger with "show me the improvement chart", "how are we improving", "progress report", "graph the eval scores", "show cycle of improvement", "what's the trend", "are we getting better". Produces a visual/text summary of how the agentic loop is improving across cycles. Do NOT use this to run the learning loop or evaluate a specific skill change.---# Loop Progress ReportVisual and text reporting on the agentic loop improvement cycle — across any plugin thatmaintains an `improvement-ledger.md` and `results.tsv` per skill.The reference output is the autoresearch progress chart: green KEEP dots on a timeline,gray DISCARD dots, running-best step line, annotations showing what each improvement was.This skill produces the same chart for agentic-os and exploration-cycle-plugin improvement cycles.Dependencies (Python 3.8+, pandas, matplotlib) are in `references/detailed-reference.md`.## What It Reads| Source | Priority | Content ||--------|----------|---------|| `context/experiment-log/index.md` | **Primary** | All logged runs; filter `result_type: numeric` for KEEP/DISCARD/score data from orchestrator runs || `context/memory/improvement-ledger.md` | Legacy fallback | Eval score progression written by os-improvement-loop Stage 4.7; used if experiment log has no numeric entries || `.agents/skills/*/evals/results.tsv` | Supplement | Per-skill detailed eval score history |The experiment log is the unified source of truth for numeric results. The improvement ledgeris a legacy format maintained for backward compatibility with older loop runs.## What It Produces| Output | Description ||--------|-------------|| `context/memory/reports/progress_YYYYMMDD_HHMM.png` | Progress chart: KEEP/DISCARD timeline, running-best step line, change annotations || `context/memory/reports/summary_YYYYMMDD_HHMM.md` | Text summary: baseline vs best, top hits by delta, survey effectiveness, north star trend |## Execution Flow1. **Read experiment log for numeric entries** — run `experiment_log.py summary`, filter `context/experiment-log/index.md` for `Result Type: numeric` rows, and parse each linked file's KEEP/DISCARD verdict string. Fall through to Phase 1 if no numeric entries exist. Full parsing detail in `references/detailed-reference.md`.2. **Check legacy data availability (fallback only)** — if `context/memory/improvement-ledger.md` is missing or its Section 1 table is empty, tell the user no cycles have completed yet rather than running the report on an empty ledger.3. **Run the report** — invoke `generate_report.py --project-dir ... --plugin-dir ...` (optionally `--skill <name>`). Exits 0 and prints the chart path + text summary.4. **Surface the output** — report the chart path, print the text summary inline, and ask whether to open the chart image or show per-skill detail.5. **Cross-plugin reporting (optional)** — if tracking both `agent-agentic-os` and `exploration-cycle-plugin`, run the report once per plugin's project dir and concatenate the text summaries. Full commands in `references/detailed-reference.md`.How to read the resulting chart, and how any other plugin can plug into this report via thethree-section ledger format, are in `references/detailed-reference.md`.## References- [improvement-ledger-spec.md](../../references/memory/improvement-ledger-spec.md) — ledger format, writing protocol, initialization- [chart-reading-guide.md](references/operations/chart-reading-guide.md) — how to interpret KEEP/DISCARD dots, step line, and text summary fields- [os-improvement-loop SKILL](../os-improvement-loop/SKILL.md) — Stage 4.7 writes to the ledger- [test-scenarios-seed.md](../../references/testing/test-scenarios-seed.md) — 50 pre-designed test hypotheses- [post_run_survey.md](../../references/memory/post_run_survey.md) — survey template (Section 2 trace sources)

Frequently asked questions