How do I install the Os Experiment Log skill?

Run npx skillmds@latest add richfrem/os-experiment-log 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 Experiment Log 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 Experiment Log?

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 Experiment Log free to use?

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

Who published Os Experiment Log?

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

Os Experiment Log by richfrem — Agent Skill

Name: Os Experiment Log
Rating: 4.666666666666667 (3 reviews)
Author: richfrem

Os Experiment Log

Maintains a persistent, folder-based log of all agentic-os experiment runs. Each run writes one dated file to context/experiment-log/ and updates index.md. Supports five source types: verifier (qualitative), tester (qualitative), orchestrator (numeric), planner (qualitative), survey (mixed). Handles both numeric results (eval scores, KEEP/DISCARD, delta) and qualitative results (PASS/FAIL/PARTIAL, gap analysis). Use after any experiment run to persist findings before temp/ is cleared.

richfrem f0d7a2f 6 files · 4.4 KB Updated Sep 10, 2026

File contents

Overview

The experiment log is the unified cross-cutting record for all agentic-os experiments. One file per run, all files in context/experiment-log/, with index.md as a queryable table of all runs.

context/experiment-log/
  index.md                                     ← one row per run (date, source, target, verdict)
  2026-04-25-verifier-os-architect-round1.md   ← from os-evolution-verifier
  2026-04-25-tester-os-architect.md            ← from os-architect-tester
  2026-04-25-os-improvement-loop-os-eval-runner.md    ← from os-improvement-loop
  2026-04-25-planner-0024.md                   ← from os-evolution-planner
  2026-04-25-survey-session.md                 ← from post_run_survey

Source Types and Result Kinds

Agents must check result_type in a log entry's header before parsing it:

`--source-type`	Produced by	`result_type`	Key fields
`verifier`	os-evolution-verifier	`qualitative`	PASS/PARTIAL/FAIL counts, HANDOFF_BLOCK validity
`tester`	os-architect-tester	`qualitative`	AC-1–4 pass/fail per scenario
`orchestrator`	os-improvement-loop	`numeric`	best_score, baseline, delta, KEEP/DISCARD counts
`planner`	os-evolution-planner	`qualitative`	workstream count, gaps identified
`survey`	post_run_survey	`mixed`	friction item count, north_star metric

Numeric entries (result_type: numeric) carry quantitative metrics suitable for trending and charting. Qualitative entries (result_type: qualitative) carry pass/fail verdicts and gap analysis prose. Mixed entries (result_type: mixed) carry both — agents must check which fields are present before parsing.

Phase 1 — Resolve Mode

Read the argument or invocation context to determine mode:

append --source-type TYPE: log a new run from a completed experiment
query <term>: search all files in context/experiment-log/ by keyword
summary: print aggregate stats across all runs, broken down by source type

Phase 2 — Execute

Call scripts/experiment_log.py append --source-type <type> --report <path> --session-id <id> --target <target> --triggered-by <caller> for each of the five source types, or query <term> / summary for lookups. Full example invocations for every source type are in references/detailed-reference.md.

Phase 3 — Confirm and Report

After append: run tail -5 context/experiment-log/index.md and report the logged filename. After query: relay matching file names and their header blocks (date, source, target, verdict). After summary: print the per-source-type breakdown verbatim.

Log Entry Format, Smoke Tests, and Gotchas

The exact YAML-header + report file format, three smoke tests, and gotchas (mixed result-type parsing, temp/ ephemerality, human-filled Actions Taken, intentional append-only duplicate rows) are in references/detailed-reference.md.

---name: os-experiment-logdescription: Maintains a persistent, folder-based log of all agentic-os experiment runs. Each run writes one dated file to context/experiment-log/ and updates index.md. Supports five source types: verifier (qualitative), tester (qualitative), orchestrator (numeric), planner (qualitative), survey (mixed). Handles both numeric results (eval scores, KEEP/DISCARD, delta) and qualitative results (PASS/FAIL/PARTIAL, gap analysis). Use after any experiment run to persist findings before temp/ is cleared.---## OverviewThe experiment log is the unified cross-cutting record for all agentic-os experiments.One file per run, all files in `context/experiment-log/`, with `index.md` as aqueryable table of all runs.```context/experiment-log/ index.md ← one row per run (date, source, target, verdict) 2026-04-25-verifier-os-architect-round1.md ← from os-evolution-verifier 2026-04-25-tester-os-architect.md ← from os-architect-tester 2026-04-25-os-improvement-loop-os-eval-runner.md ← from os-improvement-loop 2026-04-25-planner-0024.md ← from os-evolution-planner 2026-04-25-survey-session.md ← from post_run_survey```## Source Types and Result KindsAgents must check `result_type` in a log entry's header before parsing it:| `--source-type` | Produced by | `result_type` | Key fields ||---|---|---|---|| `verifier` | os-evolution-verifier | `qualitative` | PASS/PARTIAL/FAIL counts, HANDOFF_BLOCK validity || `tester` | os-architect-tester | `qualitative` | AC-1–4 pass/fail per scenario || `orchestrator` | os-improvement-loop | `numeric` | best_score, baseline, delta, KEEP/DISCARD counts || `planner` | os-evolution-planner | `qualitative` | workstream count, gaps identified || `survey` | post_run_survey | `mixed` | friction item count, north_star metric |**Numeric entries** (`result_type: numeric`) carry quantitative metrics suitable for trending and charting.**Qualitative entries** (`result_type: qualitative`) carry pass/fail verdicts and gap analysis prose.**Mixed entries** (`result_type: mixed`) carry both — agents must check which fields are present before parsing.## Phase 1 — Resolve ModeRead the argument or invocation context to determine mode:- **`append --source-type TYPE`**: log a new run from a completed experiment- **`query <term>`**: search all files in `context/experiment-log/` by keyword- **`summary`**: print aggregate stats across all runs, broken down by source type## Phase 2 — ExecuteCall `scripts/experiment_log.py append --source-type <type> --report <path> --session-id <id>--target <target> --triggered-by <caller>` for each of the five source types, or `query <term>`/ `summary` for lookups. Full example invocations for every source type are in`references/detailed-reference.md`.## Phase 3 — Confirm and ReportAfter `append`: run `tail -5 context/experiment-log/index.md` and report the logged filename.After `query`: relay matching file names and their header blocks (date, source, target, verdict).After `summary`: print the per-source-type breakdown verbatim.## Log Entry Format, Smoke Tests, and GotchasThe exact YAML-header + report file format, three smoke tests, and gotchas (mixed result-typeparsing, temp/ ephemerality, human-filled Actions Taken, intentional append-only duplicate rows)are in `references/detailed-reference.md`.

Frequently asked questions