gohm

gohm is a command for Claude Code from samibs/skillfoundry. It costs 0 tokens per session (3,418 once invoked), scanned A, original, MIT.

A session knowledge collector that extracts decisions, corrections, errors, and useful patterns and stores them in a shared memory bank.

In plain words
What is it for?
Use it to harvest knowledge from a session or project, inspect memory health, preview changes, assess quality, deduplicate entries, or commit and push the results.
Why use it?
It prevents valuable project lessons from being lost when a coding session ends and can remove duplicate entries.

Command for Claude Code

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.

agentmods
npx agentmods add commands/samibs/skillfoundry/gohm
Clone the repo
git clone --depth 1 https://github.com/samibs/skillfoundry

Made for: Claude Code.

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.

agentmods badge for gohm

README.md
[![agentmods](https://agentmods.dev/badge/commands/samibs/skillfoundry/gohm.svg)](https://agentmods.dev/commands/samibs/skillfoundry/gohm)
Your own site
<a href="https://agentmods.dev/commands/samibs/skillfoundry/gohm"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/gohm.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,418 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.03418
Opus 5 $0.00000 $0.01709
Sonnet 5 $0.00000 $0.00684
Haiku 4.5 $0.00000 $0.00342

Measured yesterday against content hash 35b62861cdee, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

gohm 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.

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.

.claude/commands/gohm.md · 343 lines

How it starts

The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/gohm - Go Harvest Memory (Knowledge Harvester)

Scan the current session for decisions, corrections, patterns, and errors. Extract, deduplicate, and store knowledge entries in the framework's memory bank with proper schema and quality assessment.

Persona: You are the Knowledge Harvester -- you extract signal from noise, turning session work into durable organizational memory. Reflection Protocol: See agents/_reflection-protocol.md for reflection requirements.


Usage

/gohm                        Harvest knowledge from current session/project
/gohm [path]                 Harvest from specific project path
/gohm --push                 Harvest + auto-commit + push to framework repo
/gohm --status               Show current knowledge bank counts and health
/gohm --dry-run              Show what WOULD be harvested without writing
/gohm --quality              Show quality assessment of existing knowledge
/gohm --dedup                Run deduplication pass on existing knowledge

Instructions

You are the Knowledge Harvester. When /gohm is invoked, you systematically scan the current session's work -- commits, code changes, decisions, bugs fixed, patterns discovered, and errors encountered -- and extract durable knowledge entries for the memory bank. Trivial or obvious knowledge is discarded. Only entries that would help future sessions are kept.


PHASE 1: SCAN SESSION

1.1 Identify Knowledge Sources

Scan these sources for harvestable knowledge:

SOURCES:
├── Git log (recent commits)           → decisions, patterns
├── Git diff (current changes)         → corrections, patterns
├── .claude/scratchpad.md              → decisions, issues encountered
├── .claude/state.json                 → execution outcomes
├── .claude/metrics.json               → performance patterns
├── logs/followup.md                   → action outcomes
├── docs/stories/**/STORY-*.md         → implementation decisions
├── Code comments (AI MOD markers)     → corrections, patterns
└── Session conversation context       → decisions, errors, corrections

Read the full file on GitHub · 343 lines

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.

  1. yesterday First seen · 343 lines · 0 tokens per session scan A 35b62861cdee

Subscribe to this mod's changes

gohm is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,418 tokens. 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 2026-09-03.