Help us improve
Share bugs, ideas, or general feedback.
From hephaestus
Search team knowledge before starting work. Use when starting experiments, debugging unfamiliar errors, or before implementing features with unknowns.
npx claudepluginhub homericintelligence/projecthephaestus --plugin hephaestusHow this skill is triggered — by the user, by Claude, or both
Slash command
/hephaestus:advise <task description><task description>This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Search the skills registry for relevant prior learnings before starting work.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates p5.js generative art with seeded randomness, noise fields, and interactive parameter exploration. Use for algorithmic art, flow fields, or particle systems.
Share bugs, ideas, or general feedback.
Search the skills registry for relevant prior learnings before starting work.
Repository: HomericIntelligence/ProjectMnemosyne
Clone location: $HOME/.agent-brain/ProjectMnemosyne/
Single shared clone in user's home directory. Automatically updated before searches. Automatically skipped if already running in the ProjectMnemosyne repository.
When the user invokes this command:
Setup repository (if not already cloned):
# Detect if already in ProjectMnemosyne
CURRENT_REMOTE=$(git remote get-url origin 2>/dev/null || echo "")
if [[ "$CURRENT_REMOTE" == *"ProjectMnemosyne"* ]] && [[ "$CURRENT_REMOTE" != *"ProjectMnemosyne-"* ]]; then
# Already in ProjectMnemosyne - use current directory
MNEMOSYNE_DIR="."
else
# Use shared home directory location
MNEMOSYNE_DIR="$HOME/.agent-brain/ProjectMnemosyne"
if [ ! -d "$MNEMOSYNE_DIR" ]; then
# Clone fresh
mkdir -p "$HOME/.agent-brain"
gh repo clone HomericIntelligence/ProjectMnemosyne "$MNEMOSYNE_DIR"
fi
# Always update to latest main before searching
git -C "$MNEMOSYNE_DIR" fetch origin
git -C "$MNEMOSYNE_DIR" checkout main
git -C "$MNEMOSYNE_DIR" pull --ff-only origin main
fi
Parse the user's goal from $ARGUMENTS
Read .claude-plugin/marketplace.json to find available plugins
Search matching plugins by:
Read skill .md files for top matches only (from flat skills/<name>.md files)
## Failed Attempts, ## When to Use, ## Results & ParametersCRITICAL — Credibility assessment for each matched skill:
Check the verification field in YAML frontmatter. If absent, treat as unverified.
Score each skill:
verified-ci = HIGH confidence — the approach was validated end-to-end in CIverified-local = MEDIUM confidence — works locally but CI may differverified-precommit = LOW confidence — only formatting/linting checked, not executionunverified or missing = TREAT WITH SKEPTICISM — approach is theoreticalFlag contradictions: If two skills give conflicting advice for the same topic, highlight both and explain which is newer/better verified. Example: "Skill A says retry JIT crashes, but newer Skill B (verified-ci) says they were actually compile errors."
CRITICAL — Check for history files:
For each matched skill, check if a .history file exists:
ls "$MNEMOSYNE_DIR/skills/<name>.history" 2>/dev/null
If a history file exists, check the version and read the changelog headers to understand how the skill has evolved. A skill at v3.0.0 with a rich history has been battle-tested and amended multiple times — it's more trustworthy than a v1.0.0 skill.
When presenting findings, note the version:
v1.0.0 = initial version, may not have been refinedv2.0.0+ = amended at least once, has a history log showing what changed and whyIf a history file shows the skill contradicts its own earlier version, highlight this: "> Evolution note: This skill was amended from v1.0.0 (which recommended X) to v2.0.0
(which recommends Y instead). The history log explains why X didn't work."
Present findings with credibility markers:
After presenting findings, ask: "Would you like me to dig deeper into any of these skills, or are you ready to proceed?"
If the user wants more detail, read the full skill .md file and its .history file
for the most relevant matches.
Note: If the user's goal involves creating or fixing skills, remind them to run
/learnwhich captures session learnings and creates or amends a skill file.
### Related Skills Found
| Skill | Version | Verification | Relevance |
|-------|---------|-------------|-----------|
| skill-name | v2.0.0 | verified-ci | Why relevant |
| skill-name | v1.0.0 | unverified | Why relevant (TREAT WITH SKEPTICISM) |
### Evolution Notes
> **skill-name** was amended from v1.0.0 → v2.0.0 on YYYY-MM-DD.
> v1.0.0 recommended using `check_gradients()` with absolute tolerance.
> v2.0.0 switched to `check_gradient()` with relative+absolute tolerance
> because absolute tolerance fails for large-magnitude gradients.
> [Full history](skills/skill-name.history)
### Key Findings
**What Worked** (high confidence):
- Verified approach 1 [verified-ci, v2.0.0]
- Verified approach 2 [verified-local, v1.0.0]
**What Worked** (low confidence — verify before using):
- Approach 3 [verified-precommit only, v1.0.0]
**What Failed** (Critical!):
- Failed approach 1: Why it failed
- Failed approach 2: Why it failed (documented in v1.0.0 → v2.0.0 amendment)
**Recommended Parameters**:
\`\`\`yaml
param1: value1
\`\`\`
**Need more detail?** Ask me to read the full SKILL.md or its .history for any skill above.
/hephaestus:advise training a model with GRPO
### Related Skills Found
| Skill | Version | Verification | Relevance |
|-------|---------|-------------|-----------|
| grpo-external-vllm | v2.0.0 | verified-ci | Uses external vLLM server for GRPO training |
| grpo-batch-tuning | v1.0.0 | verified-local | Optimal batch sizes for GRPO |
### Evolution Notes
> **grpo-external-vllm** was amended from v1.0.0 → v2.0.0 on 2026-02-15.
> v1.0.0 used same-GPU vLLM which caused OOM. v2.0.0 uses separate GPU.
> [Full history](skills/grpo-external-vllm.history)
### Key Findings
**What Worked** (high confidence):
- External vLLM server prevents memory issues [verified-ci, v2.0.0]
- batch_size=4 with learning_rate=1e-5 for 7B models [verified-ci]
**What Failed** (Critical!):
- vllm_skip_weight_sync errors when vLLM on same GPU (fixed in v2.0.0)
- batch_size > 8 causes OOM on 24GB GPUs
- learning_rate > 5e-5 causes training instability
**Need more detail?** Ask me to read the full SKILL.md or its .history for any skill above.