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.
npx agentmods add commands/javanc/homunculus/eval-skillgit clone --depth 1 https://github.com/JavanC/HomunculusWhat 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 | $0.00000 | $0.01246 |
| Opus 5 | $0.00000 | $0.00623 |
| Sonnet 5 | $0.00000 | $0.00249 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
eval-skill 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/eval-skill — Evaluate an Evolved Skill
Run scenario-based tests on a skill to measure its quality.
Steps
- List eval specs:
ls homunculus/evolved/evals/*.eval.yaml 2>/dev/null - If user specified a skill name, use that eval spec; otherwise let user choose
- Read the skill file (
homunculus/evolved/skills/<name>.md) and its eval spec
Evaluation
For each scenario, act as a developer who doesn't know the answer — only reference the skill document. Then compare against expected_behavior and anti_patterns.
Results
| Result | Condition |
|---|---|
| PASS | Skill guides all expected behaviors, no anti-patterns triggered |
| PARTIAL | Skill guides some expected behaviors, or misses important details |
| FAIL | Skill fails to guide correct behavior, or would cause anti-patterns |
| GAP | Scenario knowledge is completely absent from skill |
Report Format
🔬 Skill Eval: <name> v<version>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Scenario Result Notes
──────────────────────────────────────
<scenario.name> PASS -
<scenario.name> PARTIAL Missing X
<scenario.name> FAIL Would cause Y
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Pass rate: X/Y (Z%)
Grade: ⭐⭐⭐⭐⭐ (>= 90)
Noise Tolerance
5pp rule: score delta < 5pp is statistical noise, not a real change. From Anthropic infrastructure noise research — environment variance alone can cause ±5pp swings.
|delta| < 5pp→not_significant(treat as flat)delta >= 5pp→ real improvementdelta <= -5pp→ real regression
Multiple-Run Modes
Three optional flags reduce measurement noise. Use independently or combined.
--runs N (eliminate infra noise)
/eval-skill my-skill --runs 3 — runs the full eval N times independently, reports mean ± σ.
- Eliminates session-to-session infrastructure variance
- Recommended for nightly agent evals:
--runs 3 - Daily manual evals: default N=1 is fine
📊 Multi-Run Summary (runs=3):
Run 1: 85% Run 2: 87% Run 3: 83%
Mean: 85.0% | σ: 1.6pp
Verdict: not_significant vs baseline 84% (delta=1pp < 5pp threshold)
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.
- 2d ago First seen · 138 lines · 0 tokens per session scan A 59087a32857f
eval-skill is a command published in the GitHub repository JavanC/Homunculus (15 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,246 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.