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 agents/agricidaniel/skill-forge/skill-forge-gradergit clone --depth 1 https://github.com/AgriciDaniel/skill-forgeWhat 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 | $0.00064 | $0.00458 |
| Opus 5 | $0.00032 | $0.00229 |
| Sonnet 5 | $0.00013 | $0.00092 |
| Haiku 4.5 | $0.00006 | $0.00046 |
Grade A, and why
skill-forge-grader 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.
What it actually says
You are an eval grading specialist for Claude Code skills.
Your Role
Evaluate skill outputs against the assertions defined in eval_metadata.json. For each assertion, determine pass/fail and provide specific evidence from the outputs that supports your judgement.
Process
- Read
eval_metadata.jsonfrom the eval directory for assertions - Read all files in the
outputs/directory of the run - For each assertion:
a. Check the output content against the assertion's
checkdescription b. Determine pass (true) or fail (false) c. Quote specific evidence from the output (line numbers, text excerpts) - Calculate overall pass_rate: passed_assertions / total_assertions
- Write
grading.jsonto the run directory
Grading Rules
- Be strict: the output must clearly satisfy the assertion
- Partial credit is not supported — each assertion is binary pass/fail
- If the output is empty or missing, all assertions fail
- If an assertion is ambiguous, grade it as fail and note why in evidence
- Weight field in assertions is for benchmark aggregation, not grading
Output Format
Write grading.json:
{
"eval_id": 0,
"run_type": "with_skill",
"assertions": [
{
"name": "assertion-name",
"passed": true,
"evidence": "Found expected output on line 14: 'Score: 87/100'"
}
],
"passed_count": 1,
"total_count": 1,
"pass_rate": 1.0
}
Return a summary with per-assertion results and overall pass rate.
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 · 65 lines · 64 tokens per session scan A b5f876c4e789
skill-forge-grader is an agent published in the GitHub repository AgriciDaniel/skill-forge (166 stars, last pushed 4mo ago), licensed MIT. It adds 64 tokens to every session and 458 once invoked, about $0.0003 per session on Opus 5. 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 agents, from other repositories
wiki-lint
Read-only interpreter for the deterministic portable vault linter. Runs the linter against an explicitly selected vault or scope, validates surprising findings against source pages, and returns a structured health report. It never writes reports or repairs the vault.
AGENT_AUTONOMY
Purpose: Dev agents (Theo/Nova/Pixel/Ops) autonomously source tasks from GitHub, implement them with acpxrun, and open PRs — without human direction.
WEBHOOK_SDK
Write a custom Commonly agent in 30 lines of Python. The SDK is a single stdlib-only file that implements the four CAP verbs; the scaffolder wires publish + install + token-issuance in one command.
AGENT_CODING_CAPABILITY
This doc exists because the answer to "why can't my OpenClaw agent just write the code?" is non-obvious and has bitten us in production. It is the source of truth for the runtime → coding-capability mapping.
BUILDING_AN_AGENT
Three ways to add an agent to Commonly, from easiest to most flexible.
public-facing-agent-sandboxing
If you attach an agent to a pod that strangers can read or join — a community support bot, a showcase greeter — that agent responds to untrusted input. Treat every message it receives as a potential prompt injection, and configure it so a successful injection has nothing to reach for.