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 skills/jacobjandon/sicry/openclaw_skillnpx skills add JacobJandon/Sicry --skill openclaw_skillgit clone --depth 1 https://github.com/JacobJandon/SicryWhat 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 | $0.00627 |
| Opus 5 | $0.00000 | $0.00313 |
| Sonnet 5 | $0.00000 | $0.00125 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
openclaw_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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 57 lines · 0 tokens per session scan A cd4c663559f1
openclaw_skill is a skill published in the GitHub repository JacobJandon/Sicry (19 stars, last pushed 2mo ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 627 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 skills, from other repositories
onionclaw
Search the Tor dark web, fetch .onion hidden service pages, rotate Tor identity, and run structured OSINT investigations. Use when user asks to search dark web, investigate .onion sites, find if data appeared on dark web, conduct Tor-based OSINT, look up dark web leaks, fetch any .onion URL, check for leaked…
analyzing-certificate-transparency-for-phishing
Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
analyzing-apt-group-with-mitre-navigator
Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator…
analyzing-malware-family-relationships-with-malpedia
Query the Malpedia API to look up malware family aliases and naming (platform.familyname), pull community/vendor YARA rules, link families to threat actors, and map family relationships such as loader-payload chains and shared authorship. Use when researching a malware family's aliases, lineage, or actor attribution…
analyzing-campaign-attribution-evidence
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use…
analyzing-indicators-of-compromise
Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…