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 skills add cyborg-garden/hermes-agent-mt --skill osint-investigationgit clone --depth 1 https://github.com/cyborg-garden/hermes-agent-mtWrote 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.
[](https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/osint-investigation)<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/osint-investigation"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/osint-investigation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/osint-investigation"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/osint-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.02884 |
| Opus 5 | $0.00045 | $0.01442 |
| Sonnet 5 | $0.00018 | $0.00577 |
| Haiku 4.5 | $0.00009 | $0.00288 |
Grade A, and why
osint-investigation scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `fetch_*.py` scripts use `urllib.request` and respect `Retry-After`. Heavy This is a copy
94% identical to osint-investigation — 33 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OSINT Investigation — Public Records Cross-Reference
Investigative framework for public-records OSINT: government contracts, corporate filings, lobbying, sanctions, offshore leaks, property records, court records, web archives, knowledge bases, and global news. Resolve entities across heterogeneous sources, build cross-links with explicit confidence, run statistical timing tests, and produce structured evidence chains.
Python stdlib only. Zero install. Works on Linux, macOS, Windows. Most sources work with no API key (OpenCorporates has an optional free token that raises rate limits).
Adapted from the MIT-licensed ShinMegamiBoson/OpenPlanter project; expanded to cover identity / property / litigation / archives / news sources that the original didn't address.
When to use this skill
Use when the user asks for:
- "follow the money" — government contracts, lobbying → legislation, sanctions
- corporate due diligence — who controls company X, where are they incorporated, who serves on their boards, what filings have they made
- sanctions screening — is entity X on OFAC SDN, ICIJ offshore leaks
- pay-to-play investigation — contractors with offshore ties, lobbying clients winning awards
- property ownership — find recorded deeds/mortgages by name or address (NYC; for other counties point users at the relevant recorder)
- litigation history — find federal + state court opinions and PACER dockets
- multi-source entity resolution where naming varies (LLC suffixes, abbreviations)
- evidence-chain construction with explicit confidence levels
- "what's been said about X" — international news (GDELT) + Wikipedia narrative + Wayback Machine to recover dead URLs
Do NOT use this skill for:
- general web research →
web_search/web_extract - domain/infrastructure OSINT →
domain-intelskill - academic literature →
arxivskill - social-media profile discovery →
sherlockskill (optional) - US federal campaign finance — FEC is intentionally NOT covered here (the API is unreliable for ad-hoc contributor-name queries on the free DEMO_KEY tier). For federal donations, point users at https://www.fec.gov/data/ directly.
What ships with it
28 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/sources/courtlistener.md 3.4 KB
- references/sources/gdelt.md 3.8 KB
- references/sources/icij-offshore.md 4.3 KB
- references/sources/nyc-acris.md 3.5 KB
- references/sources/ofac-sdn.md 3.5 KB
- references/sources/opencorporates.md 3.9 KB
- references/sources/sec-edgar.md 3.1 KB
- references/sources/senate-ld.md 3.3 KB
- references/sources/usaspending.md 3.8 KB
- references/sources/wayback.md 3.1 KB
- references/sources/wikipedia.md 4.1 KB
- scripts/_http.py 2.8 KB runs code
- scripts/_normalize.py 2.0 KB runs code
- scripts/build_findings.py 7.8 KB runs code
- scripts/entity_resolution.py 6.7 KB runs code
- scripts/fetch_courtlistener.py 4.9 KB runs code
- scripts/fetch_gdelt.py 5.3 KB runs code
- scripts/fetch_icij_offshore.py 8.4 KB runs code
- scripts/fetch_nyc_acris.py 6.4 KB runs code
- scripts/fetch_ofac_sdn.py 5.4 KB runs code
- scripts/fetch_opencorporates.py 6.6 KB runs code
- scripts/fetch_sec_edgar.py 6.6 KB runs code
- scripts/fetch_senate_ld.py 4.9 KB runs code
- scripts/fetch_usaspending.py 5.4 KB runs code
- scripts/fetch_wayback.py 4.4 KB runs code
- scripts/fetch_wikipedia.py 9.2 KB runs code
- scripts/timing_analysis.py 9.0 KB runs code
- templates/source-template.md 1.2 KB
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.
- 9d ago First seen · 278 lines · 90 tokens per session scan A c75b5a30a197
osint-investigation is a skill published in the GitHub repository cyborg-garden/hermes-agent-mt (13 stars, last pushed 5d ago), licensed MIT. It adds 90 tokens to every session and 2,884 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to osint-investigation, differing in 33 lines, and is treated as a copy.
Other skills, from other repositories
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
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mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
see-invisibility
In D&D, See Invisibility lets you see creatures and objects that have been made invisible. The real-world version is revealing deliberate obscurity: finding the hidden costs in a pricing page, uncovering the actual terms buried in a EULA, identifying the obfuscated tracking in a codebase, or surfacing the real…
audit-verify
Verify CocoAudit event integrity and contract evidence for local CocoPlus artifacts.
assess-ip-landscape
Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when…