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 giggsoinc/raven --skill raven-hardengit clone --depth 1 https://github.com/giggsoinc/ravenWrote 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/giggsoinc/raven/raven-harden)<a href="https://agentmods.dev/skills/giggsoinc/raven/raven-harden"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/raven-harden/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/giggsoinc/raven/raven-harden"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/raven-harden.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 | $0.00061 | $0.00926 |
| Opus 5.5 | $0.00024 | $0.00370 |
| Sonnet 5.5 | $0.00012 | $0.00185 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
raven-harden 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 1mo 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/raven-harden
Turns session observations into permanent Raven intelligence. Run when prompted by Task-Observer or whenever you want to solidify what's been learned.
Steps
1. Read the Log
Read: docs/observations/security_log.md
Find: all entries with Status: open
Find: all entries with Status: promotion-candidate
Count: total open, total candidates
Report summary before doing anything:
Raven Harden — Review
══════════════════════════════════════
Open observations: [N]
Promotion candidates: [N platform(s)]
Last hardened: [date or never]
Open observations:
[date] [platform] — [one-line summary]
...
Promotion candidates:
[platform] — used [N] times, [N] log entries
...
══════════════════════════════════════
What would you like to do?
[A] Harden all open observations
[S] Select which to harden
[P] Promote candidates to curated skills
[N] Nothing — just reviewing
Wait for user input before proceeding.
2. Harden Observations → CLAUDE.md
For each approved observation:
Assess where it belongs:
- Rule that should never be broken → add to CLAUDE.md Non-Negotiable Rules
- Platform-specific Giggso pattern → add to docs/knowledge/internal_raven_ops.md
- General engineering pattern → add to docs/knowledge/general_security_patterns.md
CLAUDE.md hardening — append to relevant section:
## Hardened Rules (from observations)
| Date | Rule | Source |
|---|---|---|
| [date] | [rule in one line] | [platform / observation type] |
Mark entry in log:
Status: hardened → [date]
3. Promote Candidates → Curated Skills
For each platform with Status: promotion-candidate:
1. Read all log entries for that platform
2. Read cache: .raven/.cache/dynamic-skills/[platform-slug].md
3. Assemble SKILL.md using accumulated profile
Template for promoted skill:
---
name: [platform]-specialist
description: [generated from log observations]
allowed-tools: Agent, WebSearch, Read, Write
---
# [Platform] Specialist — [Expert Name]
**Expert persona:** [from cache]
**Promoted from:** dynamic-specialist after [N] sessions
**Last updated:** [date]
## Core Focus
[from cache + log]
## Key Rules (from observations)
[all confirmed patterns from log entries]
## Known Gotchas (confirmed in practice)
[gotchas from log entries]
## Agent Chain
Step 1 → check guard agents relevant to this platform
Step 2 → [platform] expertise below
Step 3 → search agent ON DEMAND only
[rest of specialist content from cache profile]
---
4. Write to: core/skills/[platform]-specialist/SKILL.md
5. Mark log entries: Status: promoted → [date]
6. Commit to giggsoinc/raven and push
7. raven-sync automatically distributes to all registered projects
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.
- 1mo ago First seen · 151 lines · 61 tokens per session scan A 1c53232d2a96
raven-harden is a skill published in the GitHub repository giggsoinc/raven (5 stars, last pushed 13d ago), licensed MIT. It adds 61 tokens to every session and 926 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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