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 jason21wc/ai-governance-mcp --skill journalgit clone --depth 1 https://github.com/jason21wc/ai-governance-mcpWrote 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/jason21wc/ai-governance-mcp/journal)<a href="https://agentmods.dev/skills/jason21wc/ai-governance-mcp/journal"><img src="https://agentmods.dev/badge/skills/jason21wc/ai-governance-mcp/journal/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/jason21wc/ai-governance-mcp/journal"><img src="https://agentmods.dev/badge/skills/jason21wc/ai-governance-mcp/journal.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 | $0.00093 | $0.00866 |
| Opus 5.5 | $0.00037 | $0.00346 |
| Sonnet 5.5 | $0.00019 | $0.00173 |
| Haiku 4.5 | $0.00009 | $0.00087 |
Grade A, and why
journal 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 2mo 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/journal — Session Memory Capture
Analyzes the current session transcript for decisions, constraints, lessons, and context that have not been persisted to memory files. Returns structured proposals categorized by target file. Does NOT write directly — you review and apply.
Runtime Context
After the skill loads, resolve the active memory layout and current transcript with ordinary read-only calls. Match transcripts by their recorded working directory, and state when the host exposes no readable transcript rather than guessing.
Instructions
Quick Start
-
Discover the current session's transcript path using the Runtime Context above. If that shows nothing, list
~/.claude/projects/directories and check which.jsonlfiles have recent modification times matching this session. -
Spawn a background Agent with these parameters:
model: "sonnet"(extraction task — Sonnet is appropriate)run_in_background: true(don't block the user's work)- Prompt: include the transcript path and current memory file contents summary
-
The Agent's task (include in its prompt):
Read the transcript JSONL file at [path], focusing on the last 500 lines. Read the current contents of SESSION-STATE.md, PROJECT-MEMORY.md, LEARNING-LOG.md, BACKLOG.md, OPERATIONS.md, and ARCHITECTURE.md.
Identify items from the conversation that are NOT yet captured in any memory file:
- Decisions (with rationale) → propose for PROJECT-MEMORY.md
- Lessons learned (actionable rules from mistakes or discoveries) → propose for LEARNING-LOG.md
- Position/state changes (current task, blockers, next actions) → propose for SESSION-STATE.md
- Deferred work items → propose for BACKLOG.md
- Operational commitments (recurring reviews, tripwires) → propose for OPERATIONS.md
- Architecture changes (system design, data flow) → propose for ARCHITECTURE.md
- Reusable non-obvious patterns (solved integrations, gotcha+fix, library-selection calls, working implementations of non-obvious techniques) → propose for the Reference Library ("REFERENCE_LIBRARY" target, with domain + suggested title). High bar per curation governance §15.4: non-obvious + reusable, not routine trivia.
For each proposal, provide:
- Target file
- Proposed content (ready to append/insert)
- Why it should be persisted (what would be lost without it)
Return ONLY structured proposals. Do NOT write to any files or call capture_reference.
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.
- 2mo ago First seen · 76 lines · 93 tokens per session scan A ec76d7b899d8
journal is a skill published in the GitHub repository jason21wc/ai-governance-mcp (0 stars, last pushed 25d ago), licensed Apache-2.0. It adds 93 tokens to every session and 866 once invoked, about $0.0004 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 2026-08-31.
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