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 chrismbryant/claude-journal-mcp --skill context-recoverygit clone --depth 1 https://github.com/chrismbryant/claude-journal-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/chrismbryant/claude-journal-mcp/context-recovery)<a href="https://agentmods.dev/skills/chrismbryant/claude-journal-mcp/context-recovery"><img src="https://agentmods.dev/badge/skills/chrismbryant/claude-journal-mcp/context-recovery/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/chrismbryant/claude-journal-mcp/context-recovery"><img src="https://agentmods.dev/badge/skills/chrismbryant/claude-journal-mcp/context-recovery.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 | $0.00018 | $0.00949 |
| Opus 5 | $0.00009 | $0.00475 |
| Sonnet 5 | $0.00004 | $0.00190 |
| Haiku 4.5 | $0.00002 | $0.00095 |
Grade A, and why
context-recovery 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 12d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Recovery Skill
You have the ability to recover working context from the journal to restore continuity after context loss.
When to Use This Skill
Use this skill automatically and proactively when:
-
After
/clearcommand- User cleared conversation history
- Need to restore what they were working on
- Should happen immediately without asking
-
Session start questions
- "What was I working on?"
- "Where did we leave off?"
- "What did I do yesterday?"
- "Catch me up"
-
Context seems lost
- User references past work you don't have context for
- Conversation appears to resume mid-task
- User seems to expect you to remember something
-
Beginning of new work
- User wants to continue previous project
- Need to understand recent changes
- Planning next steps based on past work
How to Recover Context
Step 1: Fetch recent entries
journal_list_recent(
limit=10 # Adjust based on need
)
Step 2: Analyze and summarize
- Identify the most recent project
- Determine what was accomplished
- Note any in-progress work
- Highlight blockers or open questions
Step 3: Present concisely Show the user:
- What they were working on (project/feature)
- What was completed recently
- What might be next
- Key context or decisions
Presentation Format
**Recent Work Context**
You were working on [project name], focusing on [main task].
Recent accomplishments:
- [Most recent significant work]
- [Previous work]
- [Earlier work]
Last activity: [What happened most recently]
Ready to continue? [Natural follow-up question]
Examples
Example 1: After /clear
[User runs /clear command]
→ journal_list_recent(limit=10)
→ "You were working on my-api, implementing OAuth2 authentication.
Last thing was configuring JWT token expiration.
Ready to continue with the auth flow?"
Example 2: Session start
User: "What was I doing yesterday?"
→ journal_time_query(time_expression="yesterday")
→ "Yesterday you worked on:
- Fixed cache memory leak in api-service
- Started implementing Redis integration
- Reviewed and merged 2 PRs
Want to continue with the Redis work?"
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.
- 12d ago First seen · 162 lines · 18 tokens per session scan A 74e887d6232c
context-recovery is a skill published in the GitHub repository chrismbryant/claude-journal-mcp (6 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 949 once invoked, about $0.0001 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 2026-08-31.
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