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.
git clone --depth 1 https://github.com/jamditis/claude-skills-journalismWrote 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/commands/jamditis/claude-skills-journalism/status)<a href="https://agentmods.dev/commands/jamditis/claude-skills-journalism/status"><img src="https://agentmods.dev/badge/commands/jamditis/claude-skills-journalism/status/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/commands/jamditis/claude-skills-journalism/status"><img src="https://agentmods.dev/badge/commands/jamditis/claude-skills-journalism/status.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 | $0.00007 | $0.01148 |
| Opus 5 | $0.00003 | $0.00574 |
| Sonnet 5 | $0.00001 | $0.00230 |
| Haiku 4.5 | $0.00001 | $0.00115 |
Grade A, and why
status 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 10d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Display stats and status for the autocontext in the current project.
Precondition
Before displaying stats, the script checks if .autocontext/ exists. If it doesn't, you'll see:
Autocontext not initialized for this project.
Run '/autocontext:init' to set up knowledge tracking.
Otherwise, the script reads:
.autocontext/lessons.json, active and archived lessons.autocontext/config.json, project configuration.autocontext/cache/, any pending lessons
Status report
The script displays a clean, organized report with the following sections:
1. Lessons by category
Breakdown of all lessons (active and tombstoned) by category:
Lessons by category:
bug-fix: 12 active, 3 tombstoned
architecture: 8 active, 1 tombstoned
workflow: 15 active, 2 tombstoned
pattern: 6 active, 0 tombstoned
gotcha: 9 active, 1 tombstoned
integration: 4 active, 0 tombstoned
test-strategy: 5 active, 0 tombstoned
Total: 59 active lessons | 7 tombstoned
2. Confidence metrics
Average confidence across all active lessons:
Confidence metrics:
Average confidence: 0.72
High confidence (>= 0.8): 28 lessons
Medium confidence (0.5-0.8): 26 lessons
Low confidence (< 0.5): 5 lessons
3. Most-validated lessons
Top 5 lessons with the highest validated_count (most confirmed across sessions):
Most-validated lessons (by developer/session confirmations):
1. [Architecture] "Split domain logic from infrastructure", validated 34 times
2. [Workflow] "Always run tests before commit", validated 28 times
3. [Bug-fix] "Regex patterns need raw strings", validated 22 times
4. [Pattern] "Use dependency injection for testing", validated 18 times
5. [Test-strategy] "Write integration tests for API changes", validated 16 times
4. Stalest lessons
Top 5 lessons with lowest confidence or oldest last_validated timestamp (need attention):
Stalest lessons (lowest confidence or oldest validation):
1. [Workflow] "Use Docker for local dev", confidence 0.3, last validated 120 days ago
2. [Integration] "Slack API batch operations", confidence 0.4, last validated 89 days ago
3. [Gotcha] "Python timezone handling edge case", confidence 0.5, last validated 73 days ago
4. [Architecture] "Consider microservices for X", confidence 0.4, last validated 95 days ago
5. [Pattern] "SQL query optimization with indexes", confidence 0.5, last validated 67 days ago
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.
- 10d ago First seen · 158 lines · 7 tokens per session scan A a0c8f333eb50
status is a command published in the GitHub repository jamditis/claude-skills-journalism (389 stars, last pushed 2d ago), licensed MIT. It adds 7 tokens to every session and 1,148 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.
reflect
Capture learnings from the current conversation through guided reflection.
claude-recall
Update project context using the local LLM (Qwen2.5 0.5B).
continuity
Read and update working memory — maintain context across sessions via CONTINUITY.md.
dossier
Force load of Dossier vault profile (and inbox check) before processing the rest of the user's request.
analyze-writing
Analyze academic writing quality of the current draft. Checks for passive voice overuse, weak openings, hedging balance, readability, sentence length variation, and word repetitions. Produces a writing quality report with specific improvement suggestions per section.