Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add jamditis/claude-skills-journalism/plugin install autocontextWrote 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/evolve)<a href="https://agentmods.dev/commands/jamditis/claude-skills-journalism/evolve"><img src="https://agentmods.dev/badge/commands/jamditis/claude-skills-journalism/evolve.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.00841 |
| Opus 5 | $0.00005 | $0.00420 |
| Sonnet 5 | $0.00002 | $0.00168 |
| Haiku 4.5 | $0.00001 | $0.00084 |
Grade A, and why
evolve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This command improves skill .md files based on lessons accumulated in the global skill lesson store.
Argument handling
The user may invoke this command with arguments:
/autocontext:evolve, default: scan and evolve interactively/autocontext:evolve --rollback <skill-name>, restore from backup/autocontext:evolve --export, export lessons to JSON/autocontext:evolve --import <path>, import lessons from JSON
Parse the arguments from the user's input. If --rollback is present, run the rollback flow. If --export or --import, run the sync flow. Otherwise, run the default evolution flow.
Default evolution flow
-
Run the scan script to find eligible skills:
bash "${CLAUDE_PLUGIN_ROOT}/scripts/skill-evolution/scan_eligible.sh" -
If no skills are eligible, report that and exit.
-
Present the summary to the user using AskUserQuestion: which skill(s) to evolve? Include lesson count and average confidence per skill. Add an "All" option and a "Skip" option.
-
For each selected skill: a. Read the current skill .md file using the
find_skill_pathfunction:python3 -c " import sys sys.path.insert(0, '${CLAUDE_PLUGIN_ROOT}/scripts/skill-evolution') from apply_edit import find_skill_path path = find_skill_path('SKILL_NAME') print(path or 'NOT_FOUND') "b. If NOT_FOUND, warn and skip. c. Read the skill file content. d. Run generate_diff to create an improved version:
python3 -c " import sys, json sys.path.insert(0, '${CLAUDE_PLUGIN_ROOT}/scripts/skill-evolution') from generate_diff import generate_evolved_skill from store import get_eligible_lessons lessons = get_eligible_lessons('SKILL_NAME') with open('SKILL_PATH') as f: content = f.read() result = generate_evolved_skill(content, lessons) if result: print(result) else: print('GENERATION_FAILED') "e. If generation failed, offer the append fallback. f. Show a diff between the original and evolved content. g. Ask the user via AskUserQuestion:
- Accept, apply the edit
- Edit, let user make manual changes first
- Reject, skip, lessons stay
- Append instead, use the fallback section h. Apply the chosen action using apply_edit functions. i. Mark evolved lessons as folded.
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.
- 8d ago First seen · 112 lines · 11 tokens per session scan A d20f9b32f3a7
evolve is a command published in the GitHub repository jamditis/claude-skills-journalism (387 stars, last pushed 3d ago), licensed MIT. It adds 11 tokens to every session and 841 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 2026-08-30.
Other commands, from other repositories
export-latex
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review-paper
Simulate a double-blind peer review of the current paper. Generates 2 independent reviewer reports in the style of top IS/CS conferences (ICIS, ECIS, MISQ level). Each report includes Summary, Strengths, Weaknesses, Minor Comments, and Overall Recommendation. Output is saved as simulatedreviews.md and can be fed…
triage-project
Strategic project triage — should you continue, pivot, or kill your current research project? Evaluates 5 signals (results, competition, impact, effort, motivation) and delivers a clear Continue/Pivot/Kill recommendation. For Pivot: suggests concrete new framing. For Kill: suggests what to salvage.
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.
scooping-check
Scooping risk assessment — is someone else working on your research idea? Identifies competing groups, assesses pace of publication, evaluates whether the core insight is "in the air", and provides a watch list with search terms, key researchers, and venues to monitor.
audit-paper
Audit a paper's empirical claims against the linked code repository — catches MISMATCH, MISSING, and PARTIAL claims before submission.