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 agentmods add commands/samibs/skillfoundry/evolvegit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/samibs/skillfoundry/evolve)<a href="https://agentmods.dev/commands/samibs/skillfoundry/evolve"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/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 | $0.00000 | $0.00716 |
| Opus 5 | $0.00000 | $0.00358 |
| Sonnet 5 | $0.00000 | $0.00143 |
| Haiku 4.5 | $0.00000 | $0.00072 |
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 yesterday.
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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/evolve - Framework Evolution from Dev-Memory
Bridge learned lessons from the dev-memory knowledge repository into claude_as framework agents.
Usage
/evolve Pull lessons and evolve agents
/evolve --dry-run Show what would change
/evolve --commit Evolve and auto-commit
/evolve --status Show evolution statistics
Instructions
You are the Evolution Engine — the bridge between accumulated project knowledge and framework intelligence.
When invoked:
Step 1: Locate dev-memory
- Check
.claude/knowledge-sync.confforDEV_MEMORY_DIR - Look in common locations (
~/dev-memory,~/projects/dev-memory) - If not found, guide user to clone it
Step 2: Run evolve.sh Execute the evolution script:
bash scripts/evolve.sh $ARGUMENTS
Step 3: Report results Show what was imported:
- Lessons learned count
- Anti-patterns imported
- Tech stack preferences
- Rules injected into
_quality-primer.md
Step 3.5: Promote domain reviewers (FR-007) Surface domain reviewers that have been synthesized across enough projects to graduate from project-local to framework-shared:
bash scripts/promote-experts.sh scan
For each candidate (a reviewer synthesized in ≥ 3 distinct registered projects, not yet framework-shared), propose promotion; on confirmation:
bash scripts/promote-experts.sh promote --domain <slug>
This copies the review-only reviewer into agents/<slug>-expert.md (scope=framework) and
scaffolds a framework pack, so every future project inherits it. See
agents/_domain-gap-protocol.md.
Step 4: Propagation guidance After evolution:
To propagate to all projects:
./update.sh <project-dir>
To propagate to all registered projects:
./update.sh --scan
What evolve.sh does:
- Reads
dev-memory/global/lessons.jsonl— patterns promoted from 3+ occurrences - Reads
dev-memory/global/anti-patterns.jsonl— documented failures - Reads
dev-memory/global/tech-stack.jsonl— technology preferences - Reads
dev-memory/global/preferences.jsonl— developer preferences - Generates
agents/_learned-rules.md— full rule document for all agents - Injects top rules into
agents/_quality-primer.md"Learned Rules" section - Optionally commits to claude_as repository
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.
- yesterday First seen · 109 lines · 0 tokens per session scan A 5f4e9ca4bc1a
evolve is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 716 tokens. 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.
Other commands, from other repositories
generate-project-context
Create project-context.md with AI rules. Use when the user says ""generate project context"" or ""create project context"".
checkpoint
Checkpoint — Persistent Memory Archival.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.