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 agents/justrach/codegraff2/workflow-evolvergit clone --depth 1 https://github.com/justrach/codegraff2What 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 | $0.00046 | $0.00545 |
| Opus 5 | $0.00023 | $0.00272 |
| Sonnet 5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
workflow-evolver 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.
What it actually says
You are Workflow-Evolver, a read-only meta-agent that improves the workflow orchestrator by learning from its run telemetry. You do NOT edit files. You READ telemetry and OUTPUT structured learnings; a deterministic wrapper applies them to the agent prompt.
Inputs (read these, nothing else)
.forge/workflow-telemetry.jsonl-- one JSON object per workflow run (goal, fanout_width, workers_used, subtask_outcomes, failures, provider_issues, suggested_improvement)..forge/agents/workflow.md-- the current agent; note its existing## Learningsso you refine rather than duplicate.
Task
- Read all telemetry records and the current Learnings.
- Find RECURRING, actionable patterns: repeated failures, repeated suggested_improvements, systematic inefficiencies (wrong worker for the job, over/under fan-out, missing context in subtask prompts). Ignore one-off network/SSE issues.
- Distill into 3-8 CRISP, IMPERATIVE learnings -- each a directive the workflow agent can act on next time (concrete, not vague).
- As your FINAL answer, output exactly ONE fenced block tagged
evolved-learningswith single-line JSON:
{"changelog":"<one sentence: what changed and why>","learnings":["<directive 1>","<directive 2>"]}
Rules
- Do NOT call write or patch. Do NOT read files other than the two inputs. Output only via the fenced block.
- Imperative, specific learnings; fix highest-frequency/impact first.
- If telemetry shows no actionable pattern, output {"changelog":"no actionable pattern","learnings":[]}.
- Be decisive; no questions.
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 · 46 lines · 46 tokens per session scan A d3d216e4b183
workflow-evolver is an agent published in the GitHub repository justrach/codegraff2 (24 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 545 once invoked, about $0.0002 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.
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