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/iliaal/whetstone/evolve-skillgit clone --depth 1 https://github.com/iliaal/whetstoneWrote 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/iliaal/whetstone/evolve-skill)<a href="https://agentmods.dev/commands/iliaal/whetstone/evolve-skill"><img src="https://agentmods.dev/badge/commands/iliaal/whetstone/evolve-skill.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 | $0.00032 | $0.01996 |
| Opus 5 | $0.00016 | $0.00998 |
| Sonnet 5 | $0.00006 | $0.00399 |
| Haiku 4.5 | $0.00003 | $0.00200 |
Grade A, and why
evolve-skill 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 3d 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.
Evolve a skill via DSPy optimization
Run the complete skill evolution pipeline for a single skill. Harvests fresh session data, discovers new negative patterns, builds a golden eval dataset, scores the baseline, runs DSPy optimization, and presents a before/after comparison for review.
Arguments
SKILL_NAME=$1 (required: e.g., "code-review", "pinescript", "planning")
OPTIMIZER=$2 (optional: "gepa" (default), "mipro", or "bootstrap")
Parse from: $ARGUMENTS
If no skill name provided, ask the user which skill to evolve. Show skills with the most harvested data as suggestions.
Pipeline
Maximize parallelism. Steps within the same group run concurrently (use background subagents or parallel bash). Steps across groups are sequential.
Group A (parallel): Harvest + Discover
Run these two concurrently:
Step 1: Harvest sessions (full, all projects)
python3 distillery/scripts/distiller.py harvest-sessions
Report: total examples harvested, how many attributed to the target skill.
Step 2: Discover new negative signal patterns
python3 distillery/scripts/distiller.py discover-signals --top 20
Present the top candidates to the user. If any look like genuine dissatisfaction patterns (not neutral task requests), ask whether to add them to _NEGATIVE_SIGNAL_PATTERNS in distiller.py before proceeding. If patterns are added, re-run harvest (Step 1) to update signal classifications.
If no new patterns worth adding, continue.
Group B (sequential): Build golden
Depends on Group A completing.
Step 3: Build golden eval dataset
RECOMMENDED (human-label) path — post-2026-07-07 harvest data is mostly ambiguous (no typed user outcome), and a golden set dominated by ambiguous drives GEPA to degenerate results:
python3 distillery/scripts/distiller.py build-golden <skill> --top 20
# → writes candidates.jsonl. Open it, set each "label" to positive / negative / skip
# (drop the ambiguous ones as "skip" unless you can grade them), then:
python3 distillery/scripts/distiller.py approve-golden <skill>
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.
- 3d ago First seen · 162 lines · 32 tokens per session scan A 70f26c526290
evolve-skill is a command published in the GitHub repository iliaal/whetstone (32 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 1,996 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.
Other commands, from other repositories
design-review
Workflow recipe — review a design end-to-end, ending in measured numbers rather than adjectives, by chaining 4 skills.
setup-pm-skills
Onboard a new user — find out what they do, recommend the right bundles & top skills, and set up a project CONTEXT.md so every skill is tailored to them.
statusbar-style
Switch the status-bar style (classic / capsule / hairline).
fest-show
Show festival progression (in-progress tasks, roadmap, and dependency view).
superpowers-execute
Execute the current GSD phase plan with Superpowers instead of gsd-execute-phase.
config
Command "config" from sdebruyn/fabric-dw-mcp-cli, covering configuration & defaults, http retry budget, sql retry budget, mcp workspace allowlist {#mcp-workspace-allowlist} and mcp server log level.