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/iLearn-Lab/EvoHarnessWrote 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/ilearn-lab/evoharness/session-recap)<a href="https://agentmods.dev/commands/ilearn-lab/evoharness/session-recap"><img src="https://agentmods.dev/badge/commands/ilearn-lab/evoharness/session-recap/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/ilearn-lab/evoharness/session-recap"><img src="https://agentmods.dev/badge/commands/ilearn-lab/evoharness/session-recap.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 | $0.00012 | $0.00129 |
| Opus 5.5 | $0.00005 | $0.00052 |
| Sonnet 5.5 | $0.00002 | $0.00026 |
| Haiku 4.5 | $0.00001 | $0.00013 |
Grade A, and why
session-recap 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 1mo 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 1mo ago First seen · 15 lines · 12 tokens per session scan A e2f4d10965fc
session-recap is a command published in the GitHub repository iLearn-Lab/EvoHarness (58 stars, last pushed 3mo ago), with no licence file. It adds 12 tokens to every session and 129 once invoked, about $0.0000 per session on Opus 5.5. 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
sql_schema
Command "sql_schema" from agentis-tools/ctx, covering ctx sql — public schema (v1), stability contract, views, v1.symbols — one row per symbol and v1.edges — one row per relationship.
clean
Clean up old Answer me with HTML pages, videos and the narration cache.
opsx-apply-resume
Save and resume a long-running OpenSpec apply session from a live worktree, preserving optional gates and host-specific handoff artifacts. Not for: starting a new proposal, changing the external OpenSpec workflow, or treating a commit as a prerequisite. Output: a saved/resumed handoff report with lifecycle and…
accessibility-setup
Configura las preferencias de accesibilidad de Savia para adaptarse a tus necesidades.
context-compress
Compresión semántica de contexto — mantener significado, reducir tokens (80% reduction).
context-defer
Sistema de carga diferida — cargar comandos/reglas solo cuando se necesitan (85% reducción de overhead).