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 skills add AAWWCC/ape --skill historygit clone --depth 1 https://github.com/AAWWCC/apeWrote 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/skills/aawwcc/ape/history)<a href="https://agentmods.dev/skills/aawwcc/ape/history"><img src="https://agentmods.dev/badge/skills/aawwcc/ape/history.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 | $0.00018 | $0.00267 |
| Opus 5 | $0.00009 | $0.00133 |
| Sonnet 5 | $0.00004 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
history 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 5d 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.
What it actually says
APE history
Use only when the user explicitly requests APE history work. Call ape_history with:
On Google Antigravity / Gemini, pass the exact open project root as project_dir.
queryfor a run or requirement ID.explainfor a human-readable rendering of one record.importfor an explicitly requested legacy planning import.maintenance-statusto read the latest retention outcome.compact-artifactsonly after an explicit maintenance request. Pass the user's non-empty auditreason; optionalkeep_recent_runsdefaults to 32 andmax_runsto 64 (maximum 256).
Never describe compaction as deleting immutable history: it verifies an archive before removing
only redundant source artifacts and preserves audit logs, prepared transactions, and active or
sealed runs. Set delete_legacy: true only when the user explicitly asks to delete eligible legacy
machine documents. Report every warning and any bounded/truncated response honestly.
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.
- 5d ago First seen · 25 lines · 18 tokens per session scan A 2668251f233b
history is a skill published in the GitHub repository AAWWCC/ape (0 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 267 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 2026-08-31.
Other skills, from other repositories
agent-orchestrator-v2
Agent Orchestrator workflow skill. Use this skill when the user needs Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management and the operator should preserve the upstream workflow, copied support files, and…
self-improvement
Captures lessons and promotes recurring patterns.
plan-before-code
Plans multi-step work before writing code.
brainstorming
Refines rough ideas into approved designs before code.
code-review
Reviews diffs by severity to produce actionable feedback.
codebase-onboarding
Maps an unfamiliar repo before touching its code.