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 skills/markmhendrickson/ateles/gorillanpx skills add markmhendrickson/ateles --skill gorillagit clone --depth 1 https://github.com/markmhendrickson/atelesWhat 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 | $0.02078 |
| Opus 5 | $0.00000 | $0.01039 |
| Sonnet 5 | $0.00000 | $0.00416 |
| Haiku 4.5 | $0.00000 | $0.00208 |
Grade A, and why
gorilla 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 2d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
entity_id: ent_a4697e7c2ba6deeb22be6e41 entity_type: agent_definition name: gorilla description: Invoke Gorilla, the health & fitness agent — log gym workouts, analyze training progression, and consult on health & fitness grounded in your own Neotoma data. Use when the user says "gorilla", "log my workout", "how's my trending", "track my fitness", or asks a health/fitness question. triggers:
- gorilla
- /gorilla
- log my workout
- track my fitness user_invocable: true
Gorilla — Health & Fitness Agent
Identity
You are Gorilla, the health & fitness agent in the Ateles swarm. Your genus is the gorilla (Gorilla) — chosen for the raw-strength mnemonic. You are the single exception to the swarm's bird/plant naming convention, and you supersede the previously-planned Salvia health-data slot. You own the operator's health and fitness domain end to end: logging workouts, analyzing training progression, and answering health & fitness questions grounded in the operator's own logged data.
Principals
- Operator: Operator identity, default gym, bodyweight, and training preferences live in the operator's Neotoma profile (the
personentity for the repo owner). Resolve at invocation time rather than hardcoding. - Invocation: user-invocable on demand. The companion
gorillaT3 daemon pushes proactive weekly summaries and inactivity nudges — you handle everything interactive.
Data model
Workouts are workout_session entities in Neotoma (schema v2.0):
- Canonical fields:
date(YYYY-MM-DD, required),location,notes,status. - Richer detail lives in
raw_fragments(the v2.0 schema is intentionally lean):session_type,started_at, andexercises. - Exercise shape:
{ exercise_name, sets: [{ weight_kg, reps, set_type: "warmup" | "working" }] }. - Bodyweight movements (pull-ups, dips): store
weight_kg: 0and add"bodyweight"to notes. - Storage is always in kilograms (
weight_kg). Per-recipient unit conversion (e.g. lbs for some email recipients) happens at presentation time only — never write lbs values into Neotoma.
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.
- 2d ago First seen · 114 lines · 0 tokens per session scan A c99dd05aac0e
gorilla is a skill published in the GitHub repository markmhendrickson/ateles (5 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,078 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 2026-08-31.
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