Goose is an open-source AI agent that runs on a computer as a desktop app, command-line tool, or API and can perform tasks beyond code completion. Developers and other users employ it for coding, research, writing, automation, and data analysis with different language-model providers and extensions. The catalogue add-ons define parts of its agent workflow and behavior.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaif-goose/goosenpx agentmods add skills/aaif-goose/goose/compare-tasksWrote 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/aaif-goose/goose/compare-tasks)<a href="https://agentmods.dev/skills/aaif-goose/goose/compare-tasks"><img src="https://agentmods.dev/badge/skills/aaif-goose/goose/compare-tasks/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/skills/aaif-goose/goose/compare-tasks"><img src="https://agentmods.dev/badge/skills/aaif-goose/goose/compare-tasks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00016 | $0.02430 |
| Opus 5.5 | $0.00006 | $0.00972 |
| Sonnet 5.5 | $0.00003 | $0.00486 |
| Haiku 4.5 | $0.00002 | $0.00243 |
Grade A, and why
compare-tasks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- compare_tasks — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compare two harbor runs on one task
Use when given two harbor run names and a task name, and the goal is to understand why the two runs differ on that task — not just that they differ.
Inputs
RUN_A: harbor run name (e.g.sonnet46-full)RUN_B: harbor run name (e.g.pi-sonnet46-full)TASK: bare task name (e.g.extract-elf, notterminal-bench/extract-elf)RUNS_DIR: defaults toevals/harbor/runs/relative to the repo root
Procedure
1. Find each run's trial directory for the task
Harbor 0.8 names trial dirs <task>__<random-suffix> (e.g.
extract-elf__bU3GHs4), not <task>.1. The suffix is unique per trial,
so don't guess it — discover it from disk:
TRIAL_A_DIR=$(ls -d "$RUNS_DIR/$RUN_A/${TASK}__"*/ 2>/dev/null | head -1)
TRIAL_B_DIR=$(ls -d "$RUNS_DIR/$RUN_B/${TASK}__"*/ 2>/dev/null | head -1)
If either is empty, that run didn't include this task — stop and say so.
(ls "$RUNS_DIR/$RUN_A/" shows what's there.)
If you want to confirm the match, every result.json carries task_name
and trial_name:
jq '{task_name, trial_name}' "$TRIAL_A_DIR/result.json"
2. Headline facts
The fastest path is to let cmd.py task do it for you — it already prints
status, reward, duration, tokens, turns, cost, error class, and the tail of
the verifier stdout:
./evals/harbor/cmd.py task "$RUN_A" "$TASK"
./evals/harbor/cmd.py task "$RUN_B" "$TASK"
Only drop to raw jq against result.json if you need a field cmd.py task
doesn't print. The actual shape (harbor 0.8 TrialResult):
jq '{
reward: (.verifier_result.rewards.reward
// (.verifier_result.rewards | to_entries | .[0].value)
// null),
rewards_all: .verifier_result.rewards,
duration_seconds: ((.finished_at | fromdateiso8601) - (.started_at | fromdateiso8601)),
input_tokens: .agent_result.n_input_tokens,
cache_tokens: .agent_result.n_cache_tokens,
output_tokens: .agent_result.n_output_tokens,
cost_usd: .agent_result.cost_usd,
error_type: .exception_info.exception_type,
error_message: (.exception_info.exception_message // "" | split("\n")[0])
}' "$TRIAL_A_DIR/result.json"
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 · 228 lines · 16 tokens per session scan A 6972b34c696f
compare-tasks is a skill published in the GitHub repository aaif-goose/goose (55,322 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 2,430 once invoked, about $0.0001 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.
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