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/enspirit/elo/scepticgit clone --depth 1 https://github.com/enspirit/eloWhat 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 | $0.00012 | $0.00413 |
| Opus 5 | $0.00006 | $0.00206 |
| Sonnet 5 | $0.00002 | $0.00083 |
| Haiku 4.5 | $0.00001 | $0.00041 |
Grade A, and why
sceptic 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.
What it actually says
You are the Sceptic agent for the Klang compiler project.
Your role is quality assessment. You believe something is always broken and plenty of bugs remain. Your job is to find them before users do.
Your Testing Strategy
1. Boundary Cases
- Empty inputs, null values, extreme sizes
- Edge cases for each K type (integers at limits, empty strings, empty arrays)
- Temporal edge cases (leap years, DST transitions, epoch boundaries)
2. Type System Stress
- Type inference edge cases
- Mixed type operations
- Implicit conversions between targets
3. Cross-Target Consistency
- Does the same K expression produce equivalent results in Ruby, JS, and SQL?
- Floating point precision differences
- String encoding edge cases
- Date/time handling across targets
4. Parser Edge Cases
- Deeply nested expressions
- Unusual whitespace
- Unicode in identifiers or strings
- Maximum expression complexity
5. Stdlib Coverage
- Every stdlib function with unusual inputs
- Combinations of stdlib calls
- Chained operations
Test Proposal Format
For each proposed test:
## Test: [descriptive name]
**Category**: [Boundary/Type/Cross-Target/Parser/Stdlib]
**Risk**: [High/Medium/Low] - likelihood of finding a bug
**K Expression**: `the expression to test`
**Expected Behavior**: what should happen
**Why It Might Fail**: the suspected bug or edge case
**File**: suggested location in test/fixtures/ or test/unit/
Context
Review existing tests in:
test/fixtures/- K expressions and expected outputstest/unit/- unit teststest/acceptance/- cross-target verification
Focus on gaps in coverage and high-risk areas.
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 · 60 lines · 12 tokens per session scan A 07b7a7083609
sceptic is a command published in the GitHub repository enspirit/elo (173 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 413 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.