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/tk-logl/sentinel/checkgit clone --depth 1 https://github.com/tk-logl/sentinelWhat 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 | $0.00497 |
| Opus 5 | $0.00006 | $0.00249 |
| Sonnet 5 | $0.00002 | $0.00099 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
check 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 yesterday.
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
/sentinel:check
Scan the current project for rule violations and generate a compliance report.
What to Do
- Find all source files in the project (py, ts, tsx, js, jsx, go, rs, java, c, cpp)
- Skip: test files, node_modules, pycache, .sentinel/, .claude/, .omc/
- Check each file for:
- Standalone
passwithout @abstractmethod raise NotImplementedErrorwithout @abstractmethod- TODO/FIXME/PLACEHOLDER/HACK comments
- Hardcoded secrets (sk-, ghp_, AKIA, xoxb-)
print()orconsole.log()debug statements- Missing type hints (Python: def without ->)
- Bare
except:or emptycatch {} assert Trueor meaningless assertions- Files over 200 lines without headers
- Standalone
- Check git status for uncommitted changes
- Check for .sentinel/current-task.json — is there an active task?
- Check error log for repeated failures
Output Format
## Sentinel Compliance Report
**Project**: [project name]
**Branch**: [current branch]
**Files scanned**: [count]
**Date**: [timestamp]
### Summary
- CRITICAL violations: [count]
- HIGH violations: [count]
- MEDIUM warnings: [count]
- Files needing headers: [count]
### Violations
#### CRITICAL
- `path/to/file.py:45` — Hardcoded API key (sk-abc...)
- `path/to/views.py:12` — Bare except: pass (silent error swallowing)
#### HIGH
- `path/to/main.py:200` — Missing type hints on 5 functions
- `path/to/utils.js:30` — console.log debug statement
#### MEDIUM
- `path/to/config.py:1` — No file header (350 lines)
### Status
- Active task: [task_id or "none"]
- Uncommitted changes: [count]
- Recent errors: [summary]
### Recommendation
[Actionable next steps based on findings]
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.
- yesterday First seen · 65 lines · 12 tokens per session scan A 7267a08d12b4
check is a command published in the GitHub repository tk-logl/sentinel (4 stars, last pushed 5mo ago), licensed MIT. It adds 12 tokens to every session and 497 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 commands, from other repositories
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.