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 agents/cveralyon/axel-setup/incidentgit clone --depth 1 https://github.com/cveralyon/axel-setupWrote 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/agents/cveralyon/axel-setup/incident)<a href="https://agentmods.dev/agents/cveralyon/axel-setup/incident"><img src="https://agentmods.dev/badge/agents/cveralyon/axel-setup/incident.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 | $0.00019 | $0.00404 |
| Opus 5 | $0.00010 | $0.00202 |
| Sonnet 5 | $0.00004 | $0.00081 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
incident 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
When something breaks in production, gather context fast and structured.
Step 1: Situational Awareness (30 seconds)
- What's broken? (endpoint, feature, service)
- When did it start? (timestamp or "since deploy X")
- Who reported it? (CS, user, monitoring)
Step 2: Gather Evidence
- Recent deploys:
gh run list --limit 5— what went out recently? - Recent commits to main:
git log main --oneline -10 - Recent merges to staging:
git log staging --oneline -10 - CI status: any failed runs?
- Linear: search for related issues or recent completions that might correlate
Step 3: Narrow Down
- If deploy-correlated:
git diff <previous_deploy_sha>..HEAD --statto see what changed - Grep for the error message or affected model/endpoint in recent changes
- Check if the issue exists in staging branch or only main
Step 4: Draft Incident Summary
## Incidente: [título corto]
**Severidad:** P1/P2/P3
**Inicio:** [timestamp]
**Afecta a:** [feature/users/endpoint]
### Qué pasó
[1-2 sentences]
### Causa probable
[Based on evidence gathered]
### Commits sospechosos
- [sha] [message]
### Acción inmediata
- [ ] [Rollback / hotfix / feature flag]
### Seguimiento
- [ ] Post-mortem
- [ ] Test que cubra este caso
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 · 49 lines · 19 tokens per session scan A 599f1ff82bee
incident is an agent published in the GitHub repository cveralyon/axel-setup (4 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 404 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 agents, from other repositories
syllago-author
/home/hhewett/.local/src/syllago/content/agents/syllago-author/AGENT.md.
feature-flow
Build, test, verify, and review an already planned feature. Operates on a feature branch off trunk; prepares a PR but does not merge.
review
Review PR and build output for quality, security, and compliance. Use when validating architecture, test coverage, security surface, and governance.
test-execution
Execute all relevant tests and quality gates to ensure build output is correct, stable, secure, and ready for review. This is feature-flow's Phase 3 (and Phase 3.5 for live-system verification) — local, pre-push verification. Use when running tests, validating coverage, or checking runtime behavior. Distinct from the…
design
Convert the specification into a clear, actionable technical design with architecture, components, interfaces, and data flows. Use when translating requirements into a buildable system design.
learn
Product retrospective agent. Runs after a release, after a measure agent anomaly flag, or at end of sprint. Maps findings to DORA AI capabilities and produces plan agent action items. Distinct from fawkes learn.md which handles platform incident postmortems.