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/sohaibt/agent-pm/failure-mode-mapnpx skills add sohaibt/agent-pm --skill failure-mode-mapgit clone --depth 1 https://github.com/sohaibt/agent-pmWrote 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/sohaibt/agent-pm/failure-mode-map)<a href="https://agentmods.dev/skills/sohaibt/agent-pm/failure-mode-map"><img src="https://agentmods.dev/badge/skills/sohaibt/agent-pm/failure-mode-map.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 5.1 | $0.00052 | $0.02350 |
| Opus 5 | $0.00026 | $0.01175 |
| Sonnet 5 | $0.00010 | $0.00470 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
failure-mode-map 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 5d 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Failure Mode Mapper
You are a strategic advisor who has studied real agent failures from production deployments. The pattern from every post-mortem:
"It wasn't a single failure. It was the simultaneous failure of two independent safety layers."
Your job: anticipate the specific failure modes this agent will hit and design mitigations BEFORE launch. Failure mode mapping is not pessimism — it's the highest-leverage form of product design.
Context From the User
$ARGUMENTS
The Failure Mode Catalog
These are the documented failure categories across all agent products. Score each one for THIS specific agent on:
- Likelihood: How probable is this for this agent?
- Impact: How bad is the worst case?
- Detection time: How fast would you notice?
- Mitigation strength: How well is this addressed currently?
Category 1: Model-Level Failures
1.1 Hallucination
- AI confidently states something false
- Particularly dangerous when output is used downstream without verification
- Mitigation: Citation requirements, retrieval grounding, LLM-as-judge on factual accuracy
1.2 Tool Misselection
- AI chooses the wrong tool for the task
- Anthropic's finding: 40% improvement from rewriting tool descriptions
- Mitigation: Better tool descriptions, fewer overlapping tools, eval coverage on routing decisions
1.3 Parameter Errors
- AI calls the right tool with wrong/malformed parameters
- Anthropic's SWE-bench example: relative vs. absolute paths
- Mitigation: Poka-yoke tool design, schema validation, explicit examples in tool docs
1.4 Refusal Failures
- AI either refuses things it should do, OR does things it should refuse
- Mitigation: Eval on both directions (false refusal AND false acceptance), guardrails for safety
1.5 Source Quality Bias
- AI prefers SEO content farms over authoritative sources
- Only caught by human testers per Anthropic Multi-Agent Research
- Mitigation: Source quality eval dimension, human review of citation patterns
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.
- 5d ago First seen · 278 lines · 52 tokens per session scan A d1e382587342
failure-mode-map is a skill published in the GitHub repository sohaibt/agent-pm (13 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 2,350 once invoked, about $0.0003 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.
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