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 skills add fzfclee/consulting-skills --skill abductive-reasoninggit clone --depth 1 https://github.com/fzfclee/consulting-skillsWrote 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/fzfclee/consulting-skills/abductive-reasoning)<a href="https://agentmods.dev/skills/fzfclee/consulting-skills/abductive-reasoning"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/abductive-reasoning/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/fzfclee/consulting-skills/abductive-reasoning"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/abductive-reasoning.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00049 | $0.00708 |
| Opus 5 | $0.00024 | $0.00354 |
| Sonnet 5 | $0.00010 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
abductive-reasoning 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 12d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Abductive Reasoning
Use this skill to choose the most plausible explanation when evidence is incomplete.
Method Notes
- Abductive reasoning asks: what explanation would best account for the facts we see?
- The output is a ranked explanation, not proof.
- It is useful when stakeholder behavior, market signals, failures, or events can be explained in several ways.
- A good abductive diagnosis names what each explanation explains, what it fails to explain, and what evidence would change the ranking.
Required Inputs
Collect or infer these inputs before execution:
- surprising fact, event, behavior, or outcome
- known evidence
- candidate explanations
- missing evidence
- decision consequence if the explanation is wrong
If an input is missing, mark it as missing, state the assumption used, and add a validation action.
When Not To Use
Do not use when a formal rule already determines the conclusion. Use deductive-reasoning instead. Do not use when the task is broad pattern discovery; use inductive-reasoning first.
Step-by-Step Execution
| Step | Required input | How to execute | Output |
|---|---|---|---|
| State the puzzle | Surprising fact or outcome. | Define what needs explaining. | Explanation question. |
| List evidence | Facts, signals, timing, observations. | Separate strong facts from interpretations and missing evidence. | Evidence list. |
| Generate explanations | Candidate causes, motives, mechanisms. | Create plausible explanations without settling too early. | Explanation candidates. |
| Score explanatory fit | Evidence and candidates. | Rate how well each explanation accounts for the facts, timing, and contradictions. | Fit table. |
| Check parsimony and risk | Candidates, assumptions, downside. | Prefer explanations that require fewer unsupported assumptions, while noting high-risk alternatives. | Plausibility ranking. |
| Identify discriminating evidence | Top explanations. | Define evidence that would clearly separate one explanation from another. | Validation tests. |
| State working explanation | Ranking and confidence. | Choose the best current explanation and action implication. | Working diagnosis. |
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.
- 12d ago First seen · 77 lines · 49 tokens per session scan A 4e5144b6b2d4
abductive-reasoning is a skill published in the GitHub repository fzfclee/consulting-skills (4 stars, last pushed 23d ago), licensed Apache-2.0. It adds 49 tokens to every session and 708 once invoked, about $0.0002 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.
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