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 nthnclrk/enablement-skills --skill win-loss-synthesisgit clone --depth 1 https://github.com/nthnclrk/enablement-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/nthnclrk/enablement-skills/win-loss-synthesis)<a href="https://agentmods.dev/skills/nthnclrk/enablement-skills/win-loss-synthesis"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/win-loss-synthesis/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/nthnclrk/enablement-skills/win-loss-synthesis"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/win-loss-synthesis.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 | $0.00064 | $0.00894 |
| Opus 5 | $0.00032 | $0.00447 |
| Sonnet 5 | $0.00013 | $0.00179 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
win-loss-synthesis 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 11d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win-loss synthesis
Turn mixed win-loss evidence into findings a leader can defend. Separate what the source said from what you think it means. Do not recommend until the sample shape is on the page.
Fails. "Price is why we lose. 67% of interviews mentioned it."
Passes. "Price was the primary coded reason in 8 of 12 interviewed losses (12 of 41 eligible losses participated). Direct buyer evidence in 5. Counterexamples: two losses where price was accepted and security stalled the deal. This is the interviewed sample, not the closed-lost population."
For a single competitive asset, use battlecard-builder. For recurring objections across live calls, use call-insights-to-objections.
Confirm Inputs First
Ask only for the inputs that change the findings:
- Time window, segment, and the eligible closed-deal population
- Sources on hand: interviews, CRM notes, call snippets, closed-lost reasons
- Selection method and the win / loss / no-decision mix
- Confidentiality, consent, and who will see quotes
If sample accounting is incomplete, proceed with labeled limits. Do not imply the sample represents the eligible population. Do not invent win rates.
Read The Right Reference
Read references/win-loss-coding-framework.md before you code themes, assign a primary reason, or quantify a finding. Skip it only if the user already coded the set and wants implications from a finished ledger.
Default Workflow
- Draw the boundary. Eligible population, inclusion rules, outcome definitions, and what counts as evidence. Done when a reviewer can see who is in and who is out.
- Build the sample ledger. Eligible, selected, contacted, participated, evidence-available, coded, excluded. By outcome and any material segment. No ledger, no prevalence language.
- Keep evidence and interpretation apart. Observed fact, stakeholder-reported reason, contributing factor, and analyst root-cause hypothesis are four fields. A CRM close reason is one input, not the finding.
- Calibrate, then code.
One primary reason only when the evidence supports a dominant reason; otherwise
mixedorunresolved. Preserve secondary reasons and disagreements between buyer, seller, and system. Version the codebook. - Assign confidence from the slice, not the story. Independent deals, direct buyer evidence, coverage, source mix, counterexamples. High never transfers automatically to another segment or competitor.
- Protect people and accounts. Approved attribution only. Suppress thin cells. No rep-performance verdicts unless that use was approved.
- Recommend only what the sample can carry. Owner-addressable messaging, coaching, process, or product moves. Sequence by evidence, not by how strongly someone feels.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 72 lines · 64 tokens per session scan A eee5f44d2deb
win-loss-synthesis is a skill published in the GitHub repository nthnclrk/enablement-skills (13 stars, last pushed 22d ago), licensed MIT. It adds 64 tokens to every session and 894 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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