Install
$ agentstack add skill-natan-mohart-24-strategy-skills-for-claude-value-realization ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Value Realization
When to use
Use 6-18 months after a business case, full-potential diagnostic, or synergy case was approved, to check whether it actually delivered. Every skill in this pack that produces a projection (business-case-builder, full-potential-diagnostic, synergy-case-builder) has no accountability loop without this step — a business case with no eventual value-realization check tends to get more optimistic over time, since no one ever pays for being wrong.
What it does
Compares the original projection's driver-level numbers against actuals at the same granularity, attributes material variances to a specific cause (assumption error, execution gap, or external shock), and states plainly whether the initiative is on track but delayed, or genuinely off track — then feeds the finding back into how future cases in this organization get built.
Method
- Pull the original projection at driver level, not just the headline number — the same granularity business-case-builder, full-potential-diagnostic, or synergy-case-builder produced.
- Gather actuals at the same driver-level granularity and time periods, so the comparison is apples to apples.
- Compute variance per driver, not just the bottom line — offsetting errors can make a headline number look fine while hiding that every underlying driver missed in a different direction.
- Attribute each material variance to one cause: assumption error (the input was wrong from the start — check against any assumption-audit that exists), execution gap (the assumption was reasonable, delivery fell short), or external shock (outside anyone's reasonable forecast).
- State the verdict plainly: on track but behind schedule (needs a revised timeline, not a new decision), or genuinely off track (needs a real decision about continuing, adjusting, or stopping).
- Feed the recurring cause back into practice. If assumption error keeps showing up across past reviews, that's a signal the assumption-audit step is being skipped or done too lightly upstream — name it as a process fix.
Inputs
- The original projection (business case, full-potential diagnostic, or synergy case) at driver-level detail
- Actual results at the same granularity and time periods
- Any assumption-audit findings from the original case, if available
Output format
Driver-by-driver projected-vs-actual comparison with variance; cause attribution per material variance; an explicit on-track-but-delayed vs. off-track verdict; one process-level learning fed back into future case-building.
Example
An initiative projected at $3M NPV within 18 months is reviewed at month 12: revenue tracks 15% below plan (an overly optimistic conversion-rate assumption, an assumption error) while costs run 10% under plan (an execution win). The blended NPV still looks roughly on track, but the review flags the revenue-driver miss as the real signal worth revisiting, rather than declaring the case validated because the bottom line happens to still look fine.
Common pitfalls
- Checking only the bottom-line number, missing that offsetting driver errors can accidentally produce a healthy-looking total.
- Treating every miss as an execution failure without checking whether the original assumption was ever realistic.
- Skipping the review once a case has served its purpose of getting funding approved, breaking the accountability loop for every future case.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Natan-Mohart
- Source: Natan-Mohart/24-strategy-skills-for-claude
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.