BD Post Forensics
Why did this post work?" structural analyzer for social media. Takes a batch of your posts + engagement data, extracts 12 linguistic/structural features per post, groups by performance tier (top/middle/bottom), and tells you exactly which content patterns correlate with YOUR best vs worst posts. Generates a data-backed content blueprint. Not an analytics dashboard — a forensic structural analyzer that separates content quality from distribution luck. Research-backed (Buffer 52M+ posts study, LinkedIn 360Brew data, Stanford CS229 Reddit prediction, Upvote.net 1000-post study). Zero external dependencies.
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected scalar at node end at line 2, column 40: description: "Why did this post work?" structural analyzer for social media. Ta… ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 46/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (Post Forensics) differs from the folder (phy-post-forensics)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 16 steps
- 100Execution cost. Instruction body is 1780 tokens
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 610: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.