SKILLEMALL.ai

AC pharma-analyst

Produce current, evidence-graded pharmaceutical and biotechnology analysis at the company, platform, pipeline, asset, indication, trial, competitive-landscape, catalyst, risk, and valuation levels. Use for biopharma diligence, investment-research-style reports, pipeline reviews, target or mechanism assessments, clinical-data interpretation, competitor comparisons, deal or financing analysis, catalyst calendars, risk-adjusted valuation, and updates to an existing pharma report or PDF. Supports public and private companies and preclinical through commercial-stage assets. Do not use for patient-specific medical advice.

ClawHub Agent Skills author: SciMiner v1.0.1 MIT-0 4 files body ≈ 2 176 tokens Open the sourceclawhub.ai analyzed 35 h ago

Produce current, evidence-graded pharmaceutical and biotechnology analysis at the company, platform, pipeline, asset, indication, trial…

As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerInfrastructureData and analyticsFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 52 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2176 tokens

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 623: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 52 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill is a disclosed pharma research assistant that guides evidence-graded analysis and does not contain executable code, persistence, credential handling, or hidden data movement.
    LLM: benign (high) · VirusTotal: · 10 Sept 2026