oss-forensics-orchestration

Orchestrates multi-agent forensic investigations on public GitHub repositories, coordinating parallel evidence collection, hypothesis formation, verification, and report generation.

Quality

73%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/oss-forensics/orchestration/SKILL.md

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body delivers a well-sequenced, validated multi-agent workflow with concrete Task prompts, but it is monolithic — inlining agent prompt templates and referencing scripts absent from the bundle — and carries minor duplicate guidance.

Suggestions

Extract the per-agent Task prompt templates into a reference file (e.g. references/agent-prompts.md) and link to it from each phase to improve progressive disclosure.

Remove the 'Critical Rules' section or trim it to only non-obvious rules not already stated in the Workflow phases to reduce duplication.

Ship the referenced scripts (init_investigation.py, raptor-may-ask) in a scripts/ bundle directory so the cited paths resolve, or note their provenance explicitly.

Dimension	Reasoning	Score
Conciseness	The body is reasonably lean for a 7-phase orchestration, but the 'Critical Rules' section largely restates guidance already embedded in the workflow and the example execution re-walks every phase, which are minor instances of over-explanation that could be trimmed.	4 / 5
Actionability	Provides concrete, specific Task prompts with real agent names, working-directory and target parameters, plus an executable init command; the hypothesis/validation loops remain templated pseudocode with placeholders, leaving minor gaps versus fully copy-paste-ready guidance.	4 / 5
Workflow Clarity	Phases 0–7 are explicitly sequenced with validation checkpoints (prerequisite check, verifier phase, hypothesis checker accept/reject), feedback loops (rebuttal → revise → re-check), and explicit error-handling rules, satisfying the score-5 anchor for a batch operation.	5 / 5
Progressive Disclosure	Section structure is clear, but the entire orchestration including per-agent prompt templates is inlined in SKILL.md with no bundle reference files present, and the referenced script paths (init_investigation.py, raptor-may-ask) do not exist in the bundle, so content that could be split out remains inline.	3 / 5
	Total	16 / 20 Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description conveys a clear, distinctive purpose with concrete coordinated actions and natural trigger terms, but it omits any explicit 'when to use' guidance, which caps its completeness.

Suggestions

Add an explicit 'Use when...' clause naming the triggering situations, e.g. 'Use when investigating suspected malicious activity in a public GitHub repository.'

Include a couple of natural synonyms users might say ('security incident', 'supply-chain attack', 'suspicious commits') to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	Names the domain ('forensic investigations on public GitHub repositories') and several specific coordinated actions — 'parallel evidence collection, hypothesis formation, verification, and report generation' — but the actions read more as workflow phases than discrete concrete verbs, leaving minor coverage gaps relative to the score-5 anchor.	4 / 5
Completeness	Clearly states what the skill does but provides no 'Use when...' clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3.	3 / 5
Trigger Term Quality	Good keyword coverage with natural terms a user would say ('forensic investigations', 'GitHub repositories', 'evidence collection', 'report generation'); a few natural synonyms are missing (e.g. 'security', 'incident', 'supply chain').	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche ('multi-agent forensic investigations on public GitHub repositories') with distinct triggers and minimal realistic overlap with other skills.	5 / 5
	Total	16 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: gadievron/raptor
Path: .claude/skills/oss-forensics/orchestration/SKILL.md
Commit: a5deef3

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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