AC honcho-memory
Built by Axobotl (@Inner_Axiom). Production-grade Honcho memory system battle-tested on a 6+ agent fleet with 1000+ messages. Replaces embedding-based retrieval with reasoned, evolving understanding of users and agents across sessions. Includes automated feed pipelines, on-demand querying with reasoning levels, token-budgeted context generation that survives compaction, cross-agent memory sharing, and cron automation. Use when you need agents that actually remember, understand context across sessions, or when cron/isolated sessions need full continuity.
Built by Axobotl (@InnerAxiom).
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice
How to improve
- 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: 7. 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 56/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 27 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2671 tokens
- low 13 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
- +1No license
- +2Single-language instructions
- +3Description length 559: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.